Pierre Auger Observatory and Telescope Array Joint Cosmic Ray Detection, and Cross Calibration

Home , Cherenkov radiation, Giuseppe Occhialini, Heidi Jo Newberg, Pierre Auger Observatory, Star Generation

PIERRE AUGER OBSERVATORY AND TELESCOPE

ARRAY JOINT COSMIC RAY DETECTION, AND

CROSS CALIBRATION

RYAN JAMES LOREK

Submitted in partial fulﬁllment of the requirements

for the degree of Doctor of Philosophy

Department of Physics

CASE WESTERN RESERVE UNIVERSITY

MAY 2019 CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of Ryan James Lorek

candidate for the degree of Doctor of Philosophy*.

Committee Chair Dr. Corbin Covault

Committee Member Dr. Kathleen Kash

Committee Member Dr. John Ruhl

Committee Member Dr. Stacy McGaugh

Date of Defense

18 MAR 2019

*We also certify that written approval has been obtained

for any proprietary material contained therein. Contents

List of Tables iv

List of Figures vi

Acknowledgments xiv

Abstract xv

1 Introduction to and History of Cosmic Rays 1 1.1 Introduction ...... 1 1.2 History of Detection ...... 3 1.2.1 Discovery of Cosmic Rays ...... 3 1.3 Early Experiments ...... 5 1.4 Cosmic Ray Observatories ...... 9

2 The Physics of Cosmic Rays 13 2.1 Ultra High Energy Cosmic Rays ...... 13 2.2 Propagation ...... 14 2.2.1 Galactic Magnetic Fields ...... 14 2.2.2 GZK Energy Spectrum Suppression ...... 20 2.3 Possible Sources ...... 24 2.3.1 Active Galactic Nuclei ...... 24 2.3.2 Starburst Galaxies ...... 27 2.3.3 Diﬀusive Shock Acceleration ...... 28 2.3.4 Exotic Collapsars ...... 32 2.4 Extensive Air Showers ...... 34 2.4.1 Composition ...... 35

3 Methods of Detection 41 3.1 Photo Multiplier Tube ...... 41 3.2 Water Cherenkov Tank ...... 43 3.3 Scintillation Panel ...... 45 3.4 Fluorescence Telescope ...... 50 3.5 Atmospheric Cherenkov Light ...... 53

i CONTENTS CONTENTS

4 Current UHECR Observatories 55 4.1 Pierre Auger Observatory ...... 55 4.1.1 Pierre Auger South ...... 55 4.1.2 Research and Development Array ...... 60 4.2 Telescope Array ...... 62

5 Pierre Auger at the Telescope Array 68 5.1 Auger@TA Phase I: PA@TA at the CLF ...... 69 5.2 Auger@TA Phase II: The Micro Array ...... 72 5.2.1 Communications System ...... 74 5.2.2 Range and Hardware Testing ...... 76 5.2.3 Local Station Controller ...... 77 5.2.4 CANBUS Interface Concerns ...... 79 5.2.5 Software Control ...... 80 5.2.6 Comms Conﬁguration within the Planned Micro Array . . . . 82

6 Cosmic Ray Signal Waveform Comparisons of Auger vs. Telescope Array Surface Detectors 86 6.1 Picoscope ...... 86 6.2 Picoscope Data Cuts ...... 91 6.2.1 Reduced Chisquare Cut ...... 91 6.2.2 Peak Voltage Cut ...... 94 6.3 TA Local SD Analysis ...... 96 6.4 PA and TA Coincidence ...... 100 6.4.1 Local Trigger Time Oﬀset ...... 100 6.4.2 Cross Calibration ...... 102 6.5 TA Global SD Analysis ...... 108 6.6 Picoscope Near Future ...... 109 6.7 TAL and Auger Prime ...... 111

7 Air Shower Reconstruction with Detector Simulations 113 7.1 MCCRRS ...... 114 7.1.1 Phase I ...... 115 7.1.2 Phase II ...... 123 7.1.3 Phase III ...... 123 7.1.4 Phase IV ...... 124 7.2 Reconstructed Primaries ...... 125 7.3 VEM and MIP Matching ...... 130

8 Supporting Work Related to the Pierre Auger Observatory 132 8.1 Wireless Communications Monitoring ...... 132 8.2 Remote Shift Station ...... 134 8.3 Monte Carlo Prototyper ...... 136 8.4 ASCII Boilerplate ...... 144

ii CONTENTS CONTENTS

9 Conclusion 149

A Acronyms 153

B Digi XBee RF Modem Specs 155

C Digi XBee Firmware Settings 156

D MCCRRS Reconstructions 158

iii List of Tables

4.1 A comparison and contrast of the Pierre Auger Observatory and Tele- scope Array [100]...... 67

5.1 Phase I represents the task of cross calibration. Phase II represents direct detection. Phase I is by and large complete, with Phase II prepared and Part 1 beginning in 2019 [107]...... 70

7.1 An outline of the processing method of the MCCRRS broken down by phase...... 115

8.1 Comparing the global triggering rates and array eﬃciencies of the two proposed Micro Array conﬁgurations...... 140

A.1 Table of acronyms used in this thesis...... 154

B.1 Relevant Digi XBee RF Modem parameters as reported from the man- ufacturer technical speciﬁcations [111]...... 155

C.1 Relevant XCTU Firmware settings for the XBee Modem DAQ Unit. All other settings are either default for this version of the firmware, or classified for network security reasons...... 156 C.2 Relevant XCTU Firmware settings for an XBee Modem SD Unit. All other settings are either default for this version of the firmware, or classified for network security reasons...... 157

D.1 MCCRRS reconstructions for available 2ndQuarter of 2017. Event 2 included a local trigger from an SD that was not adjacent to any of the other triggering SDs and so was excluded from the reconstruction. Event 3 included an SD with a time stamp that was an extreme outlier too large for the MCCRRS to account for and so was excluded from the reconstruction. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS...... 159 D.2 MCCRRS reconstructions for 3rd Quarter of 2017. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS...... 160

iv LIST OF TABLES LIST OF TABLES

D.3 MCCRRS reconstructions for 4th quarters of 2017. Event 1 was not reconstructable in the MCCRRS due to an extreme outlier time stamp at an SD near the center of the trigger cluster. Event 2 could not reconstruct beyond a course primary energy estimate. Event 3 failed to reconstruct at Phase IV and thus only produced a rough primary energy and core location estimate. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS...... 161 D.4 MCCRRS reconstructions for 1st Quarter of 2018. Event 2 excluded an outlier SD with an anomalous timestamp that could not be rec- tiﬁed by the MCCRRS. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS...... 162 D.5 MCCRRS reconstructions for 2nd Quarter of 2018. In Event 5 two SDs were excluded from the reconstruction as their timestamps were extremely outside of the coincidence range of the other participant SDs. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS...... 163

v List of Figures

1.1 The cosmic-ray differential flux energy spectrum, with reconstructed EAS data observed by various experiments. The grey box represents the energy region where direct observations of cosmic rays have been made. Note that the energy spectrum has been multiplied by E2.7 to highlight spectral features, with “the knee” and “the ankle” labeled. Figure from C. Amsler et al (2008) [3] ...... 2 1.2 The Explorer 1 satellite in its flight configuration. The instrument module and spent fourth stage booster would remain coupled in orbit. A Geiger-Müellertube on board measured radiation levels in orbit to gauge cosmic ray rates above Earth’s atmosphere and lead to the discovery of the Van Allen Radiation Belts. NASA (1958) [23]. . . . .8 1.3 Interior and exterior view of the ALFMED device used on Apollo flights to gauge the coincidence between cosmic ray detection and astronauts experiencing cosmic ray visual phenomenon. Osborne, Pinsky, and Bailey (1975) [27]...... 9 1.4 Layout of the Volcano Ranch Experiment in its 1960-1963 configuration. Linsley (1985) [28]...... 10 1.5 The multiple apertures of the Fly’e Eye experiment located at the US Army Dugway Proving Ground. Fly’s Eye Collaboration (1981) [30]. 11

2.1 A model of a the galactic magnetic field showing how magnetic field lines follow the galaxy’s spiral arms. Vectors mark the direction of the field and highlight areas with known field reversals. The position of the Sun and the galactiv center are marked for reference. Image from Vallee (1996) [45]...... 15 2.2 COBE data of the CMBR fitted with a black body curve in order to illustrate the black body nature of the CMBR. Fixsen, et al. (1996). [53]...... 22 2.3 From Matthiae’s 2010 paper on the cosmic ray energy spectrum. At propagation distances over about ∼100 Mpc protons with energies > 4×1019 have lost their initial extreme energies due to GZK suppression [52]...... 24

vi LIST OF FIGURES LIST OF FIGURES

2.4 From Kachelrieß, Parizot, and Semikoz’s 2008 analysis of the GZK horizon. The left graph indicates 90% survival rate of UHECRs with an energy E in EeV as a function of radial distance R as measured in MpC for different models of interactions and environments to propagate through. The right graph indicates the percentage of UHECRs of a given energy E in eV that will successfully propagate the radial distance R in MpC. Both graphs indicate a GZK horizon of ∼50 Mpc for UHECRs at the GZK suppression energy limit [56]...... 25 2.5 NGC 4261 taken from the Hubble Space Telescope is an example of a galaxy with an AGN. Relativistic jets ,larger than the luminous portion of the galaxy, being ejected by the AGN are clearly visible. Inset shows close up infrared image of the galaxy core. NGC 4261 is estimated to be 29.4 ± 2.6 Mpc from Earth. Image from NASA and STScI (1992) [60]...... 27 2.6 Composite image of the SBG M82 utilizing visible, infrared, and x- ray images from the Hubble, Spitzer, and Chandra space telescopes respectively. M82 is estimated to be 3.5 − 3.8 Mpc from Earth. Image from The Veritas Collaboration (2009) [66]...... 29 2.7 Sky mapping of anisotropy studies from Pierre Auger [left] and the Telescope Array [right]. Of particular interest is the ”Hot Spot” Ob- served by Telescope Array, which may correlate to M82, but is not observable by the Pierre Auger Observatory. Figures from The Pierre Auger Collaboration (2012), and The Telescope Array Collaboration (2014) [72][73]...... 31 2.8 A cartoon of an EAS from Semikoz and Dmitry (2010) illustrating the shape profile of a typical EAS with UV florescence highlighted. Insert image illustrates the rapid particle spallation and decays that take place as the EAS develops through the atmosphere [80]...... 35 2.9 A Pierre Auger Collaboration reconstruction of Xmax and rmsXmax showing a correlation between the estimated composition of cosmic ray primaries versus energy against various hadronic interaction models. Figure from Aloisio and Gazizov (2010) [82]...... 36 2.10 From Pierre Auger and Telescope Array work group on composition’s proceedings on the UHECR composition working groups within PA and TA (2015). Composition is shown as a function of Xmax with PA’s results on the left, and TA’s on the right. Within the error bars reported by both collaborations the independent assessments of UHECR compositions may, or may not agree with each other [83]. A direct overlay of the two graphs is provided for ease of comparison. . 37

vii LIST OF FIGURES LIST OF FIGURES

2.11 From the 2018 UHECR2018 proceeding, by T.∼AbuZayyad, et al. (2019) showing the latest cross collaboration work towards identifying the differences in TA and PA UHECR EAS reconstructions. (Left) An overall correction of +5.2% (Auger) and -5.2% (TA) to the energy spectra in the common declination band. (Right) Even when restricting data to the common declination bands, and applying the energy scale corrections significant differences between TA and PA energy spectra persist [84]...... 39

3.1 Cutaway of a PMT showing basic elements from the 1970 RCA Pho- tomultiplier Manual Technical Series PT-61 [86]...... 42 3.2 A Cherenkov cone illustrating the beaming eﬀect of the light. Photons radiate perpendicularly to the surface of the bow shock. Figure from Rauscher and Amoroso (2004) [89]...... 45 3.3 A graph from Bichsel, Groom, and Klein (2005) illustrating the stopping power of a positively charged muon through solid copper as a function of muon momentum. Minimum ionization is present at ∼ 5 βγ, which is where a particle passing through the absorbing material will lose the least amount of energy due to “stopping power”. This is the same region that EAS muons exist, and is the basis for calibrated energy deposited in a scintillator [90]...... 47

4.1 Map view of PAO showing approximate location of SDs as red dots, and location and ﬁelds of view of the FDs. Image from the Pierre Auger Collaboration (2015) [99]...... 56 4.2 A PAO SD in the ﬁeld with major components highlighted. Image from the Pierre Auger Collaboration (2015) [99]...... 57 4.3 A PAO FD with shutters open showing telescope apertures. Image from the Pierre Auger Collaboration (2015) [99]...... 59 4.4 A cutaway schematic of an FD telescope with major components highlighted. Image from the Pierre Auger Collaboration (2015) [99]. . . . 59 4.5 Main PCB of the LSC with major components highlighted. From Guglielmi, Courty, Colonges, and Dufour (2014) [103]...... 62 4.6 Layout of the telescope array depicting the three sub-array divisions covered by the three FDs and three communication towers. Figure from The Telescope Array Collaboration (2012) [105]...... 63 4.7 A TA SD with major features highlighted. A communication tower is visible atop a mesa in the background. Image from The Telescope Array Collaboration (2012) [105]...... 65 4.8 A Cutaway cartoon of a TA SD with internal features highlighted. Image from The Telescope Array Collaboration (2012) [105]...... 66

5.1 A cartoon outlining the data ﬂow of the CLF DAQ from the TA and PA SDs at the site. Cartoon from Quinn, et al. (2017) [108]...... 71

viii LIST OF FIGURES LIST OF FIGURES

5.2 The proposed Micro Array SD locations for the initial array configuration. Visible on this chart is the location of the TA CLF to the extreme west, and the BRM communication station to the south by south-east, and the BRM FD site to the extreme south-east. The spacing between each SD is approximately 1.2km [110]...... 74 5.3 A Digi Xbee RF Modem. This model was chosen due to its 1Watt output at the desired 900MHz bandwidth, and industry grade structural robustness...... 75 5.4 A Yagi type antenna to be used in the Micro Array communication system. This antenna was chosen for its 13dbi high gain, and directional broadcasting within a 30o directional beam...... 76 5.5 A Raspberry Pi 3 used as a communications control computer. The Raspberry Pi 3 was chosen due to its versatility, easy programmability, and multiple USB ports which allow for easy hardware coupling and data storage upgrading...... 78 5.6 A cartoon scheme of the Micro Array SD communication network linking individual stations to the central data acquisition system [107]. . . 82 5.7 Members of the Pierre Auger and Telescope Array Collaborations posing in front of the TA CLF on a August 2017 field expedition. Work included calibrating the trigger rates and thresholds of the PAS doublet, as well as installing new electronic in the CLF. Note the TA SDs to the left of the group, and directly over the head of the gentleman second to the left. These are, respectively, the Global and Local triggering TA SDs. The PA doublet is visible on the right side of the CLF building. The single solar panel of the North Type SD, and the Dou- ble Panel of the South Type SD are clearly visible. Less visible is the ASCII boiler plate mounted on top of the South Type SD...... 84 5.8 The former RDA SDs staged at the Cosmic Ray Center in Delta, Utah with their superstructures reconfigured for Micro Array operation. . . 85

6.1 Closeup of the Pico Technology 2206B USB oscilloscope from Pico Technology [112]...... 88 6.2 Layout of the internal configuration of the Picoscope with major components highlighted, from Quinn (2017) [100]...... 88 6.3 The Picoscope unit inside of its housing with power control unit, hard drive, and computer control board visible during its January 2018 installation ...... 89 6.4 Cartoon from S. Quinn, et al. (2018) representing the TAL SD’s flow of signal pulse data and power supply. The left figure utilizes a pair of discriminators and a logic gate to collect signals from the two PMTs. The right figure shows the Picoscope oscilloscope and SBC system swapped in place. Note that modifications to the station’s power distribution system were required in order to supply the needs specific to the Picoscope [109]...... 90

ix LIST OF FIGURES LIST OF FIGURES

6.5 Three examples of triggered events on the Picoscope with their respective Gaussian fits. The top left figure shows a recorded event that failed to fit and does not appear to have a discernible pulse above background noise. The top right figure did produce a fit though it also does not appear to have a discernible pulse above background noise. The bottom figure shows a strong and clear pulse with an excellent fit. 92 2 6.6 χν histograms resulting from the event trace fitting of the June 2018 2 TAL data set for the two PMT channels, as well as a χν versus peak 2 signal voltage plot showing how χν values relate to signal strength. 2 The trend of high voltage events correlating to relatively low χν values indicates that these events represent non-background noise events. . . 93 6.7 The results of the slope of linear fits of the PMT1 vs PMT2, and PMT2 vs PMT1 peak voltages plot as a function of voltage cuts, used in order to determine the best cuts for channel 1 and 2 respectively. The x-axis denotes the voltage value below which data was excluded. The slope values suggests that all data points below 1.5 V for channel 1 and 1.0 V for channel 2 should be excluded from a final fit...... 95 6.8 The results of the index values of power law fits of the PMT1 and PMT2 peak voltages histograms as a function of voltage bin cuts. The x-axis denotes the voltage bin value below which data was excluded. From these plots it appears that power law fits should exclude the first 15 voltage bins of the PMT1 and PMT2 peak voltage histograms. . . 96 6.9 Raw Picoscope data for June 2018 comparing the peak voltages of the two channels for the two TA Local PMTs is shown in the top left graph. 2 The top right graph shows the results of the first χν based cut which removes several high voltage outlier event and a little less than half of the low voltage events that were likely the result of background noise triggers. After a voltage cut is applied to the two PMT channel a fit of higher voltage events is made in the bottom graph, which produces a linear fit with a slope of 1.06±0.04, indicating that the two channels respond linear with respect to each other though channel 1 tends to record events at higher volts than channel 2...... 97 6.10 Picoscope peak voltage histograms for June 2018. The two channels for the two TAL PMTs are shown with linear fits that exclude the first three bins. The index of the power fit to PMT 1 and 2 are -4.05±0.26, and -2.73±0.39 respectively...... 98 6.11 Picoscope histograms for June 2018. The two channels for the two TAL PMTs FWHM values before and after the two cuts are applied. Signals representing reliable EAS traces fall into a very narrow pulse width band centered around 0.25 µsecs...... 99

x LIST OF FIGURES LIST OF FIGURES

6.12 The time offset between PAS and TA Local recorded in microseconds. The left histogram has bin resolutions of 52 nsecs and shows a dominant offset time at ∼1.1µsecs. The right histogram utilizes a bin resolution of 5 nsecs per bin to match the time axis resolution of the Picoscope data, and was fitted with a Gaussian optimized fit. The peak of the fit suggests an offset of -1.098±0.1995 µsecs, with a σ of 0.113 µsecs. . . 102 6.13 From the CTAL June 2018 data set. Over the course of several hundred events the converted ADC count to charge values of all three PAS PMTS show the pedestal value of each event trace for a PMT, as well as the difference between the muon peak from that pedestal value. These values allow for the conversion of a raw ADC trace into a calibrated VEM value...... 103 6.14 The peak voltages of the 306 CTAL events of TAL PMT1 and PMT2. The histogram of PMT1 shows a strong muon pulse that has been fitted with a Gaussian distribution...... 104 6.15 Raw Voltage from TAL and ADC traces from PAS for a single post Cut I CTAL event. The high peak values above background with narrow pulse widths indicates a relatively high energy EAS event...... 105 6.16 (Left) Mean peak MIP and VEM values of the TAL and PAS stations from all 306 CTAL events. (Right) The 106 local triggers found to be within ≈1100 nsecs of each other after the application of Cut I. For higher MIP and VEM value events in the post Cut I data a trend may be beginning to emerge, but more data is necessary to determine if that is true...... 106 6.17 Histograms of the MIPs of the 306 CTAL events as reported by both TAL PMT channels. This was utilized to determine the cut values of Cut IIB...... 107 6.18 (Left) The result of Cut IIA applied to the data showing the best estimated fit line resulting from an estimated upper bound of the cross calibration factor with a value of 1.22±0.34. (Right) The result of Cuts IIB being applied to the data showing the best estimated fit line resulting from an estimated lower bound of the cross calibration factor with a value of 0.77±0.20...... 109 6.19 From Covault, et al. (2018); Calibrated VEM of local triggers for the PA doublet with black dots representing events that registered on the TA Global trigger. A power law fit of the PA doublet local trigger VEM values shows an index of 1.1±0.1 (solid red line), with a fit of 1.01±0.02 for the global events (dashed red line)[107]...... 110

7.1 Plots comparing predicted value of MIP scaling factor from Quinn’s VEM-MIP relation with derived scaling factor at zenith angles of 0o, 15o, 30o, 45o, and 60o. For nearly vertical showers the derived scaling factor matches exactly with the Quinn correlation prediction. At around 45o a divergence appears making this scaling factor unsuitable for reconstructing EAS with steep zenith angles...... 120

xi LIST OF FIGURES LIST OF FIGURES

7.2 Plots of the preliminary reconstructed core Northing and Easting values, including absolute values, and zenith and azimuth angles versus reconstructed energy show no sign of a bias in reconstructed parameters as a result of the MCCRRS calculation methods...... 126 7.3 Differences between the preliminary TA MOU EAS parameter reconstructions versus the MCCRRS reconstructions. Energy reconstructions show a bias towards lower energy estimates than the TA method. Fit values provided in Table 7.2 ...... 128 7.4 Histograms of primary energy fits for Phase I. Notice that the perfect match fit is dominant and that the perfect match energy distribution produces a very symmetric Gaussian distribution...... 129 7.5 Histograms of primary energy fits for Phase II. At Phase II the number of station mismatches approaches the number of stations participating in the trigger, and the energy distributions are no longer symmetric. . 129

8.1 A Sample of the PAO communication system’s status log...... 134 8.2 The RSS in ready to operate configuration located in AW Smith Room 11, Case Western Reserve University...... 137 8.3 From the 2016 comparison between the PA doublet and the small muon telescope co-located at the TA CLF. The red ”Detected” counts denote triggers produced with the muon telescope, while the blue ”T2s with Coincidence” denote EAS witnessed by the PA doublet simultaneously while not participating in triggering...... 142 8.4 From a trial run of 100,000 cosmic rays between the energies of 1015.8 − 1019 eV for the rectangular configuration (left), and 1016 − 1019 eV for the triangular configuration (right) showing the number of cosmic ray EAS detections versus total number of simulated cosmic rays generated. For the rectangular configuration energies below 1016 fail to trigger, while at ∼ 3 × 1017 eV and greater nearly all EAS landing in the fiducial area produce a trigger. For the triangular configuration energies below ∼ 1.5 × 1016 fail to trigger, while at ∼ 2 × 1017 eV and greater nearly all EAS landing in the fiducial area produce a trigger. . 143 8.5 From a trial run of 100,000 cosmic rays between the energies of 1015.8 − 1019 eV for the rectangular configuration (left), and 1016 − 1019 eV for the triangular configuration (right), breaking down the number of triggering events that occurred as a function of number of stations triggered...... 143 8.6 From a trial run of 100,000 cosmic rays between the energies of 1015.8 − 1019 eV for the rectangular configuration (left), and 1016 − 1019 eV for the triangular configuration (right), showing the trend in triggering coincidence levels as the energy of cosmic ray primaries increases. . . 144

xii LIST OF FIGURES LIST OF FIGURES

8.7 From a trial run of 100,000 cosmic rays between the energies of 1015.8 − 1019 eV for the rectangular configuration. Lower energy triggers remain concentrated near the center of the Micro Array, as they do not produce a sufficiently high enough VEM density to produce a three-fold coincidence without straddling multiple SD positions. By contrast higher energy events successfully produce triggers outside of the array due to their much larger LDF footprint...... 145 8.8 From a trial run of 100,000 cosmic rays between the energies of 1016 − 1019 eV for the triangular configuration. Lower energy triggers remain concentrated near the center of the Micro Array, as they do not produce a sufficiently high enough VEM density to produce a three-fold coincidence without straddling multiple SD positions. By contrast higher energy events successfully produce triggers outside of the array due to their much larger LDF footprint...... 146 8.9 The ASCII upgrade prototype as installed at the PAO infill (top image), and the ASCII boilerplate located at the TA CLF atop the PAS (further away) SD (bottom image)...... 148

xiii Acknowledgments

I give my most sincere thanks to my good friend Grant Junno for his countless pieces of helpful advice in regards to computer coding, which no doubt saved me from many hours of grief, and did a great deal to expedite my work. To my undergraduate assistants Peter Fedrizzi, who turned the remote shift station hardware into the work space that it is today, and Annika Gabriel, whose hard work and dedication enabled all of my projects to continue to move forward to completion, you have my deep thanks. I’d like to acknowledge my labmates Sean Quinn, Danielle Lahurd, and Robert Halliday who were the giants that gave me shoulders to stand on. I’d like to acknowledge my colleagues at the Colorado School of Mines, Dr. Fred- eric Sarazin, and Jeﬀ Johnsen, who dedicated many hours of hard work in the desert of Utah to achieve our common science goals. To my closest friends Haley Gittleman, Ian Nemitz, Sukrit Sucharitakul, Bryce Murray, and Andrew Ferris you made the trying and taxing task of obtaining a graduate degree after a ﬁve year absence from academia still seem like a vacation. My deepest thanks goes to my advisor, Prof. Corbin Covault. You believed in me when it seemed like the world had abandoned me, and you gave me the chance that it felt like I didn’t deserve.

xiv Pierre Auger Observatory and Telescope Array Joint Cosmic Ray Detection, and Cross Calibration

Abstract by RYAN JAMES LOREK

For over a decade both the Pierre Auger (PA) and Telescope Array (TA) observatories have been monitoring the sky for Ultra High Energy Cosmic Rays, with PA in the Southern Hemisphere and the TA in the Northern. Both observatories use similar architecture in their hybrid detector arrays, but use notably different detector hardware and data analysis methods. The goal of this research is to enable cross calibration of The PA hardware relative to that of the TA’s, and to ultimately allow for simultaneous direct detection and reconstruction of identical extensive air showers. The cross calibration of hardware was conducted by deploying and operating both two PA type surface detectors, and a TA type surface detector fitted with special triggering electronics at the TA central lasing facility, and allowing them to collect local cosmic ray triggering data over the course of several months. The parameter utilized to make the cross calibration is the calibrated detection energy of the two different stations, that being MIP for TA and VEM for PA. The new TA electronics were found to function as designed, and were used to find that the PA South Type and the TA type surface detectors experience a systematic time offset of -1.098±0.1995 µsecs. This result was applied to data representing cosmic ray events recorded by both the TA and PA types of detectors and an upper bound to the cross calibration factor of 1.22 ± 0.34, and a lower bound of 0.77 ± 0.20 were found, with this factor representing MIP = C × VEM. As data continues to be recorded these bounds will converge onto a conversion factor that will be utilized to directly compare TA and PA data sets.

xv Abstract

Direct detection and reconstruction of air showers will be facilitated by the completion of the Micro Array, an array of PA type surface detectors co-location with TA surface detectors at the TA Observatory, that will operate concurrently with the up- graded TA Observatory. Two conﬁgurations for this Micro Array are considered, one rectangular one triangular, with energy thresholds of ∼ 1 × 1016 eV and ∼ 1.5 × 1016 eV, detection rates of about 2 and 1.5 cosmic ray showers at this threshold per day, and detection eﬃciencies approaching 100% for primary energies above ∼ 3 × 1017 eV and ∼ 2 × 1017 eV respectively. The ground work for the construction and operation of the Micro Array are also detailed.

xvi Chapter 1

Introduction to and History of Cosmic Rays

1.1 Introduction

The study of cosmic rays was one of the new fields of scientific research born at the beginning of the 20th Century, and continues to this day. What started as a curious observation during meteorological studies of Earth’s atmosphere rapidly coupled with the then recent discoveries in nuclear physics which grew into the beginning of experimental particle physics and was a motivator for early space exploration. Cosmic Rays are a form of ionizing radiation originating from sources outside of the Earth. The surface of the Earth encounters roughly 1000 cosmic rays per second per square meter of surface area across all energies. The energy spectrum of cosmic rays can be neatly represented in a differential flux power law, that falls steeply and uniformly for approximately 10 orders of magnitude, steepens significantly at a few PeV, and then flattens again at about 10 EeV. These features in the energy spectrum are known colloquially as “the knee” and “the ankle” as the total energy spectrum when graphed on a log-log plot resembles a cartoon drawing of a leg, as can be seen in Figure 1.1 [1][2]. The lowest energy forms of cosmic radiation originate from our Sun and nearby stars. Although not yet proven conclusively, it is widely believed that supernovae

1 1.1. INTRODUCTION CHAPTER 1. HISTORY

Figure 1.1: The cosmic-ray differential flux energy spectrum, with reconstructed EAS data observed by various experiments. The grey box represents the energy region where direct observations of cosmic rays have been made. Note that the energy spectrum has been multiplied by E2.7 to highlight spectral features, with “the knee” and “the ankle” labeled. Figure from C. Amsler et al (2008) [3] contribute the majority of cosmic ray flux of energies approaching 1 PeV at “the knee”. Above this limit the cosmic ray flux is dominated by ultra high energy radiation with particle energies ranging from three to six orders of magnitude greater than what can be produced by the most powerful particle accelerators currently built [4][5]. The latter form of cosmic rays are known as Ultra High Energy Cosmic Rays (UHECR). The source of UHECR is a yet to be resolved mystery, although hypotheses exist such as particles episodically accelerated by active galactic nuclei, generated en mass by star burst galaxies, or accelerated over time by the Milky Way’s powerful galactic

2 1.2. HISTORY OF DETECTION CHAPTER 1. HISTORY magnetic ﬁeld. UHECR, and the question of their origins, are the focus of this research [5][6].

1.2 History of Detection

1.2.1 Discovery of Cosmic Rays

Cosmic Rays may have first been inadvertently observed by Charles-Augustin de Coulomb while conducting his experimental research on electrostatics in the 1780s. Coulomb observed that a sensitive experimental apparatus, known as an electroscope, used to gauge the strength of static electric fields would spontaneously discharge no matter how carefully insulated it was. Coulomb however never made the conclusion of this being the results of cosmic rays, or any other form of ionizing radiation, but over the next century other researchers, including Michael Faraday, would confirm Coulomb’s observations [7]. By repeating Coulomb’s experiments with electroscopes that had the gas evacuated from them Crookes correctly concluded that the dis- charging must be due to some sort of ionization of the air around the electroscope’s elements [8][9][10]. Perhaps the first proper experimental observations of cosmic rays were made by Julius Elster and Hans Geitel who conducted a series of experiments gauging the effects of electricity in the atmosphere. By building an extremely sensitive electroscope nested inside a heavily shielded metal box, and observing the discharge rates they concluded that the source of the radiation must be from outside of the box and very penetrating, that is to say, high in energy [8][11]. Charles Thomson Rees Wilson immediately followed up these observations by suggesting that the origin of high penetrating ionizing radiation in the atmosphere may have a cosmic origin. Wilson spent several years conducting his own shielded electroscope tests in tunnels but due to experimental uncertainties was unable to definitively

3 1.2. HISTORY CHAPTER 1. HISTORY conclude whether rates were increasing or decreasing under different observation conditions. In his efforts to create a more sensitive and certain method of detecting ionizing radiation Wilson would invent the cloud chamber in 1911, which would be a critical tool for the field of particle physics and research in cosmic rays [8][12]. In the first few years of the 1900s researchers such as Rutherford, Cooke, McLen- nan, and Burton used different configurations of shielding on electroscopes to determine that the ionizing radiation did not have a particular direction of origin, and in 1907 and 1908 Eve made a transatlantic voyage where he determined that the radiation rates in England, Canada, and various locations over the Atlantic Ocean all had approximately the same rate of detection. This suggested that the radiation indeed had a non-terrestrial origin [13][8]. The proper discovery of cosmic rays was made by Victor Hess in 1912, when Hess made his famous balloon observations that lead to the accepted conclusion that the observed radiation was indeed originating from off of the Earth. Preceding this discovery ionizing radiation had been observed and was being extensively studied in laboratories. At that time background radiation was understood to originate from rocks in the Earth’s crust since all known natural sources of radioactive materials were mineral ores. Hess observed that as he ascended inside a high altitude balloon that an on board electroscope would discharged more rapidly. This followed published findings by Theodor Wulf who observed an increase in radiation rates at the top of the Eiffel Tower over what was observed at its base, and Domenico Pacini who observed a decreased rate in detectors placed under several meters of water. Based on these observations Hess concluded that the empirical data was contrary to the Terrestrial based radiation theory and that the observed radiation was coming from outside of the Earth. However, this lead to a greater mystery that is still not resolved: What are the origins of cosmic rays [13][8][14]?

4 1.3. EARLY EXPERIMENTS CHAPTER 1. HISTORY

Shortly before observing an increased detection rate of radiation in high altitude balloons, Hess conducted another balloon-borne experiment during a solar eclipse. Hess observed that the radiation detection rates during the eclipse were unchanged relative to normal background radiation levels. It is reasonable to assume that even for extremely deep penetrating radiation that a celestial body as large as the Moon would block most radiation if it were originating from the Sun, and so the Sun was ruled out as a significant source of cosmic rays [13][14]. For two decades after their discovery cosmic rays were believed to be a component of the electromagnetic spectrum, hence the “ray” portion of their name. In the 1930s researchers observed that cosmic rays were deflected by the Earth’s magnetic field, showing that at least a major component of cosmic rays must be charged. So by the end of this era of research cosmic rays had been fully characterized as charged particles originating from outer space that enter the Earth’s atmosphere as ionizing radiation, which is still understood to be true to this day [15].

1.3 Early Experiments

The previous section’s descriptions of the discovery of cosmic rays, and the means by which the scientific community came to understand their nature was conducted under the fields of meteorology, atmospheric science, and even as corollary studies to agrar- ian research. Once the atmospheric ionizing radiation phenomenon was attributed to cosmic ray particles from outer space the new field of particle physics stepped up to continue the study of cosmic rays. The legacy of these subsequent studies carry through to today in the form of current cosmic ray observatories such as Pierre Auger and the Telescope Array. Early studies of cosmic rays were conducted with cloud chambers, which were invented by Charles Thomson Rees Wilson for the explicit purpose of observing the

5 1.3. EARLY EXPERIMENTS CHAPTER 1. HISTORY cosmic ray phenomenon. A cloud chamber is a sealed vessel that contains air that is super saturated with some type of alcohol and kept very cold, usually being cooled with dry ice. A charged particle moving through the alcohol vapor will on occasion scatter off of an alcohol molecule in a way that causes it to ionize. This ionized molecule acts as a condensation seed causing the gas around it to rapidly condense into a tiny vapor cloud that is visible to the human eye. These condensation events happen so rapidly that they appear as nearly continuous condensation trails. The thickness and opacity of the trail gives a sense of the “size” of the particle, their length gives a sense of the momentum and lifetime of the particle, and if in the presence of an external uniform magnetic field observed curvature of the path can indicate the charge of the particle [16][17]. Cosmic rays proved to be a valuable source of high energy accelerated particles that were used for particle physics studies prior to the development of particle accelerators and colliders. Cloud chamber observations lead to the discovery of the positron by Carl Anderson in 1932 when he recorded tracks that indicated that the observed particle had all of the same qualities of an electron but were definitely positively charged. In 1936 Anderson would share the Nobel Prize with Hess for their discoveries in the field of cosmic ray research, and in that same year he would co-discover the muon with Seth Neddermeyer [16][18][19]. Cloud chamber research continued into the 1950s with larger chambers and more sensitive film being placed for long periods at high altitudes on mountains in order to benefit from the increased detection rates of cosmic rays that hadn’t attenuated through the atmosphere. This lead to the discovery of charged pions in 1947 by the collaboration of Cecil Powell, Csar Lattes, and Giuseppe Occhialini. The success of the cloud chamber lead to the invention of the bubble chamber in 1952 by Donald A. Glaser. Bubble chambers work on the same principle as cloud chambers, but instead of a supersaturated cold vapor, they utilize superheated transparent liquids,

6 1.3. EARLY EXPERIMENTS CHAPTER 1. HISTORY like liquid hydrogen or xenon, as their detection medium. Bubble chambers would become the major particle physics experimental apparatus at research facilities like CERN and Fermilab [16][20][21]. July 1957 to the end of December 1958 was declared the International Geophysical Year. This event was intended to focus scientific efforts towards a myriad of Earth science studies, including cosmic ray studies, by way of international collaborations. Rockoons, sounding rockets carried aloft by balloons before being launched, were becoming popular as a means to study upper atmospheric phenomena, and through a series of major and minor geo-political events that won’t be discussed here the Inter- national Geophysical Year ended up being the catalyst that would trigger the Space Race. One rockoon researcher, James Van Allen, helped designed and instrument the United States’ first successful satellite Explorer 1, Figure 1.2, which was launched in 1958. Explorer 1 was a modified artillery rocket motor mounted atop of a modified US Army ballistic missile designed and built by Wernher von Braun’s rocket team. Its primary instrumentation was a Geiger meter intended to give readings of the cosmic ray rate above the Earth’s atmosphere. Surprisingly, the measured rates were lower than expected, which Van Allen attributed to the instrument being saturated by radiation trapped in Earth’s Magnetic field. This was confirmed by observations made by Explorer 3, and the discovery of the Van Allen radiation belts [22][23]. An interesting moment in the history of cosmic ray studies occurred during the US Apollo Program. Some of the Apollo lunar space craft carried with them plates of materials that would be exposed to space at different parts of their flight in order to gauge the intensity and frequency of cosmic ray strikes both in transit to the Moon and on the Lunar surface. These instruments would be penetrated by cosmic rays and by counting the number of penetrations as well as the size and depth of the holes left behind in a post-flight analysis researchers could determine energy thresholds and detection rates. It turned out that the astronauts themselves also served as a type

7 1.3. EARLY EXPERIMENTS CHAPTER 1. HISTORY

Figure 1.2: The Explorer 1 satellite in its flight configuration. The instrument module and spent fourth stage booster would remain coupled in orbit. A Geiger-Müellertube on board measured radiation levels in orbit to gauge cosmic ray rates above Earth’s atmosphere and lead to the discovery of the Van Allen Radiation Belts. NASA (1958) [23]. of cosmic ray detector. In 1952 Cornelius Tobias predicted that cosmic rays may interact with the human visual system to cause anomalous light perception. During the trans-lunar flights astronauts did indeed experience strange flashes of lights that couldn’t immediately be explained. After a series of experiments, including having the astronauts wear a device in flight called ALFMED which was a hood made of scintillation panels used to identify a correlation between anomalous flashes and the passage of a cosmic ray (Figure 1.3), it was determined that the astronauts were indeed witnessing the effects of Cherenkov radiation produced by cosmic rays in their eyes. This lead to a series of space station based experiments on the cosmic ray visual phenomenon which continue to this day aboard the ISS [24][25][26][27].

8 1.4. COSMIC RAY OBSERVATORIES CHAPTER 1. HISTORY

Figure 1.3: Interior and exterior view of the ALFMED device used on Apollo ﬂights to gauge the coincidence between cosmic ray detection and astronauts experiencing cosmic ray visual phenomenon. Osborne, Pinsky, and Bailey (1975) [27].

1.4 Cosmic Ray Observatories

During the 1960s cosmic ray observatories began being built that pioneered the techniques that are well established today of utilizing a large array of surface detectors spaced out at regular intervals to search for coincidences in radiation detection that would indicate that a UHECR had just passed through the atmosphere. A notable example is the Volcano Ranch Experiment. The Volcano Ranch Experiment ran from 1959 to 1963 with 3.3m2 scintillator panel type detectors spaced at 442m in a hexag- onal pattern, which was expanded in 1960 to 884m spacing for the remainder of its operation (Figure 1.4). The Volcano Ranch Experiment was very successful in ex- tending the observed cosmic ray energy spectrum, and made the first observations of UHECR compositions and arrival direction distributions, showing for the first time that the highest energy particles appeared to arrive anisotropically. These observations helped to confirm models of particle interactions by directly observing extensive air shower daughter particles. On 22 February 1962 a primary particle with an energy greater than 1020 eV was observed by the experiment, which was the highest particle

9 1.4. OBSERVATORIES CHAPTER 1. HISTORY energy recorded at that time and fueled evidence that the highest energy cosmic rays may originate from outside of the galaxy [28][29].

Figure 1.4: Layout of the Volcano Ranch Experiment in its 1960-1963 conﬁguration. Linsley (1985) [28].

Following the success of the Volcano Ranch Experiment in the late 1970s and early 1980s successor experiments were designed that utilized new techniques of cosmic ray detection that capitalized on the fluorescent light signature produced by extensive air showers passing through the atmosphere. The Fly’s Eye experiment operated from 1981 to 1993 in the United State’s Army Dugway Proving Ground of Utah, USA. This site was chosen due to its dark skies, and its elevation at 1372 m msl, or a vertical atmospheric depth of 860 g/cm2 in terms of X, which would allow florescence detectors to operate closer to the upper atmospheric incident of shower interaction maxima than if the observatory was located at sea level1. The Fly’s Eye observatory consisted of 67 large aperture telescopes containing a spherical mirror and a set of photo multiplier tubes. The telescopes were arranged in a cluster that divided the observable sky into 880 pixels covering about 0.0066 steradians each (Figure 1.5). Fly’s Eye proved that fluorescence telescopes were an effective method to reconstructing cosmic ray shower arrival directions and primary energy, and upon 1See Section 2.4 for an explanation of X

10 1.4. OBSERVATORIES CHAPTER 1. HISTORY the completion of data collection Fly’s Eye had collected the then world’s largest UHECR data set [30][31].

Figure 1.5: The multiple apertures of the Fly’e Eye experiment located at the US Army Dugway Proving Ground. Fly’s Eye Collaboration (1981) [30].

The High Resolution Fly’s Eye Cosmic Ray Detector (HiRes) was the immediate sequel experiment to Fly’s Eye, and operated from 1997 to 2006 in the western desert of Utah, USA. With larger mirrors and smaller PMTs HiRes was able to provide a higher resolution reconstruction of EAS from atmospheric florescence. HiRes’s technique of cosmic ray observation proved successful as the observatory made the first observation of the Greisen-Zatsepin-Kuzmin limit cutoff2, and would be the basis of the florescence detectors used in the Telescope Array, and the Pierre Auger Observatories’3 [31][32][33][34]. The Akeno Giant Air Shower Array (AGASA) is a large surface detector (SD) array constructed in Akeno, Japan, ∼ 120km west of Tokyo, covering an area of about 100 km2, consisting of 111 scintillator type surface detectors (SD) each with an area of 2.2 m2 with a nearest-neighbor separation of about 1 km. The SDs are connected with

2See Chapter 2 3See Chapter 4

11 1.4. OBSERVATORIES CHAPTER 1. HISTORY pairs of optical fibers, and are controlled at detector sites with their own CPU and through rapid communication with a central computer. The array is supplemented with 27 muon detectors, built in six different sizes, that allows AGASA to observe EAS component-wise, separating muons from electrons. AGASA set the record for the most UHECR events recorded at and above 1020 eV that both supported evidence of GZK suppression4, and strengthened the hypothesis of extra galactic origins. The success of the AGASA array lead to the design utilized in the Telescope Array and Pierre Auger Observatories’ surface array component [35][36]. The success of the AGASA architecture provided a model for future cosmic ray observatories to built on with members of the AGASA and HIRES collaboration merging to form the Telescope Array Collaboration [37][38][39][40]. The history of the discovery and observation of cosmic rays outlines the current scientific understanding of cosmic rays as well as presenting mysteries that are yet to be solved. The greatest question still remaining about high energy cosmic rays is their origin. The main science goal of this dissertation work detailed in this thesis is the continuing effort to discover the origins of ultra high energy cosmic rays.

4the GZK suppression mechanism is detailed in Chapter 2

12 Chapter 2

The Physics of Cosmic Rays

2.1 Ultra High Energy Cosmic Rays

Ultra high energy cosmic rays (UHECR) are defined as ionizing nuclei, originating from extra solar sources, that have primary energies measured on the PeV to EeV scale. These types of particles are extremely rare, with current measured rates indicating an E ≥ 1020eV particle encountering each km2 of Earth’s surface about once a century [6][41]. This makes detecting UHECR challenging and necessitates observatories that cover very large areas of the Earth’s surface in order to gather a sufficient quantity of statically significant data. The origins of UHECRs are yet unknown, and is a major motivator for their continued study. Hypotheses regarding their origins include extraordinary astrophysical objects, like Active Galactic Nuclei (AGN) which may act like very powerful particle accelerators blasting beams of ultra high energy particles into space, Starburst Galax- ies (SBG) which may be producing high levels of radiation due to their extremely high rates of star generation and supernovae, or perhaps young and highly magnetic neutron stars accelerating iron nuclei to relativistic speeds. Conventional thinking is that these sources may utilize Fermi diffusive shock acceleration as their principle mechanism of energized particle production. Determining which hypothetical source is the likeliest candidate depends greatly on how UHECRs propagate through space.

13 2.2. PROPAGATION CHAPTER 2. PHYSICS

2.2 Propagation

Two major factors of cosmic ray propagation that are directly relevant to studying their origins are the strength and turbulence of our galaxy’s magnetic field, and Greisen-Zatsepin-Kuzmin (GZK) suppression. The galactic magnetic field has the potential to deflect cosmic ray particles from extra galactic sources sufficiently so that the observed arrival direction on Earth may not correlate to the cosmic ray’s actual point of origin [6]. Meanwhile, GZK suppression imposes an expected cutoff in cosmic ray spectral flux above about 1020eV, and sets a theoretical upper limit on the distance that UHECR generating astrophysical objects may exist in order for Earth based observers to still encounter UHECRs before their energy is attenuated [41][42].

2.2.1 Galactic Magnetic Fields

The galactic magnetic field is a challenging problem as Earth based observers cannot directly observe or measure it outside of the immediate vicinity of the Solar System. The best estimates of the magnetic field’s large-scale shape and behavior comes from observing other bar-spiraled type galaxies, especially ones that appear perpendicular to us and how matter within those galaxies may be influenced by magnetic fields, and from the observation of the polarization of light from distant stars by none spherical dust grains being influenced by local magnetic fields within our own galaxy [6][43]. Field measurements near the Solar System indicate a strength of ∼2µG aligned along the galactic longitude of 90o, but this field reverses itself at a distance of 500pc towards the galactic center, and at ∼3kpc in the direction away from the galactic center (Figure 2.1). This demonstrates that the galactic magnetic field may not be treated as a simple toroidal system, and are likely to be turbulent on both large and small scales [44][45][46].

14 2.2. PROPAGATION CHAPTER 2. PHYSICS

Figure 2.1: A model of a the galactic magnetic field showing how magnetic field lines follow the galaxy’s spiral arms. Vectors mark the direction of the field and highlight areas with known field reversals. The position of the Sun and the galactiv center are marked for reference. Image from Vallee (1996) [45].

In the 1950s cosmic ray researchers, including Enrico Fermi, calculated that fluctuations in galactic magnetic field strength and direction would cause charged particles to accelerate as they propagate through the galaxy and may account for the energy observed in cosmic rays. Fermi’s work speculated that on large scales the magnetic fields within a region of the galaxy comparable in size to one of the spiral arms should be fairly regular, with charged matter and the field lines working mutualistically to

15 2.2. PROPAGATION CHAPTER 2. PHYSICS regulate each other. That is to say that charged matter rarely escapes the confinement of the field lines, and the magnetic field lines rarely experience dramatic fluctuations due to the presence of the charged matter. Indeed, later astronomical research suggests that field turbulence from sources like giant molecular clouds or nebula are only on the scale of about 10pc, while the spacing between these disturbances is on the order of 100-150pc, and, as previously stated, field reversals appear on distance scales of thousands of pc. So it may be generally true that on most scales of interest galactic magnetic fields are fairly regular, and it may be the case that within the turbulent regions the magnetic fields are still regular though their alignment may be in a random orientation with respect to the surrounding field [43][47][48]. Based on these assumptions Fermi made a compelling argument in his 1949 and 1954 papers that protons propagating through these galactic magnetic field lines would on average experience more scatters and collisions that would add to the energy of the particle than those that would take energy away. This is due to how less likely an ‘overtaking’ type of collision would be compared to a ‘head on’ one. In this context collision means a free particle’s velocity meeting a line of magnetic force of intensity H at a “pitch angle” θ so that a quantity “q” is generated as

sin2 (θ) q = (2.1) H with reﬂection type collisions occurring when H > 1/q. Fermi also estimated that a free proton with in “injection energy” of 200MeV or greater would generally only lose negligible amounts of energy compared to its total energy during unfavorable collisions, with diﬀusive shock acceleration being a likely injection energy source. This suggests that the eldest protons propagating through the galaxy would reach the highest energies in a time dependant relation expressed as

t/A E(t) = Eoe (2.2)

16 2.2. PROPAGATION CHAPTER 2. PHYSICS where A is the time required for a particle to gain a factor of e in energy. Fermi estimated A to be on the order of 100 million years, which is also fairly close to the estimated rate of particle-destroying nuclear collisions, described as B. The probability of ﬁnding a particle with age t is expressed as

dt e−t/B = (2.3) B which leads to a probability of observing a particle with energy E as

dE (2.4) E1+A/B

This produces a cosmic ray energy spectrum close to the one that is observed, and so suggests that UHECRs may originate within the Milky Way Galaxy [43][48]. Fermi’s estimates on the effect of galactic magnetic field acceleration lead to a curious conclusion that though a proton may be accelerated to energies approaching the UHECR regime, these particles would no longer be strongly bound to the galaxy and would likely escape the disk of the galaxy after a relatively short time. This came from observations that the magnetic fields in the spiral arms may be regular enough to efficiently channel particles with relativistic velocities out of the galaxy rather than continuing to accelerate them to higher energies. Current estimates have the Milky Way’s radius at 23-28kpc, with a disk width of about 0.6 kpc. Although allowances must be made for the fact that particles will tend to travel in a random path due to scattering, and would likely be guided along the spiral path of the galactic arms, it is clear that a relativistic particle traveling with a significant portion of its velocity vector pointing radially outwards would reach the edge of the galaxy in less time than Fermi estimated as A. Nuclei heavier than a proton will be more likely to scatter or experience a particle destroying collision due to their larger nuclear scattering cross section. Therefore less particles would be available to be accelerated on long time

17 2.2. PROPAGATION CHAPTER 2. PHYSICS

scales, and the estimate of time B must be reduced. These realizations suggested that UHECRs above some energy being observed on Earth may originate from outside of the Milky Way Galaxy as it seems less likely that the most energetic or heaviest UHECRs would survive long enough within the galaxy’s magnetic field to produce the entirety of the cosmic ray energy spectrum that is observed [43][48][49]. In the latter half of the 20th Century through the early 2000s astronomical surveys continued to refine the scientific community’s understanding of the local magnetic fields in the region of our galaxy surrounding the Sun. Cosmic ray propagation “back-tracing” models have been generated by simulating an antiproton originating from Earth propagating isotropically, and then recording trends in deflection. These models work well for general assumptions of the galactic magnetic field, but encounter problems in the form of field turbulence. As stated, most turbulence exists in the form of giant molecular clouds and nebulae with unknown and unmeasured field which may be oriented randomly with regards to the galactic field. This makes modeling turbulence extremely difficult especially when attempting to predict travel paths of particles originating from extra galactic sources. Extra galactic fields also exist and are even less understood or measured than the galactic magnetic field. [43][46][47][50]. Despite these challenges, astrophysicist use their best estimates of galactic and extra galactic magnetic fields to predict how UHECRs originating and propagate from extra galactic sources. It is very likely that while traversing the extra galactic fields that these high energy charged particles will have their courses steered and changed several times as they traverse the multiple Mpc distance from one galaxy to another. Upon encountering the Milk Way Galaxy the galactic magnetic field, particularly in regions with strong regular fields, may act like a lens and strongly deflect the UHECRs so that an observer can no longer deduce the particles source from its observed arrival direction. Capel and Mortlock estimated in their 2019 paper that the mean angular deflection resulting from the combined effect of galactic and

18 2.2. PROPAGATION CHAPTER 2. PHYSICS

extra galactic ﬁelds may be estimated as:

E −1 B D 1/2 l 1/2 θ ≈ 2.3o c (2.5) 50EeV 1nG 10Mpc 1Mpc

correlating the primary particle energy E to the root mean square ﬁeld strength B

for a travel distance D, which is much greater than the coherence distance lc, with the total magnetic field treated as a random Gaussian field with zero mean. Detailed modeling of these field deflection effects is necessary if observed anisotropy is expected to point towards a potential UHECR source [43][46][47][50]. While galactic and extra galactic magnetic field deflections remain an extremely challenging factor to model and account for, key point to these deflections relevant to EAS reconstruction is the primary energy dependence. As can be seen in equation 2.5 the net deflection angle is inversely proportional to the cosmic ray primary energy. As the primary energy increases the angle of deflection will become smaller and smaller. This can be evaluated further by considering the classical gyro-radius formula

E Rgyro = 0.11Mpc (2.6) ZBµG

With magnetic field strengths estimated to be in the micro Gauss range, a proton with primary energy ∼ 1019 eV would experience a gyro radius deflection larger than the estimated size of the galaxy. For an iron-like primary with the same energy this deflection would be very different as the atomic number, Z, would be 26 times that of a proton. This motivates the exploration of ultra high energy cosmic rays, energies above ∼ 1018 eV, as these cosmic rays should have fairly small deflection effects being produced by the galactic and extra galactic magnetic fields and allow for the possibility to identify anisotropy which may point back to the UHECR generator source. Likewise, in order to properly account for the ”Z effect” on the deflection properly

19 2.2. PROPAGATION CHAPTER 2. PHYSICS reconstructing the chemical composition of the primary particles being observed is also necessary and is discussed in Section 2.4.1. [43][46][47][50].

2.2.2 GZK Energy Spectrum Suppression

GZK suppression is the phenomenon where exceptionally high energy cosmic ray primaries will scatter inelastically with Cosmic Microwave Background Radiation (CMBR) photons in a way that will cause any cosmic ray primary above a certain energy level to lose energy over a path length measured in tens Mpc until the primary no longer possesses energy above the interaction energy limit. A description of GZK suppression with comic ray primary energies at or above 1020 eV undergoing photohadronic interactions with CMBR photons at energies representing the peak energy of the CMBR black body spectrum (Figure 2.2) follows: The interaction energy limit is found by analyzing the type of interactions that are possible between a proton nuclei type UHECR and a photon. The primary mode of interaction for these particles is a photopion interaction in which a πo is produced. Pion production via this mechanism follows three paths:

γ + p → π+ + n

γ + p → πo + p (2.7)

γ + n → π− + p

In a laboratory frame of reference, this interaction energy can be found from the square of the center of mass energy of the two particles

2 s = mp + 3Ep (1 − βpcosθ) (2.8) with representing the energy of the photon, the subscript p parameters characterizing the proton, and cosθ being the angle at which the two particles meet. Assuming

20 2.2. PROPAGATION CHAPTER 2. PHYSICS a head-on collision, and an average CMBR energy of ∼6.34 × 10−4 eV the GZK threshold energy can be found as.

mπo 20 E = (2m + m o ) ' 10 eV (2.9) p 4 p π

This describes a maximum energy cutoﬀ for protons to propagate through cosmic space before being energetically quenched due to photohadronic interactions, resulting in the “post-knee” cosmic ray energy spectrum that we currently observe. Greisen further pointed out that a mean path length for these interactions, nσ, based on a mean scattering cross section of 200 µb, and a photon density of 5.0 × 10−4m−3, is 3 Mpc. Calculating a distance scale for this loss of energy

E L = (nσ)−1 (2.10) ∆E with E being the initial proton energy, in this case 4 × 1019 eV as that is the pion production energy threshold, and ∆E being the loss of energy per interaction, leads to a scale of 10 Mpc [6][41][42][51][52]. As a result, cosmic ray protons at or above ≈1020 eV will undergo a photopionic interaction over a mean path length of 10 Mpc causing a loss of 10-15% of their energy per interaction. This eﬀect is much more pronounced for cosmic ray protons with energies much greater than the 4 × 1019 eV interaction threshold as they have larger interaction cross-sections resulting in photohadronic interactions taking place at higher rates causing a greater energy loss and consequently a shorter distance scale [52]. It is noteworthy that the photon energy does play a small role in this calculation as well. As Figure 2.2 illustrates, the CMBR is a black body spectra. While so far the photon energies at the CMBR spectrum peak has been addressed, and propagating primary particle will likely encounter photons at higher and lower energies as well.

21 2.2. PROPAGATION CHAPTER 2. PHYSICS

Since the photopion interaction is dependent on the total collision energy of the particles, in the reference frame of the primary proton a lower energy photon would mean a higher energy proton being necessary in order for the GZK photohadronic interaction to take place, and vice-versa for higher energy photons.

Figure 2.2: COBE data of the CMBR ﬁtted with a black body curve in order to illustrate the black body nature of the CMBR. Fixsen, et al. (1996). [53].

The above calculation applies to the specific example for CMBR photons at the CMBR spectrum peak, and for proton energies that are at photopion production threshold of about 4 × 1019 eV. The CMBR photons interact in different ways with non-proton heavy nuclei type cosmic rays. The photopion production is limited to proton type cosmic rays, but a GZK suppression-like effect will apply to heavier nuclei type cosmic rays as well. Heavy nuclei are subject to photo-disintegration (spallation) in which nucleons are stripped away from heavy nuclei as the result of

22 2.2. PROPAGATION CHAPTER 2. PHYSICS relativistic collisions with photons. This is represented thus

(A, Z) + γ → (A − n, Z − n0) + nN (2.11) where A and Z are the atomic mass and number respectively, and n/n’ represent the number of stripped nucleons N. This stripping happens at energies above ≈ 8×106eV and so relativistic nuclei with energies of ≈ 5 × 1018eV/nucleon encountering CMBR photons of only ≈ 7 × 10−4eV will be subject to these interactions. The heavy nuclei experience little to no recoil from these collisions, but the ejected nucleon, whether it is a proton or a free neutron that will decay into a proton, are now subject to the same photopion interaction described above if they meet the energy threshold requirement1. Eventually high energy iron-like nuclei cosmic rays, if given a suﬃcient path length to propagate through, will lose nucleons and experience an energy suppression akin to the GZK proton energy suppression. [52][54][55]. One result of these suppression eﬀects is the so called “GZK Horizon” which exists around the Earth beyond which observers do not expect to see UHECRs above the photopion interaction energy limit. The distance to this horizon varies depending upon the primary particle energy and chemical composition, however, as Figures 2.3 and 2.4 demonstrate, the GZK horizon appears to exist at ∼50-100 Mpc for UHECR primaries detected at or above ≈ 1018eV. That is to say, that we do not expect to see UHECR primary energies above the GZK suppression limit from sources beyond 50-100 Mpc. The observation of these energies indicates possible UHECR sources within that horizon [52][54][55][56].

1A much smaller eﬀect on heavy nuclei takes place at energies above ≈ 30 × 106eV where the quasi-deuteron process begins to dominate interactions. In this process the photon interacts with one or two nucleons inside the nucleus with the extraction of two or more nucleons with the same results as photo-disintegration.

23 2.3. POSSIBLE SOURCES CHAPTER 2. PHYSICS

Figure 2.3: From Matthiae’s 2010 paper on the cosmic ray energy spectrum. At propagation distances over about ∼100 Mpc protons with energies > 4 × 1019 have lost their initial extreme energies due to GZK suppression [52].

2.3 Possible Sources

2.3.1 Active Galactic Nuclei

Active galactic nuclei (AGN) are currently understood to be galaxies containing a super massive black hole in their core that is in the process of accreting nearby matter. As material falls into the black hole conservation of angular momentum causes most of the matter to form into an accretion disk around the black hole with diﬀerential ﬂow causing fricative heating. Under certain theoretical scenarios a hot plasma near the disk is formed where charged particles become extremely energetic as a result of

24 2.3. SOURCES CHAPTER 2. PHYSICS

Figure 2.4: From Kachelrieß, Parizot, and Semikoz’s 2008 analysis of the GZK horizon. The left graph indicates 90% survival rate of UHECRs with an energy E in EeV as a function of radial distance R as measured in MpC for different models of interactions and environments to propagate through. The right graph indicates the percentage of UHECRs of a given energy E in eV that will successfully propagate the radial distance R in MpC. Both graphs indicate a GZK horizon of ∼50 Mpc for UHECRs at the GZK suppression energy limit [56]. diffusive shock acceleration. The intense rotation of the super massive black hole and its plasma accretion disk also generates very powerful magnetic fields. Protheroe and Szabo (1993) calculated that the energy acceleration rates of charged particles free falling into a super massive black hole were approximately

dE 1 ≈ 10−26b−1x L 2 U eV s−1 (2.12) dt 1 c rad

−1 with Lc being the luminosity of the black hole as measured in ergs s , x1 is an expression of the ratio of the shock radius versus the Schwarschild radius, and b is a factor describing how much larger the diffusion shock acceleration is than the rate of plasma diffusion within the accretion disk. Although it is possible for protons to achieve great energies in the accretion disk region near the black hole’s Schwarschild radius, it is unlikely for them to escape due to the intense magnetic fields present near the black hole’s surface. However, this region would then act like an intense high energy particle collider that would produce secondary particles including electron-

25 2.3. SOURCES CHAPTER 2. PHYSICS positron pair production and π − µ − e decay, ν’s also from π − µ − e decay, γ rays from πo decay, and free neutrons [51][57][58]. The free neutrons, being charge neutral, would be able to escape the black hole’s intense magnetic field, though only a small portion would be successful at this due to the high likelihood of undergoing pion photohadronic interactions in the particle dense regions around the AGN. Protheroe and Szabo (1993) calculated that neutrons with energy below about 5×1016 eV would stand a very high likelihood of escaping the photon dense regions of the AGN accretion disk, but these energies hardly represent UHECR energies observed on Earth. Free neutrons are not nuclear-stable and a relativistic neutron will decay into a proton after travelling a distance ' 2.8 × 104 (E/eV)cm. Observed AGN exhibit extremely long jets of material, reaching thousands of light-years in length, traveling at relativistic speeds traveling along the axis of their super massive black hole’s spin, being focused by the extremely strong magnetic fields produced by the AGN [59]. The protons produced by neutron decay near these AGN jets would be channeled into the flow of these jets and begin undergoing shock acceleration as they scatter off of the magnetically bound matter present in the jets. It is possible that these magnetically bound jets capture and accelerate these charged particles to UHECR level energies which then escape the jets at relativistic speeds, and may constitute a major portion of extra-galactic UHECRs. By this mechanism it is plausible that AGN may serve as a source of proton cosmic rays that are generated at energies many orders of magnitude above the GZK energy suppression limit. Figure 2.5 Shows NGC 4261, an AGN galaxy that is estimated to be 29.4 ± 2.6 Mpc, which is within the GZK Horizon [51][57][58].

26 2.3. SOURCES CHAPTER 2. PHYSICS

Figure 2.5: NGC 4261 taken from the Hubble Space Telescope is an example of a galaxy with an AGN. Relativistic jets ,larger than the luminous portion of the galaxy, being ejected by the AGN are clearly visible. Inset shows close up infrared image of the galaxy core. NGC 4261 is estimated to be 29.4 ± 2.6 Mpc from Earth. Image from NASA and STScI (1992) [60].

2.3.2 Starburst Galaxies

Starburst galaxies (SBG) are galaxies that are observed to be in a state of very active star formation, with observed star generation rates as much as ten times what is observed within the Milky Way. These high rates of massive stellar generation activity are believed to be the result of galaxies colliding with each other, or experiencing tidal forces from nearby more massive galaxies which causes giant molecular clouds within the SBG to collapse into large stellar nurseries. These galaxies are characterized by their large populations of very massive, young bright blue stars. As these stars are both very massive, and short lived they end their relatively short lives via extinction by supernova. The large population of these types of stars in SBGs suggests that the galaxies of this type may experience high rates of supernova events over a relatively short period of time. Since these star populations are quite young in terms on main sequence star life-spans, it’s not surprising that these SBGs also tend to have large

27 2.3. SOURCES CHAPTER 2. PHYSICS areas of giant molecular clouds and nebulae nearby, as these are understood to be the material from which stellar nurseries were formed. Population II stars like our Sun, that is stars that contain metals, are believed to be the result of supernova shock waves colliding with giant molecular clouds which cause over dense regions that can then collapse into new stellar nurseries rich in the chemical elements generated in the supernova explosion. It may be that the high concentration of supernova could be a generating source for UHECRs as these shock waves meet each other in these regions of interstellar medium providing a particle energy injection mechanism in the form of diﬀusive shock acceleration. This would be especially true in the even of typically rare, but extremely energetic star death scenarios like hypernovae, gamma-ray bursts, and magnetars which would be generated at higher rates than are observed in “normal” galaxies. These star death events would indicate a metal-rich composition to UHECRs origination from SBG. However the dominant contributor to UHECR production is likely to be ﬁrst order Fermi shock acceleration caused by galaxy sized “superwinds” fueled by the continuous cycle of star birth and death in the SBG. These winds drive gasses in the galaxy that causes high energy proton producing regions that dominate the SBG. Figure 2.6 exhibits M82, a SBG in the northern sky estimated to be only 3.5-3.6 Mpc from Earth [61][62][63][64][65].

2.3.3 Diﬀusive Shock Acceleration

Whether the source of extra-galactic UHECRs are AGNs, SBGs, or a combination of both, the principle mechanism of particle acceleration in each case is diffusive shock acceleration, or Fermi shock acceleration. Fermi first proposed the idea of diffusive shock acceleration in 1949 while hypothesising a possible origin for cosmic rays. The basic concept of this type of acceleration is that as two diffuse clouds of charged interstellar material meet each other particles on the converging front will scatter off of each other. The particles will tend to experience more “head-on” than “tail-on”

28 2.3. SOURCES CHAPTER 2. PHYSICS

Figure 2.6: Composite image of the SBG M82 utilizing visible, infrared, and x-ray images from the Hubble, Spitzer, and Chandra space telescopes respectively. M82 is estimated to be 3.5 − 3.8 Mpc from Earth. Image from The Veritas Collaboration (2009) [66]. collisions and will gain energy in the process of these collisions. Scattering will be largely dominated by magnetic field scattering in a plasma-like environment, with the result being that even particles moving towards the “upstream” direction of the colliding materials will eventually be accelerated “downstream” back into the shock region where further scattering will take place that again favors “head-on” scatters. The fully derived second-order Fermi acceleration diffuse matter transport equation is ∂f 1 ∂f 1 ∂ ∂f + U · ∇f = ∇ (κ∇f) + ∇ · Up + p2D (2.13) ∂t 3 ∂p p2 ∂p ∂p where U represents the velocity of the diffuse matter that the individual particles travel through, and D is a coefficient that describes the diffusion of the bulk material

29 2.3. SOURCES CHAPTER 2. PHYSICS in momentum space given as

1 D ∼ (4p)2 ν ∼ V 2p2/qk (2.14) 3 k all of which describes the random changes of momentum, p, for the isotropic portion, f, of a diffuse particle distribution function moving parallel through a magnetic field, kk [67][68][69][70]. The net result of this type of acceleration is that the energy of the affected particles are increased in a way that produces an energy spectrum that follows a power law akin to that observed in cosmic rays. Chapel and Mortlock give the rate of UHECR particle production per unit energy as

dN α − 1 E −α k = L (2.15) dtdE Emin Emin where L is the rate that a source outputs UHECRs above the minimum emission energy of Emin, and the spectral index, α is very near -2 which is akin to the observed cosmic ray differential energy spectrum index value [46]. Diffusive shock acceleration has been thoroughly observed throughout the Solar System with examples such as the Earth’s magnetic field colliding with solar wind, and the solar wind colliding with the inter-galactic medium at the Heliopause which was recently observed directly by the Voyager probes [71]. This implies that perhaps most low energy cosmic rays, those below the energy spectrum “knee”, are very likely produced by fairly common galactic astrophysical objects with supernovae and their remnants being dominant contributors. However, to account for the high energy cosmic rays, exceptionally large and energetic objects must be considered, such as AGN and SBG [67][68][69][70]. In the case of AGN or SBG as an extra galactic UHECR source, an observer would expect a certain anisotropy to become evident as UHECR events are con-

30 2.3. SOURCES CHAPTER 2. PHYSICS tinually recorded, as both types of extra galactic events correspond to galaxy sized accelerators. Both AGN and SBG correspond to galaxies that approximately trace the large scale distribution of matter. This distribution is known to be anisotropic out to scales of a few 100 Mpc or more. In 2018 the Pierre Auger Collaboration published results suggesting the emergence of anisotropy correlated to known AGN and SBG [64]. As the detection rate of UHECRs is so low ( ≈ 1 km−2century−1) very long periods of continuous observation is needed to generate that statistics needed to declare a definitive discovery. This is especially true as galactic magnetic fields will cause deflections to the UHECR paths in ways that are challenging to model which reduces the resolution of arrival directions, and so observational work must continue [64].

Figure 2.7: Sky mapping of anisotropy studies from Pierre Auger [left] and the Tele- scope Array [right]. Of particular interest is the ”Hot Spot” Observed by Telescope Array, which may correlate to M82, but is not observable by the Pierre Auger Ob- servatory. Figures from The Pierre Auger Collaboration (2012), and The Telescope Array Collaboration (2014) [72][73].

If UHECRs are being observed above the predicted GZK suppression limit, and these galaxy sized astrophysical objects were the source, then the catalog of potential source objects could not be more than a few hundred Mpc away. The Telescope Array Collaboration does in fact report an anisotropic “hot-spot” in their observed hemisphere in the direction of Ursa Major. This could correspond to the known SBG M82, which has been studied by The VERITAS Collabortion as a signiﬁcant source of high energy gamma rays. This underscores the need for UHECR observatories

31 2.3. SOURCES CHAPTER 2. PHYSICS to be able to reconstruct and resolve EAS arrival directions with precision. As the Telescope Array Collaboration is currently the only working UHECR observatory with the ability to make these reconstructions observing the Northern Hemisphere, members of The Pierre Auger Collaboration, which can only observe the Southern Hemisphere, are very eager to verify this Northern Hemisphere hot-spot observation (Figure 2.7). This spells out one of the major scientiﬁc goals of this thesis, that being the cross calibration and eventual side-by-side observation and reconstruction of EAS in the same part of the sky with both types of observatory instruments2 [58][62][63].

2.3.4 Exotic Collapsars

Detection of UHECRs with primary energies at or above the predicted GZK suppression limit, like Fly’s Eye’s 1991 observation of the “Oh-My-God-Particle”, and the Pierre Auger Observatory’s primary particle chemical composition reconstruction suggesting a heavy iron nuclei-like component to UHECRs near the GZK limit suggests that a portion of UHECR may be generated by exceptionally rare but highly energetic astrophysical objects within the galaxy, or at least nearer than the predicted GZK horizon[74]. The lead candidates for such an astrophysical object are young post super novae star cores, like white dwarves, neutron stars, pulsars, or magnetars, with spin periods shorter than 10 ms, and with extremely powerful surface magnetic fields on the order of 1012 − 1014 G. Stars that undergo neutron star generating supernovae generate iron as their final fusion product just before exploding, and so a very young neutron star will reside in a supernova remnant nebula rich in iron. Magnetohy- drodynamics (MHD) modeling show that the kinetic energy of the rapidly rotating magnetic field near the surface of the star can be efficiently converted into kinetic energy for the particles caught in the field [57]. An estimate of the energy of an accelerated ion in side the light cylinder, the distance around the spin axis of the star

2This will be covered in detail in Chapter 5

32 2.3. SOURCES CHAPTER 2. PHYSICS where rotational velocities approach the speed of light, given by Blasi, Epstein, and Olinto is

20 2 E ≈ 4 × 10 Z26B13Ω3keV (2.16)

Here Z26 ≡ Z/26 to specify the atomic number of interest, B13 represents the magnetic

ﬁeld in the region from the star’s surface to its light cylinder, and Ω3k ≡ Ω/3000 rad s−1 with Ω being the rotational velocity of the star. The rotational velocities and ﬁeld strengths described this can in principle lead to iron nuclei accelerating to energies in excess of 1020 eV. As the star loses energy and ‘spins down’ the energy of escaping iron nuclei will likewise drop, producing an estimated particle spectrum of

31 5.5 × 10 −1 N(E) = ξ GeV (2.17) B13E20Z26

Here ξ represents a eﬃciency factor for particles being accelerated at the light cylinder (<< 1) [64][75][76][77]. In principle, acceleration mechanisms for protons also apply to heavier nuclei. For example iron nuclei may be accelerated to UHECR energies by passing through the remnants of the star that existed prior to going supernova. However, if the remnant nebula is dense enough to accelerate ionized particles any iron nuclei accelerated may also risk scatter or spallation that would prevent further propagation. Blasi, et. al. (2000) demonstrated that this barrier would only be temporary as the column density of the remnant material would gradually become transparent to high energy iron nuclei over time as the supernova nebula dissipates as it continues to travel away from the pulsar core from the initial supernova explosive shock. Although the pulsar continues to spin down and lose energy during this wait for transparency, the time for transparency to occur is much shorter than the amount of time needed for a pulsar with the right initial conditions to lose energy to the point that it can no longer throw oﬀ high energy iron nuclei. Although these pulsars in principle could

33 2.4. EXTENSIVE AIR SHOWERS CHAPTER 2. PHYSICS act as point sources of UHECR generation, the iron nuclei are still subject to galactic magnetic field turbulence deflection which over long enough distances could obscure signs of anisotropy, even if the source is within our own galaxy. Observations that sufficiently conclude that the highest energy UHECRs are composed of iron nuclei could be explained by high spin, highly magnetic pulsars as a source for UHECRs [75][76][77].

2.4 Extensive Air Showers

UHECRs propagate through space as a single ionized particle that is essentially a bare atomic nucleus. This is known as the primary particle which can be described by a characteristic energy and chemical composition. Making direct measurements of cosmic ray primaries above 1014 eV is extremely challenging as particle flux rates are too rare to make a balloon-borne or spacecraft based detector practical. This limits direct measurement of cosmic rays to the low energy regime. However, in 1937 and 1938 Rossi and Auger3 independently discovered that spreading out multiple detectors at mountain top level and looking for time coincidence detections of muons and electrons between these detectors could reveal the signature of a high energy cosmic ray coming through the atmosphere. At sufficiently high enough energies these indirect detections could even be observed at sea level. This is the basis of modern cosmic ray studies [13][78][79]. Upon entering the Earth’s atmosphere the primary particle scatters off of molecules within the atmosphere. This scattering causes the primary cosmic ray particle to rapidly spallate and form a cascading series of daughter particles in a phenomenon known as an Extensive Air Shower (EAS), which is illustrated in Figure 2.8. These daughter particles rapidly undergo a chain of nuclear decays producing a shower of stable elementary particles, typically in the form of free electrons and muons, that

3In his publication of this discovery Pierre Auger would coin the term ‘Extensive Air Shower’

34 2.4. EAS CHAPTER 2. PHYSICS can be detected on the surface of the Earth. This process also excites molecules in the atmosphere causing them to emit florescent light, which can be observed at distances of many kilometers with telescopes due to the florescent light radiating isotropically. Due to EAS, the Earth’s atmosphere can act like a calorimeter that allows researchers to measure the characteristics of UHECR by observing the final particles that reach the ground, and the florescent light produced in the atmosphere [6][13].

Figure 2.8: A cartoon of an EAS from Semikoz and Dmitry (2010) illustrating the shape proﬁle of a typical EAS with UV ﬂorescence highlighted. Insert image illustrates the rapid particle spallation and decays that take place as the EAS develops through the atmosphere [80].

2.4.1 Composition

The altitude of these atmospheric interactions is described by a quantity known as X, which is a gauge of “atmospheric depth”. X is expressed in units of mass per unit area, g/cm2, and describes the surface area density of matter encountered by particles

35 2.4. EAS CHAPTER 2. PHYSICS

as they move from the vacuum of space into the denser depths of the Earth’s lower atmosphere. A higher X value represents a lower altitude as measured from the Earth’s surface, and corresponds to a region in which scattering will be more likely.

One observable parameter of interest for an EAS is Xmax, the depth at which atmospheric interactions are at their greatest prior to the cascade of daughter particles

decaying into lower energy leptons and photons. Xmax can be measured by observing the position of brightest ﬂuorescent light emission from multiple ﬂuorescence detecting telescopes as the shower develops in the atmosphere. For a proton, the scattering interaction cross-section raises with increasing energy, and so we expect the value of

Xmax to increase with energy for protons. The situation in reality is more complicated for heavier nuclei, but in the approximation that we can treat a nucleus with N nucleons as N nucleons each with /(E/N/) kinetic energy, we expect that the Xmax for a heavier nucleus will be, on average, smaller than that for a proton of the same

total kinetic energy. Thus observing Xmax it is possible to estimate whether the observed EAS was produced by a heavy, iron-like nuclei, or a light, proton-like one

(See Figure 2.9 for an example of Xmax reconstruction from the Pierre Auger data) [6][81].

Figure 2.9: A Pierre Auger Collaboration reconstruction of Xmax and rmsXmax showing a correlation between the estimated composition of cosmic ray primaries versus energy against various hadronic interaction models. Figure from Aloisio and Gazizov (2010) [82].

36 2.4. EAS CHAPTER 2. PHYSICS

Figure 2.10: From Pierre Auger and Telescope Array work group on composition’s proceedings on the UHECR composition working groups within PA and TA (2015). Composition is shown as a function of Xmax with PA’s results on the left, and TA’s on the right. Within the error bars reported by both collaborations the independent assessments of UHECR compositions may, or may not agree with each other [83]. A direct overlay of the two graphs is provided for ease of comparison.

In current experiments, chemical composition estimation is not measured directly on an event-by-event basis, but rather comes from the statistical analysis of large data sets comparing trends in Xmax as a function of energy. This analysis is dependant

37 2.4. EAS CHAPTER 2. PHYSICS

on the hadronic interaction model being used in the data analysis. As can be seen in Figures 2.9 and 2.10 the Pierre Auger Collaboration utilizes different hadronic interaction models, namily EPOS, QGSJettII, and Sibyll while the Telescope Array Collaboration utilizes Sibyll and other different versions of QGSJettII. These hadronic interaction models are derived from observational work at particle accelerator and particle collider labs and are frequently being updated to improve model fitting to real data. Though these models are used reliably in the energy regimes of particle beam experiments, it is not certain how accurate these models may be for the UHECR

4 energy regime . The interpretation of Xmax in different experiments is complicated by the use of different hadronic interaction models. Figure 2.10 shows what may be evidence for a discrepancy in the composition reconstructions made by the Pierre Auger and Telescope Array Collaborations. Both sets of data appear to agree to within each other’s error bars, but apparently do not agree on whether a trend towards iron like nuclei at higher primary energies is present. This possible discrepancy may be a result of differences in hadronic interaction models being used, systematic uncertainty differences between the different observatories, or could correlate to the actual differences in observed composition of UHECRs coming from different parts of the sky[81][82][83]. Concurrent to the writing of this thesis the issue of the differences between TA and PA reconstructions was addressed again at the 2018 Ultra High Energy Cosmic Ray Conference in Paris, France. As is shown in Figure 2.11, a more direct comparison of energy spectrum reconstruction between the two collaborations was made which limited the data being compared to only common declination bands, and also applied an energy scale correction of +5.2% and -5.2% to the Auger and TA data respectively. Differences between the energy spectras persisted despite these corrections. Resolving

4The current most powerful particle collider in the world is the LHC which in 2018 ﬁnished a date run that utilized a total beam energy of 13 × 1012 eV. This is on the order of 1/100,000 of the minimum energies of interest probed by both the Telescope Array and the Pierre Auger Observatories, and 1/10,000,000 of the GZK energy suppression limit.

38 2.4. EAS CHAPTER 2. PHYSICS this reconstructed energy dilemma is the central purpose of the cross-calibration and direct joint detection goals addressed in this thesis, and will be presented in detail in Chapter 5 [84].

Figure 2.11: From the 2018 UHECR2018 proceeding, by T.∼AbuZayyad, et al. (2019) showing the latest cross collaboration work towards identifying the differences in TA and PA UHECR EAS reconstructions. (Left) An overall correction of +5.2% (Auger) and -5.2% (TA) to the energy spectra in the common declination band. (Right) Even when restricting data to the common declination bands, and applying the energy scale corrections significant differences between TA and PA energy spectra persist [84].

Because composition measurements place strong constraints on UHECR source and propagation models, the UHECR differential energy spectrum, shown in Figure 1.1, exhibits flattening at “the ankle” which corresponds to energies near or above the GZK limit this suggests that a major contributor to UHECRs exists to produce cosmic ray populations almost exclusively at or above the GSK suppression energy limits. If these UHECRs are predominantly of proton composition then one might reasonably expect that they originated from galaxy sized diffusive shock accelerators, like AGN or SBG, on the order of hundreds of MPC away, as heavier nuclei type primaries would be less likely to successfully propagate through the CMBR background without spallating. If it is found that these ankle type UHECRs are predominantly iron nuclei, then it seems much less likely that these cosmic rays survived great extra- galactic propagation, and fast spinning, highly magnetic pulsars would seem to be the primary candidates for UHECR sources. The composition of UHECRs is an

39 2.4. EAS CHAPTER 2. PHYSICS extremely important scientific question to be resolved if the origins of UHECR is ever to be understood. The EAS provides a convenient method of observing cosmic rays and measuring their characteristics. The atmosphere acts like a large calorimeter into which a cosmic ray dissipates its energy in the form of the EAS, with the signature of fluorescent light produced, and the lepton footprint on the ground both being directly related to the primary energy and arrival direction of the cosmic ray particle. Therefore an observatory consisting of a large array of regularly placed surface detectors (SD) ringed with several florescence detector (FD) telescopes should be able to fully characterize an EAS’s primary energy, arrival direction, and gain a rough estimate of its primary’s composition. This is the method utilized by both The Telescope Array and Pierre Auger Observatories, and will be discussed in detail in Chapter 4.

40 Chapter 3

Methods of Detection

In this chapter mechanisms currently being used to observe extensive air showers (EAS) are introduced. This is not exhaustive, as there are other mechanisms for EAS detection currently being utilized, such as radar detection, not detailed here as they were not relevant to the dissertation work presented in this thesis. Each section describes a mechanism of detection in a broad way. Later chapters will describe in more detail the experimental instruments used to make the observations discussed in this thesis.

3.1 Photo Multiplier Tube

A critical piece of hardware used in nearly all methods of UHECR detection is the photo multiplier tube (PMT). A PMT is a device that converts a light signal into an electrical voltage pulse that can be read by electronics. PMTs can be extremely sensitive, producing a usable pulse from a signal that may have an intensity of only a few hundred photons, can be designed to operate at a particular wavelength band, and contribute very little random noise to their output signal, all with very fast response times. For these reasons a PMT is ideal for catching the faint and transitory light produced by cosmic rays as they either emit Cherenkov radiation in a medium, cause atmospheric air to ﬂuoresce, or stimulate a scintillating medium [85].

41 3.1. PMT CHAPTER 3. METHODS

The typical construction of a PMT is a glass vacuum tube that has a photosensitive layer coating its aperture end. This is the photocathode which reacts to incident photons landing on its surface by emitting low energy electrons. These electrons are too few and too low in energy to produce a useful signal, so they are passed through a series of dynodes which greatly amplify the signal. Once ampliﬁed these electrons produce a charged pulse signal which is stored at the PMT’s anode on the opposite side of the tube from the aperture. The signal is read out through the anode as an analog current pulse to be processed by electronics. Figure 3.1 shows a cross section of a typical PMT. This is only a general description of what a PMT is and how they work. A more detailed description of how the PMTs of each particular type of detector works, as well as their systematic characteristics, will be provided in the relevant sections of this thesis [85].

Figure 3.1: Cutaway of a PMT showing basic elements from the 1970 RCA Photo- multiplier Manual Technical Series PT-61 [86].

42 3.2. WATER CHERENKOV TANK CHAPTER 3. METHODS

3.2 Water Cherenkov Tank

A well proven method of radiation detection, in the context of cosmic ray studies, is by observing the light produced by Cherenkov radiation in a large volume of water. Cherenkov radiation is produced as a result of a charged particle moving with a velocity greater than the phase velocity of the medium it is traveling through. The theory of relativity states that a charge traveling uniformly through a vacuum will not radiate, and this is also true for a charge moving uniformly through a dielectric medium. However, if the index of refraction of a medium is greater than one, then a scenario exists in which a charge’s velocity may exceed the phase velocity of the medium. In this case the Li´enard-Wiechart potentials, normally expressed as

q φ = κR (3.1) qu A = cκR

are modiﬁed with κ = 1 − β cos θ becoming κ = 1 − βnr cos θ, with nr being the index of refraction of the medium. Under these conditions the κ will vanish when

−1 the angle θ is equivalent to cos θ = (nrβ) , and these potentials become infinite. Consequentially, a charge will release energy in the form of photon radiation in order to preserve a non-infinite potential [87]. This phenomena presents an exploitable opportunity for charged particle detection. A charged particle moving at relativistic speeds encountering a medium with an index of refraction greater than the one it is currently propagating through will emit Cherenkov light. A large tank of water will meet these conditions. If the tank is sufficiently light sealed, a PMT will be able to easily observe this light. Cherenkov radiation will beam along a Cherenkov cone, rather than radiate isotropically. This is a result of the charge particle’s velocity being greater than the local medium’s speed of light, c/nr. Light radiated by the traveling particle will fail to

43 3.2. WATER TANK CHAPTER 3. METHODS outpace the particle itself, and as a result a cone of propagation is formed in a geometry based on the local speed of light in the medium, c/nr, and the velocity of the particle, ν as is shown in Figure 3.2. Light will only propagate in a direction perpendicular to the surface of this cone, so a PMT outside of the Cherenkov light beam path would miss making a detection1. This can be remedied by coating the inside of the water tank with a highly reflective material so that the normally directional beamed light can be detected by a centrally mounted PMT as isotropically reflected light. With calibration this reflected light can be correlated to the intensity of the original radiated light [87]. The Cherenkov energy deposited into the water by EAS is calibrated by a vertical- equivalent muon (VEM). VEM is an artificial construction used as a standard unit of deposited energy in the water medium. It describes the energy that an ideal muon traveling perfectly vertically through the center of a water tank would deposit. In practice muons produced by EAS will only in extremely rare instances pass perfectly vertically through a tank, or near its center, or singularly. Muons often pass through water detectors at such a geometry as to cause either a considerably shorter than VEM path length, or much longer, and a water SD near the EAS core will likely experience numerous muons at once. As a water Cherenkov detector only gives a measure of total energy deposited in an instance, EAS reconstruction is based on particle density as measured in VEM. That is a light signal is measured and quantified in terms of a VEM density which is then converted to a VEM count based on the particular calibration and effective surface area of an SD. This particle density relates directly to a lateral distribution function which relates particle density to the primary energy and zenith angle of an EAS as measured from a certain radial distance to the shower core 2 [88].

1See Figure 3.2 2Lateral distribution functions are covered in detail in chapter 6

44 3.3. SCINTILLATION PANEL CHAPTER 3. METHODS

Figure 3.2: A Cherenkov cone illustrating the beaming eﬀect of the light. Photons radiate perpendicularly to the surface of the bow shock. Figure from Rauscher and Amoroso (2004) [89].

3.3 Scintillation Panel

A scintillator is a material that responds to the kinetic energy of a charged particle passing through it by rapidly emitting photons from electrons undergoing excitation in the material. Ideally this conversion will take place with high eﬃciency and the yield of light should relate linearly to the deposited energy. Other desirable characteristics of a scintillator medium are having a very short decay time of the emitted luminescence in order to generate fast signal pulses, the material should be transparent to the wavelength of light that it generates, and for PMT coupling the index of refraction should be akin to the index for glass [85]. Scintillators can be broadly categorized by their composition as either being organic or inorganic. Generally organics have a fast response time, but yield lower

45 3.3. SCINTILLATION CHAPTER 3. METHODS

intensities of light compared to inorganics, while inorganics also exhibit superior linear responses, but can have slower response times [85]. Calibration of the energy deposited into a scintillator is conducted by a minimum ionizing particle (MIP). MIP is conceptually similar to VEM, but unlike VEM which utilises a “standardized” muon particle as its basis MIP is based on the “stopping power” of a relativistic particle traveling through a medium. This stopping power represents the mean rate of energy loss of a relativistic particle and is expressed in the Bethe-Bloch equation:

dE Z 1 1 2m c2β2γ2T σ − = Kz2 ln e max − β2 − (3.2) dx A β2 2 I2 2

This describes the loss of energy through a travel distance in a material [MeV g−1cm2] as a function of the maximum kinetic energy which can be imparted to a free electron in a single collision, Tmax, with Z and A representing the absorber medium’s atomic

−1 2 number and atomic mass [g mol ] respectively, mec being the mass of an electron multiplied by the speed of light squared [0.510998918(44) MeV], β and γ are the

K standard relativistic kinematic variables, K is a constant with A being equivalent to 0.307075 MeV g−1cm2, and σ representing a density eﬀect correction to ionization energy loss [90]. Figure 3.3 shows how the stopping power of a charged particle changes as a function of momentum, given as βγ = p/Mc. In Figure 3.3 a positively charged muon traveling through solid copper is used as the model example. For any given species of particle and absorber the rate of energy lose increases with a decrease in velocity, with the most energy loss taking place at non relativistic velocities. At about βγ = 1 a minimum stopping power, or energy loss rate, is reached and over the next several decades the stopping power curve undergoes a gentle linear increase. This minimum is the regime at which most relativistic charged particles, like cosmic ray muons, experience most of their energy loss when passing through a material. These

46 3.3. SCINTILLATION CHAPTER 3. METHODS

particles are thus described as minimum ionizing particles, MIPs. For scintillation detection of cosmic ray particles MIPs are used for particle energy calibration since the particles interact with the detection medium in that regime, and lose the least amount of energy to medium absorption in their interactions. Due to the minimum energy loss it can be inferred that the amount of energy deposited in the scintillation interaction is more narrowly constrained to a given initial particle energy, making calibration more direct [85][90].

Figure 3.3: A graph from Bichsel, Groom, and Klein (2005) illustrating the stopping power of a positively charged muon through solid copper as a function of muon momentum. Minimum ionization is present at ∼ 5 βγ, which is where a particle passing through the absorbing material will lose the least amount of energy due to “stopping power”. This is the same region that EAS muons exist, and is the basis for calibrated energy deposited in a scintillator [90].

As with Cherenkov water tanks it is highly desirable to minimize the loss of signal light due to scattering within the detection medium. As was described in the opening paragraphs of this section, an ideal scintillator material will not absorb light at the wavelengths that it emits signal light. This property aids in signal light collection as a solid scintillator material may act as a waveguide and channel light within the

47 3.3. SCINTILLATION CHAPTER 3. METHODS material. A waveguide is a material or structure that confines waves by means of internal reflections to only one or two dimensions in order to prevent amplitude loss as the wave propagates. An relevant example of a waveguide would be a fiber optic cable in which electromagnetic waves are confined to the long axis of the fiber by virtue of Snell’s law:

sinθ1 n = 1 (3.3) sinθ2 n2 due to the angle of incident of light waves, θ1, encountering the internal side of the fiber being such that the ratio of indexes of refraction between the fiber medium and the external medium, n1 , produces an internal reflection. This allows light waves to n2 travel preferentially along the length of the fiber optic cable with a minimum of signal loss [91]. Scintillator panels built to the size necessary to conduct ultra high energy cosmic ray (UHECR) research, usually on the order of several square meters in area, will behave as waveguides but will have cross sectional areas too large to couple directly to PMTs which usually have aperture diameters of only a few cm. This could make coupling PMTs to large sized scintillator panels a prohibitive challenge, but modern waveguide materials allow for this to be accomplished. Typical large sized scintillator panels will have small channels cut at a shallow depth into their largest surface which allows lines of fiber optic cables, known as wavelength shifting fibers, to be laid into the scintillator material. These wavelength shifting fibers act as waveguides to gather light emitted inside the scintillator material and direct the light towards a PMT mounted outside of the scintillator assembly, while simultaneously shifting the wavelength on the signal light from the wavelength emitted from the scintillation material to a desired wavelength that optimizes the PMT performance as the result of attenuation of the light signal over the chosen length of the fiber. Both the PMT aperture and the fiber optic cables are made of glass, and so have little difference in

48 3.3. SCINTILLATION CHAPTER 3. METHODS their respective indexes of refraction. With a layer of optical grease, a thick gel-like material with wavelength transparency and index of refraction nearly identical to glass, placed between the gathered fiber optic cable and the PMT the lose of light signal can be minimized further. All current scintillator panel based detectors follow this type of architecture. Although some signal light is lost at each junction, from panel to wavelength shifting fibers to PMT, with careful calibration, usually executed with a light source of known intensity, this lose of signal can be compensated for. Signal loss minimization is further enhanced by sealing the scintillator material in a light proof material to block out errant signals, with the interior of the light proofing material having a highly reflective surface in order to allow as much of the signal light as possible to be caught by the fiber optic cables. The difference of calibrated energy units utilized in water Cherenkov and scintillator detection media and how this deposited energy is calibrated within the respective media comes from the nature of the detection material itself and the conventions by which it is measured. MIPs are utilized in the case of a solid “absorber”, such as a scintillator panel, to describe energy deposited by penetrating relativistic charged particles into the material, while for liquid water tank detectors VEM was chosen to be utilized in order to describe a model particle of known energy traveling an ideal path length within the detection medium to describe the energy that such a particle would deposit in the process. While MIPs come from detailed material science studies of solid materials encountering charged particles, and VEMs comes from an idealized model of energy deposition, both MIP and VEM are treated as calibration energies measured as quanta that are found to correlate to the peak event trace values found by the PMTs of the detectors, with the scintillator system measuring traces in volts and the water tank system measuring in analog-to-digital conversion (ADC) counts. As will be demonstrated in Chapter 6, a histogram of the peak measured volts observed by a scintillator system will, with a sufficiently large sample size, reveal a

49 3.4. FLUORESCENCE TELESCOPE CHAPTER 3. METHODS

“muon bump” indicating the voltage value corresponding to one MIP deposited in the material. A water Cherenkov system utilizing ADC counts, will likewise reveal a value that, once removed from a pedestal value identifying the “ADC floor” of a particular PMT channel, will reveal an ADC value corresponding to one VEM. Be- cause these different types of detection mediums are calibrated by different standards, and use different units in measuring event traces, it is not immediately possible to compare MIP values measured by a solid scintillator system to VEM values measured by a water Cherenkov system. As is discussed in Chapter 4, this presents a problem in directly comparing the TA and PA data sets, and motivates the dissertation work towards cross calibration.

3.4 Fluorescence Telescope

Akin to how a charged particle can induce light emission within a scintillator material, a charged particle can also induce ﬂuorescent light to emit from gasses in the atmosphere. This emission comes from atmospheric molecules, chieﬂy nitrogen, being excited or ionized by scattering with a relativistic charged particle. This is a result of Coulombic force interaction,

q q F = k 1 2 (3.4) (r) r2

causing the charged particle to pull on the electrons of molecules that it passes near [85]. In the case of ﬂuorescent light produced by EAS, the dominant form of light comes from electrons and/or positrons produced in the EAS scattering oﬀ of molecular ni-

+ trogen, N2 and N2 . Although some ionization occurs the vast majority of light comes from molecular excitation and results in UV light emission bands being generated from multiple modes of excitation between the wavelengths of 290 and 430 nm [92].

50 3.4. FLUORESCENCE CHAPTER 3. METHODS

In an ideal environment the light emitted can be directly calibrated to the energy of the primary particle, however in practice there is a quenching factor present caused by other molecules in the atmosphere interacting with the emitted photons. This quenching factor depends upon the particular atmospheric conditions present during the EAS event, and the total actual ﬂuorescent light yield can be represented by the following formula:

po 1 + 0 pair(λ,To) Yair(λ,p,T ) = Yair(337nm,po,To) · Iλ/I337 (po,To) · (3.5) 1 + p 0 q T Hλ(To) p (λ,To) air To Hλ(T )

Here Yair(337nm,po,To) is the absolute yield of the ﬂuorescent emissions of nitrogen molecules at their peak wavelength of 337nm in an ideal lab condition, with po and To being the laboratory pressure and temperature respectively, and Iλ is the wavelength dependant factor of ﬂuorescent light intensity as measured against the wavelength of absolute yield. The term r T H (T ) λ o (3.6) To Hλ (T ) encompasses the temperature-dependent scattering cross sections between excited nitrogen molecules and other general atmospheric air molecules. The quenching formula

0 is complicated further as pair is a reference pressure for dry air. With humidity the

0 term 1/pair becomes: 1 p 1 e 1 − h + (3.7) p0 p p0 p air H2O

0 with pH2O being the characteristic pressure for collision induced quenching with water vapor and ph is the partial pressure of water vapor in the atmosphere [85][93][94][95]. These quenching effects need to be accounted for in order to accurately calibrate any fluorescent light observations, and can be accomplished with an observatory weather station recording local atmospheric temperature, pressure, and humidity. Aerosol particles in the atmosphere also contribute an attenuation effect to observed

51 3.4. FLUORESCENCE CHAPTER 3. METHODS

ﬂuorescent light from photons scattering in a way that prevents them from being observed by a ﬂuorescence telescope and must be accounted for in data collection calibration. Attenuation caused by scattering is described by the formula:

dΩ I = I T T (1 + H.O.) (3.8) (λ,s) o(λ,s) mol(λ,s) aer(λ,s) 4π

with I(λ,s) being the light intensity observed by a ﬂuorescence telescope from the source intensity, Io(λ,s), after loss from atmospheric transmission coeﬃcients from atmosphere molecule and aerosol particles, Tmol(λ,s)Taer(λ,s). H.O. denotes higher order

dΩ corrections that are instrument dependant, and 4π represents the solid angle sub- tended by the telescope aperture as seen from the light source. Calibration of aerosol scattering effects is accomplished by use of a calibrated laser located near the center of the observatory where all fluorescent detectors can view it, and firing multiple laser beam pulses into the air at the beginning of an observation cycle in order to gauge the current attenuation effects. These setups are generally referred to as a central lasing facilities (CLF), and are present in both the Telescope Array and Pierre Auger Observatory which will be discussed in the following chapter [96]. Atmospheric fluorescence radiates isotropically which makes it a particularly effective means of observing cosmic rays, as a single fluorescence detector (FD) can have a very large field of view and be able to observe any EAS occurring anywhere within that field. Observing the fluorescent signature of an EAS as it travels through the air provides a direct measurement of the lateral distribution of the shower in terms of intensity of emitted light, and can be directly gauged to the primary energy of the cosmic ray. When multiple FDs are used, measuring the same fluorescence signature from different angles, the altitude at which maximum interaction, Xmax, occurs can be directly measured, and a geometric reconstruction of the arrival direction can be readily found. This makes FDs extremely useful for not only providing

52 3.5. ATMOSPHERIC CHERENKOV LIGHT CHAPTER 3. METHODS an independent means of measuring the primary energy and arrival direction of an EAS, but also provides a mechanism to estimate the composition of the cosmic ray primary3. While the atmospheric FD method of UHECR detection seems ideal for gauging primary energy, composition, and arrival direction, it is unfortunately very limited in that an FD observatory can only operate on moonless nights with clear skies which corresponds to only a 10 % duty cycle [93][94][95].

3.5 Atmospheric Cherenkov Light

Cherenkov light can also be produced in the atmosphere by EAS particles by the same process described in Section 3.2. Unlike atmospheric fluorescence light which radiates isotropically, atmospheric Cherenkov light is beamed in a relatively tight pattern. This beamed light from the propagating showers particles travels along a wave front with curved geometry produced by the combined Cherenkov cones of each individual particle4. The result of these two effects is that EAS induced atmospheric Cherenkov light is not detectable unless an instrument is within a few hundred meters of the shower’s core, and is pointing to within a few degrees towards the arrival direction of the cosmic ray primary. The light gathering capabilities of a PMT based Cherenkov detector can have its viewing angle increased by fitting the aperture with a Winston cone, though the observed portion of the sky would still be dramatically less than that of an FD telescope. This makes an observatory based on atmospheric Cherenkov light detectors less desirable than other detection options as it would require thousands of instruments arrayed with about 200 meter spacing in order to cover a sufficiently large surface area to observe the chance UHECR passing through a given part of the sky. An array of such instruments could measure the varying intensity and arrival time of the light at different locations and make a fairly accurate reconstruction of

3See Section 2.4.1 4See Fig 3.2

53 3.5. ATMOSPHERIC CHERENKOV CHAPTER 3. METHODS

the EAS’s primary energy, arrival direction, and Xmax. Atmospheric Cherenkov light would also experience quenching and scattering attenuation effects similar to those described in Section 3.4. Similar formulas as are presented in Section 3.4 would be used to account for these light loss effects, though different coefficients would be used to account for the difference in photon wavelength, and instrumentation [1][97].

54 Chapter 4

Current UHECR Observatories

4.1 Pierre Auger Observatory

4.1.1 Pierre Auger South

The Pierre Auger Observatory (PAO) is a hybrid cosmic ray observatory located near Malarqüe,Argentina1 consisting of 1660 surface detectors (SD), and four fluorescence detector (FD) observatories covering an area of about 3000 km2 (Figure 4.1). The area is owned largely by cattle ranchers who utilize the land for grazing, with a portion of the observatory built on land owned by the Province of Mendoza or the Argentina Natural Resources Administration. PAO was originally intended to be the southern hemisphere component of a duel observatory, but is currently the sole entirety of the the observatory. Data collection began in 2004, with the full observatory being completed and put into operation in 2008. The observatory is designed to observe extensive air showers (EAS) at or above 1017 eV in order to reconstruct information on the composition and arrival direction of the cosmic ray primaries with the scientific goal of elucidating on the possible origins of ultra high energy cosmic rays (UHECR) [98][99][100]. The Pierre Auger (PA) SD is a large polyethylene water tank that uses three Photonis XP1805/D1 photo multiplier tubes (PMTs) to observe the Cherenkov light

1Near 35.2o S, 69.2o W

55 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES

Figure 4.1: Map view of PAO showing approximate location of SDs as red dots, and location and ﬁelds of view of the FDs. Image from the Pierre Auger Collaboration (2015) [99].

produced by relativistic charged particles traveling through the water. Each SD cov- ers a surface area of approximately 6.6 m2 and stands about 1.5 m high, holding close to 12,000 L of quadruple-filtered water (Figure 4.2). The water needs to be highly purified in order to prevent flora from growing inside the tank and inhibiting PMT performance. The water itself is contained within a Tyvek® coated liner, which provides a highly reflective surface to ensure observable light is not lost due to undesired absorption by the tank walls. Each SD is spaced 1.5 km from its nearest neighbor in a regular triangular pattern so that the spacing between all nearest neighbor SDs is identical. The SDs are sensitive to the charged particle component of an EAS, composed mostly of muons and electrons, and have a 100% duty cycle since they can operate under any weather condition, day or night [98][99][100]. Each PMT provides a high gain and low gain output, with the high gain feed being utilized during normal data gathering operations and the lo gain being used

56 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES

Figure 4.2: A PAO SD in the field with major components highlighted. Image from the Pierre Auger Collaboration (2015) [99]. during rare, near shower core high energy events that would produce signals that would saturate the high gain PMT feed. Noise reduction is handled by a 5-pole Bessel filter with -3 dB attenuation at 20 MHz. The two PMT channels use AD9203 10-bit 40 MHz semi-flash analog to digital converters (ADCs) operating in the range 0-2 V in order to digitize the signals for data collection. Calibration of the PMTs was executed via an LED pulse flasher mounted inside of a small clear window in the SD water liner. The VEM calibration of the PA SDs is set to correspond to a minimum threshold energy of about 240 MeV [100]. Each SD is controlled by a single board computer system known as the SBC that is built around IBM PowerPC 403 GCX 80 MHz CPU motherboard with 32 MB of DRAM. The SBC monitors diagnostic and maintenance data for each SD, and collects voltage pulses from the three PMTs. The electronics of the PAO SD require 24 VDC to operate which is provided by solar panels and a pair of 12 VDC batteries mounted in a parallel circuit. The voltage readouts of each PMT is compared against

57 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES a threshold voltage to determine if a local trigger has occurred. Local triggering data is time stamped, via a GPS synchronized common timing protocol and an onboard clock to produce a 10 ns root mean square detection time stamping precision, and radioed to a nearby concentrator node, which consolidates a set of SDs’ data before relaying it to a Base Station Unit (BSU), which further compiles the sets of data before passing them along to a series of microwave relay towers to the observatory DAQ. If a the observatory’s Central Data Acquisition Server (CDAS) observes multiple adjacent SDs reporting triggers within a coincidence window, then all participating stations are requested, via their BSU, to transmit their local data logs to the BSU. The BSU compiles this data and relays it to the observatory’s central CDAS. The entire global trigger data collection process may take as long as two minutes. In order to mitigate data loss over this time the PAO comms system is designed to make automatic repeat requests (ARQ) where upon the detection of incomplete or corrupt data packets individual stations may be asked to resend the last trigger data packet that they attempted to transmit. In the event that the data cannot be retrieved after seven attempts, an ARQ7, that data is dropped. The incidence of ARQ7s is monitored as a metric of observatory health and typically occur at rates so small that a nearly error-free data stream is maintained [98][99][100][101]. In addition to the main SD array, a smaller “Infill” array is located in the north- west quadrant of PAO. The Infill consists of 61 SDs spaced out at only 750 m to their nearest neighbor in a triangular grid pattern, covering 23.5 km2. the purpose of the Infill is to provide low energy shower observations, with an threshold of ∼ 1017 eV, which is used for EAS observations nearer “the knee” of the cosmic ray energy spectrum, and for tasks requiring calibration or prototyping new equipment [98][99]. The four FD observatories ring the outside of the SD array and face towards the center of the array. Each FD observatory has six telescopes designed to look for the UV fluorescent light produced by an EAS exciting nitrogen gas in the atmosphere in

58 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES

Figure 4.3: A PAO FD with shutters open showing telescope apertures. Image from the Pierre Auger Collaboration (2015) [99].

Figure 4.4: A cutaway schematic of an FD telescope with major components highlighted. Image from the Pierre Auger Collaboration (2015) [99].

59 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES the 300-400 nm wavelength range. This UV light radiates isotropically which allows these sufficiently sensitive telescopes with wide fields of few to detect it from multi kilometer ranges. The FD telescopes use a 22x20 sized cluster of XP3062 Photonis PMTs fitted with Winston cone light collectors and UV filters that produce a non- imaging trace of the fluorescent light in terms of intensity and time on a 2D plane. Figures 4.3 and 4.4 illustrate the exterior and interior of the PA FD observatories. Calibration of the telescopes is conducted at the beginning of each observation session by means of a NIST UV LED flasher, and atmospheric lasing from the PAO CLF and the eXtreme laser facility (XLF). As the telescopes can only operate on clear nights without the moon being visible, the FDs have a duty cycle that makes up only about 10% of the total observation time. The four FDs observing the same EAS simultaneously allows for observers to make a 3D reconstruction of the EAS path in the sky as a function of intensity and time. This reconstruction provides a calibrated measurement of the primary energy, the Xmax, and the arrival direction of a shower [98][99][100]. In addition to the 24 main FD telescopes, three additional telescopes observe the area directly over the Infill array. As the telescopes can only operate on clear nights without the moon being visible, the FDs have a duty cycle that makes up only about 10% of the total observation time [98][99][100].

4.1.2 Research and Development Array

As part of the planned but never built Pierre Auger North Observatory the Research and Development Array (RDA) was intended to be used as a preliminary test bed to prototype new conﬁgurations of PMTs in the water tank SDs, new types of computer control system for the autonomous collection of data and SD diagnostics known as the Local Station Controller (LSC), and a new means of data retrieval via CANBUS protocol (Figure 4.5).

60 4.1. PAO CHAPTER 4. CURRENT OBSERVATORIES

The RDA SD is nearly identical to the PAO SD, also having a surface area of approximately 6.6 m2 and stand about 1.5m tall, and holding close to 12,000 L of quadruple-filtered water. Each RDA SD used only one Photonis XP1805/D1 PMT, the same type used by PAO, instead of the three used by PAO, in order to reduce the cost of each SD without compromising data collection capability. This configuration along with the new LSC electronics were intended to reduce the distance from an EAS core at which individual SDs would saturate. The CANBUS protocol data retrieval was intended to establish a peer-to-peer communications system that was hoped would limit the amount of total hardware infrastructure required for data collection as it would allow each SD to act as a radio relay instead of relying on radio substations for data consolidation. Unlike the electronics on the PAO SDs the RDA SDs only require 12 VDC to operate, which dramatically reduces the necessary solar panel and battery infrastructure [102]. The RDA consisted of 10 SDs controlled by 10 LSCs, with an additional 10 LSCs configured for radio relay communication work only. The RDA array was located near Lamar, Colorado with the SDs setup in an ”L” shape configuration with ir- regular distances between each SD. This was intended to allow the RDA to observe coincidence triggers at a variety of different primary energy thresholds which would allow researchers to better gauge the performance of the instruments [102]. By the time the RDA project came to an end in late 2013 the CANBUS peer-to- peer communication system had not been completed. As a result all data collected by each SD had to be stored locally at each station with a terabyte storage sized flash drive. This made later efforts to reconstruct RDA data challenging [104]. The hardware for the RDA has since been moved to the Telescope Array’s Cosmic Ray Center (CRC) in Delta, Utah, with the exception being the 10 LSC radio communication

61 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES

Figure 4.5: Main PCB of the LSC with major components highlighted. From Guglielmi, Courty, Colonges, and Dufour (2014) [103]. only units being located at CWRU in order to serve as test units for a new radio communication scheme. This hardware is the basis for the upcoming Micro Array2.

4.2 Telescope Array

The Telescope Array (TA) is a hybrid cosmic ray observatory near the town of Delta in the western desert of Utah3 at an altitude of 1400m above sea level. The vast majority of the observatory is built on land managed by the US Bureau of Land Management (BLM). The TA observatory can be considered a successor to the HiRes and AGASA experiments as it utilizes both ﬂuorescence detection and surface array detection to observe cosmic rays with hardware modeled on that used by these predecessor experiments. TA is designed to observe EAS with primary energies at and above 1017.5eV,

2See: Section 5.2 3Near 39.3o N, 112.9o W

62 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES speciﬁcally at energies near the Greisen-Zatsepin-Kuzmin (GZK) suppression limit, with the goal of determining the origins of the UHECRs [100][105][106].

Figure 4.6: Layout of the telescope array depicting the three sub-array divisions covered by the three FDs and three communication towers. Figure from The Telescope Array Collaboration (2012) [105].

The observatory’s surface array consists of 507 SDs arranged in a north-east running square grid, with a spacing of 1.2km between nearest neighbors covering an area of about 700km2 (Figure 4.6). The SD array operates at full efficiency for primary EAS energies of ∼ 10 × 1018 eV or greater, and arrival direction zenith angles <45o. Like PA, TA includes an infill array known as the Telescope Array Low Energy Ex- tension (TALE). TALE consists of 40 SDs at 400 m spacing, and 60 SDs at 600 m spacing giving energy thresholds of ∼ 1018 eV and ∼ 1016.5 eV respectively. TALE serves a mission nearly identical to that of PA’s infill. As of this writing the Telescope Array is currently planning a major upgrade that will expand its SD array surface

63 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES

area to approximately four times its current size which will dramatically increase its rate of UHECR detection [100][105][106]. Each SD has a surface area of 3m2, and holds two layers of 1.2cm thick polyvinyl toluene scintillator panels layered over top of each other with two 1.5m x 1.0m scin-

tillator panels per layer. Each panel is wrapped in Tyvek®, and uses 104 wavelength shifting fibers to wave guide signal light towards the aperture of an Electron Tubes 9124SA PMT, with one PMT monitoring each scintillator panel layer (Figure 4.7 and 4.8). The two scintillator layers are separated by a sheet of 1mm thick stainless steel, and contain an LED flasher used for calibrating the PMTs. The entire scintillator/PMT/LED assembly is housed inside of a box made of 1.5mm stainless steel [100][105]. The SD stations are powered by a solar panel and battery system, and have a support structure constructed from steel with the scintillator panel boxes housed inside of a box made of 1.2mm thick steel in order to protect the panels from the elements and excessive heating during the daytime. Unlike the PAO SDs, which interprets local triggers in terms of VEM, the TA SDs interpret local triggers in MIPs4. The entire surface array is divided into three sub-arrays, shown in Figure 4.6, with each SD within the sub-arrays relaying data in real time via a wireless LAN modem to their respective sub-array’s communication towers located at Smelter Knolls, Black Rock, and Long Ridge locations5 [100][105]. Unlike PA, the TA PMTs utilize only one channel output which digitizes its signal with a 50 MHz 12-bit Analog Devices AD9235 flash analog digital converter. The SD electronics are all housed inside of the station’s battery box and utilize a 266 MHz Renesas SH-4 processor motherboard running a firmware operating system, with digitized PMT signals being received and processed by a Xilinx SPARTAN 3

4A discussion on why the PA and TA systems use diﬀerent calibrated energy units is discussed in Chapter 3. 5See Figure 4.6

64 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES

FPGA. The TA SDs, akin to the PA SDs, utilize a combination of GPS common timing protocol and on board 50 MHz clocks to time stamp data with a reported precision of 20 ns. The MIP calibration of the TA scintillators is calculated to have a muon detection threshold of 300 MeV from the de-excitation of π orbitals in the detection medium, with deposited energy peaking at around 2 MeV [100][105].

Figure 4.7: A TA SD with major features highlighted. A communication tower is visible atop a mesa in the background. Image from The Telescope Array Collaboration (2012) [105].

The FD component of the observatory is facilitated by three ﬂuorescence telescope observatories that ring the entire SD array on top of nearby mesas. The Middle Drum FD site was originally part of the HiRes-I experiment which provides a direct comparison of the energy spectrums obtained by the HiRes experiment and new data collected by TA. The TA FDs are very similar to those of PAO with a notable diﬀerence being that the TA FD telescopes utilize only 256 PMTs instead of the 440 used by PA. The remoteness of the site, along with its aridness allows for the FDs

65 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES

Figure 4.8: A Cutaway cartoon of a TA SD with internal features highlighted. Image from The Telescope Array Collaboration (2012) [105]. to operate with an optimum duty cycle due to the low light pollution, scarce cloud cover and low humidity that the site provides. The high altitude of the site allows for the FDs to observe EAS nearer to their Xmax altitude with less atmosphere between the Xmax location and the FD sites. Like PA, the TA FDs calibrate their telescopes at the beginning of each observation session with a combination of UV LED flashers and CLF atmospheric lasing [100][105]. While both Pierre Auger (PA) and the Telescope Array (TA) utilize similar hybrid observatory architecture there are significant differences in their hardware, and they have both published observations that may or may not come to similar conclusions. One place where this is seen is in the two collaborations estimates on UHECR chemical composition. As is shown in Figure 2.9 the UHECR chemical compositions published by PA and TA may both indicate that at relatively lower energies UHECRs

66 4.2. TELESCOPE ARRAY CHAPTER 4. CURRENT OBSERVATORIES

Item PAO TA Station Geometry cylindrical rectangular cuboid Dimensions r = 1.8 m, h = 1.2 m l = 2 m, w = 1.5 m, h = 0.012m Detector medium Ultra pure water (>15MΩcm) Polyvinyl toluene: C9H10 Light sensor PMT Photonis XP1805/D1 PMT Electron Tubes 9124SA Time Sync Protocol GPS on the order of 10s of ns rms GPS 20 ns Calibration unit VEM MIP Observed Hemisphere South North Mean altitude (msl) 1400 m 1400 m Wireless bandwidth 1100 b/s 11 MB/s Grid Geometry triangular square SD array separation 1500 m 1200 m SD array area 3000 km2 700 km2 SD array exposure 5500 km2 sr yr 890 km2 sr yr SD array efficiency 100% for E > 3 EeV 100% for E > 10 EeV Infill separation 750 m 400 m, 600 m Upgrade plans Auger prime TAx4 Table 4.1: A comparison and contrast of the Pierre Auger Observatory and Telescope Array [100]. are dominated by protons and gradually shift towards heavier species of particles as energy increases, or it could be inferred from the TA graph that the compositions remains more-or-less consistently proton-like at all reported energies. Both conclusions appear to be valid based on the reported error bars of the two graphs. It is also the case that the PA and TA all-particle cosmic ray spectra agree up to ≈ 50 × 1018 eV, but the results begin to diverge at higher energies [107]. This discrepancy underscores the main goal of this dissertations research; which is to rectify the differences of observational and reconstruction methods, and to establish the groundwork necessary to begin making direct side-by-side observations and reconstructions with the hardware and techniques utilized by both observatories.

67 Chapter 5

Pierre Auger at the Telescope Array

With The Pierre Auger North Observatory being canceled, the Pierre Auger Col- laboration was faced with the handicap of only being able to observe the southern hemisphere of the sky. Total sky coverage is highly desirable as it increases the statistics of UHECR observations, and if an anisotropy that correlates to astrophysical objects exists it is important to allow no blind spots in observation. This is especially important when considering sources that are distributed across the sky and/or anisotropy on very large angular scales. The TA observatory does observe the northern hemisphere and so a partnership between the two collaborations could allow for the entire sky to be effectively observed by combining data sets. However, this solution has its own problems as both observatories use different hardware for measuring EAS, and are each sensitive to different particle components of an EAS, and use different particle interaction models for their data reconstruction. For these reasons a simple direct comparison of data sets is not practical. As was discussed in Section 2.2 concerning the chemical composition of UHE- CRs, the PA and TA collaborations have published findings on the composition of UHECRs that may not be in agreement with one another. Within the error bars of data reported by both groups, PA analysis seemed to indicate UHECR compositions

68 5.1. AUGER@TA PHASE I: PA@TA AT THE CLF CHAPTER 5. PA@TA trending towards iron-like nuclei, while the TA analysis straddled a conclusion between proton-like or iron-like compositions. It is also the case that the PA and TA all-particle cosmic ray spectra agree up to ≈ 50 × 1018 eV, but the results begin to diverge at higher energies [107]. This could be the result of water Cherenkov detectors being more sensitive to the muon component of EAS, while scintillator panels are generally more sensitive to the electron component, the result of the two collaborations utilizing different hadronic interaction models or other factors in their composition reconstruction methods, or may be the result of the two observatories witnessing different astrophysical objects. To properly interpret data from both experiments we need to cross calibrate the instrument responses between TA and PA type SDs. In order to overcome this challenge, a program began in 2014 to relocate the RDA SDs from Colorado to Utah where they would be utilized within the TA observatory to make direct detection of EAS alongside the TA SD array. This project is called “Auger@TA”, or “PA@TA”, and involves a workgroup composed of a subset of collaborators from both the PA and TA collaborations, including the group at CWRU. The Auger@TA effort can be broken down into two phases of operation. Phase I being the co-location and cross calibration of both PA and TA SDs at the TA CLF, and Phase II being the deployment and operation of a PA type SD array, the Micro Array, inside of the TA SD array. As of this writing Phase I is largely complete with Phase II just beginning. The Table 5.1 outlines the two phases [107].

5.1 Auger@TA Phase I: PA@TA at the CLF

Phase I of Pierre Auger at the Telescope Array (PA@TA) began by installing a PAO type SD, henceforth referred to as a PA South Type, or PAS SD, within the footprint of the TA CLF in the center of the TA observatory. This was expanded soon after to have an RDA type SD, henceforth referred to as PA North Type, or PAN SD, installed

69 5.1. AUGER@TA PHASE I: PA@TA AT THE CLF CHAPTER 5. PA@TA

1) Deployed Auger South and North SDs into TA at CLF. 2) Veriﬁed hardware level triggers between TA and PA doublet. Phase I 3) Veriﬁed integrated signals of Auger doublet. 4) Translated between TA (MIP) and Auger (VEM) signals. 1) Deployment of 6 Auger SDs into an independent ‘Micro Array Phase II within TA. 2) Dual reconstruction with PA and TA SD signals. 3) Separate reconstruction using PA and TA SD signals (allows for cross-check of reconstruction methods).

Table 5.1: Phase I represents the task of cross calibration. Phase II represents direct detection. Phase I is by and large complete, with Phase II prepared and Part 1 beginning in 2019 [107]. immediately adjacent to the South Type SD. This created a detector doublet with the pair of SDs seeing nearly identical parts of the same EAS. In this way a direct comparison of the performance of the North and South type SDs could be made, which was critical for the success of the second phase of the PA@TA operation, the construction of an all North Type Micro Array1. Phase I construction and initiation of operations, along with the initial prepping and staging of Phase II was conducted over a series of ﬁeld expeditions to the Cosmic Ray Center in Delta Utah, and the Telescope Array Observatory by members of the Case Western Reserve University and Colorado School of Mines working group, with enthusiastic cooperation by members of the Telescope Array Collaboration, between Autumn 2014 and Summer 20182. Data from the PA SD doublet was piped in real time to a data acquisition (DAQ) system built by the HEA group from CWRU within the TA CLF building. A cartoon showing the data ﬂow of the PA doublet, as well as that of a specially located TS SD, is shown in Figure 5.1. This setup utilizes a custom built electronics and computer

1See Section 5.4 2For details about the installation of this doublet, see Sean Quinn’s 2017 Thesis: Characterizing Arrival Direction Probabilities of Ultra High Energy Cosmic Rays with the Pierre Auger Observatory and Progress Toward an In-situ Cross-calibration of Auger and Telescope Array Surface Detector Stations.

70 5.1. AUGER@TA PHASE I: PA@TA AT THE CLF CHAPTER 5. PA@TA package named TICTAC that requests and receives local triggering data from the two PA stations, along with time stamp and Picoscope3 local trigger data from a TA SD located near the doublet, as well as data from the TA Central DAQ reporting global trigger time stamps for another TA SD located at the CLF. This data is compiled and relayed to PA@TA work group members via an internet port made available by the TA collaboration at the CLF. This entire system is powered independently of the CLF’s organic power system via a small cluster of 12 VDC photovoltaic panels and 12 VDC deep cycle batteries wired in parallel for increased capacity [100].

Figure 5.1: A cartoon outlining the data ﬂow of the CLF DAQ from the TA and PA SDs at the site. Cartoon from Quinn, et al. (2017) [108].

To facilitate direct comparison of the two PA type SDs to the TA SDs a working group within the TA Collaboration relocated two spare TA SDs to the TA CLF within a few dozen meters of the PA SD doublet. Due to limitations in the TA SD ﬁrmware it was not possible to allow these two TA SDs to participate in global triggering with the rest of the TA SD array and to pipe that data in real time to the local CLF DAQ. This limitation was the reason for TA providing two TA SDs, and was overcome by

3See Capter 5

71 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA conﬁguring one TA SD, known as TAG for TA Global, to listen to the entire TA SD array and report the time stamps of global triggers that took place within the vicinity of the CLF while not participating in global triggers itself, and the second TA SD, known as TAL for TA Local, to operate normally as a standalone station recording local triggers and sending them, with a time stamp, to the same DAQ used by the two PA SDs. Initially TAL only provided time stamps of local triggering events and the CLF DAQ was blind to the actual PMT voltage intensities and pulse widths of the two PMTs observing the scintillator panels. This limitation was remedied in early 2018 by installing a new triggering and data acquisition systems known as the PicoScope to replace the TA designed pulse reader, which allowed the CLF DAQ to monitor TAL’s pulse strength directly and in real time4.

5.2 Auger@TA Phase II: The Micro Array

Phase II of PA@TA will be the construction of a small array of PA type SDs inside of the TA SD array for the purpose of direct detection of identical EAS, known as the Micro Array. The Micro Array will consist of six PA North Type SDs located inside of the TA SD array. Each SD will be run by its own LSC measuring local triggers from a single large PMT. These SDs will use oﬀ-the-shelf radio communication equipment to relay their data to a central DAQ computer which will compile all of the stations’ data for future analysis5. The ultimate goal of the Micro Array is to collocate PA type SDs with TA type SDs within the TA observatory in order to provide a fully independent observation and reconstruction of EAS recorded simultaneously by both types of SDs. The Micro Array EAS reconstruction will be conducted independently from the TA reconstruction in order to avoid either group from biasing the other. This will allow for direct comparison of EAS reconstruction methods, and may determine if diﬀerences

4See Chapter 6 5See Section 5.2.1

72 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA in published results from the Pierre Auger Collaboration and the Telescope Array Collaboration are the result of instrumentation differences, reconstruction method differences, or the observance of different and unique astrophysical objects resulting from monitoring different hemispheres of the sky. The current proposed plan for the Micro Array is to collocate six PA north type SDs adjacent to six TA SDs within the TA observatory east of the CLF and North of the Black Rock Mesa (BRM) communication tower site. Each Micro Array SD will be placed, by helicopter, within a few dozen meters of its partner TA SD. Placing each PA SD directly next to its partner TA SD is not feasible in all cases as a water truck must be able to access each PA SD in order to fill it with purified water as its detection medium. Due to BLM land access limitations this means that each PA SD must be within a few dozen meters of an accessible road or trail in order to be reached by water hoses while ensuring that water spillage is minimized. The currently planned scheduled will begin in Spring of 2019 with two SDs being deployed and put into operation as a test of deployment procedures and to allow time for the hardware to be verified with a period of data collection. Once this is completed the remainder of the stations will be deployed and integrated into the Micro Array. Based on simulation work detailed in Chapter 8, this planned design for the Micro Array should produce an energy detection threshold of ≈ 1 × 1016 eV, with a detection rate of an average of about 2 cosmic rays at or above that threshold per day, with detection efficiency approaching 100% at primary energies of about 3 × 1017 eV [109]. As of this writing a second configuration of the Micro Array has begun to be considered by the Micro Array work group with would utilize seven PA SDs arranged in a triangular pattern with 1.5 km spacing identical to what is used at the PAO. The power of the simulation work outlined in Chapter 8 is such that even with only hours notice new predictions for this seven SD configuration could rapidly and reliably be generated. The seven SD Micro Array configuration should produce an

73 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA energy detection threshold of ≈ 1.5 × 1016 eV, with a detection rate of an average of about 1.5 cosmic rays at or above that threshold per day, with detection eﬃciency approaching 100% at primary energies of about 2 × 1017 eV [109].

Figure 5.2: The proposed Micro Array SD locations for the initial array conﬁguration. Visible on this chart is the location of the TA CLF to the extreme west, and the BRM communication station to the south by south-east, and the BRM FD site to the extreme south-east. The spacing between each SD is approximately 1.2km [110].

5.2.1 Communications System

Due to the distances involved, the harshness of the environment, and the lack of available infrastructure at the site, it is not practical to hard-wire each SD to the observatory’s DAQ. Therefore it is necessary to utilize some form of wireless communications system to allow data to flow from each SD to the DAQ, and for the DAQ to occasionally send special commands to each Micro Array SD as needed. Here the development of an effective and ready-to-deploy wireless communication system for the Micro Array based on commercially available “off the shelf” solution to radio

74 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA communications is described. This radio communication system is centered around the Digi Xbee RF Modem.

Figure 5.3: A Digi Xbee RF Modem. This model was chosen due to its 1Watt output at the desired 900MHz bandwidth, and industry grade structural robustness.

The Xbee was chosen as it operates in the 900MHz RF bandwidth already being utilized by the PAO, showing its proven effectiveness, and did not require special FCC licensing in order to operate. With a power output of one Watt it was determined that a hi-gain Yagi-type antenna would be able to transmit and receive signals at the ranges anticipated to be needed for the Micro Array with little to no data loss. The Digi company itself provided verbose documentation for all of their products, and large Internet communities of Digi product users provide ample support for implementation and troubleshooting. The Xbee modems come with programmable firmware that handles many complex radio network functions, like station hopping, repeat transmissions until confirmation of reception is obtained, and encryption. The modem hardware is also rated to tolerate the temperature and other relevant mechan- ical stress factors that will be present in the field of deployment. For these reasons the Digi Xbee RF modem was determined to be an ideal candidate for the Micro Array’s radio communication system. A table of the Xbee modem specs is provided in Appendix B [111].

75 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA

The Digi Xbee GUI, XCTU, is the proprietary software that allows for program- ming of the firmware of individual modems. Via a serial port the XCTU reads and writes to the modem unit, downloading the latest firmware versions from the internet and installing them directly to the device. The user can then access the firmware directly and manipulate certain parameters within it. These include the option to encrypt transmissions and set an encryption key, the option to request confirmation of a radio packet reception, to enable repeat transmissions if a confirmation is not received and the number of repeat attempts to be made, the option to allow for station hopping and the maximum number of stations to utilize, and the format of the radio packets. The settings utilized in the current modem firmware configuration are detailed in Appendix C.

Figure 5.4: A Yagi type antenna to be used in the Micro Array communication system. This antenna was chosen for its 13dbi high gain, and directional broadcasting within a 30o directional beam.

5.2.2 Range and Hardware Testing

Initial range testing of the Xbee modems was conducted on the CWRU campus, as well as oﬀ campus at a site near Case Western’s Squire Valleevue and Valley Ridge Farms. Two modems being controlled by laptop computers were attached to high gain Yagi type antennas which were mounted on stands to ensure they could be aimed reliably. Running a simple shell script that generated a series of time stamped

76 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA radio packets, and another shell script that received and recorded these test packets was conducted to gauge how many packets were being successfully received at various ranges. On the CWRU south quad the modems were verified to have 100% fidelity at a range of ∼250m. Testing at greater ranges was a challenge due to limited clear line of sight on and around the CWRU campus beyond about 400m. A medium range test was conducted at Squire Valleevue and Valley Ridge Farm where a reliable and clear line of sight could be established at a range of 1.5km with an identical setup. At this range 100% fidelity was maintained. In June 2018 a series of long range radio tests were conducted at the TA CLF in Utah where, using Yagi type high gain antennas, 100% fidelity was sustained to a range of 10.86km. This test was especially significant as it utilized the same modems, Rapsberry Pi hardware, and Python SD unit and DAQ unit control scripts that are intended to be used in the fully functional Micro Array, thus verifying the viability of the hardware and software in addition to long range transmission/reception fidelity. Although longer range testing would have been desirable the accessibility of the terrain made this prohibitive. However, this range includes the distances from the planned DAQ receiver site to all proposed SD sites for the Micro Array, and thus demonstrates the robustness and suitability of the communication hardware’s signal strength capabilities.

5.2.3 Local Station Controller

The baseline plan for the Micro Array control and PMT signal collection electronics has been to use the previously developed Pierre Auger RDA local station controllers (LSC). The LSC collects PMT data, digitizes the signal, calculates local triggers, and outputs packets of data for each PA North Type SD. As the LSC never made it out of the prototyping phase of development, the LSC operating system was written mostly in ﬁrmware, with little available writable memory, and was never tested as a fully

77 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA integrated array. This means that the radio communications and other functionalities needed for operating the Micro Array needed to be implemented with no attempt to alter or add to the LSC operating system. For data communication purposes we retrieve the raw data from each LSC and port it to an external computer to be conditioned and sent to the modems with all modem control being facilitated by the external computer. A Raspberry Pi 3 was chosen for this task as they are inexpensive, easy to program, highly customizable, and if encased in a protective housing durable enough to function in a ﬁeld environment. The Raspberry Pi runs on a Linux based operating system called Raspbian, which is based on the Linux operating system Debian. This familiarity in operating systems made radio control script writing fairly easy as work could be done and tested on a full sized work station running Debian and then transferred to the Raspberry Pis once complete. The Raspberry Pis have limited memory handled by an SD card slot, so local data storage will be handled by a much faster and larger capacity USB 2.0 type memory stick.

Figure 5.5: A Raspberry Pi 3 used as a communications control computer. The Raspberry Pi 3 was chosen due to its versatility, easy programmability, and multiple USB ports which allow for easy hardware coupling and data storage upgrading.

78 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA

5.2.4 CANBUS Interface Concerns

The LSCs were originally designed to output data via a CANBUS port, however this feature was never finished or tested by the original LSC designers. Initially, attempts were made to apply a CANBUS to serial converter between the LSC and Raspberry Pi to allow for the LSC to continue operations with no alterations. Unfortunately driver and data conversion issues prevented complete packets from being successfully conveyed to the Raspberry Pis resulting in 100% data loss. Via “trial and error” our group has managed to establish CANBUS communication systems but these have proven to have long term reliability issues. As of this writing this issue is still being addressed by the Case Western Reserve University and Colorado School of Mines work group. Although in principle the LSCs do have the capability of outputting data via a serial or Ethernet port, however this option does not relay data in real time as is desired, and was not built into the LSC hardware. Current work progress suggests that an additional peripheral may be necessary to condition the CANBUS data from the LSC in order for it to be successfully received and understood by the Raspberry Pi control system. The CWRU group is also exploring new CANBUS interface hardware options that could improve the relay of CANBUS signals from the LSC to the Raspberry Pi, however if the instability issues are inherent to the LSCs themselves this may not lead to a solution [103]. In the event that the CANBUS porting issue of the LSC cannot be corrected, alternate control hardware options exist. As the PAO upgrades to Auger Prime hardware will become available that presents two options as an LSC alternative. Auger south style SBCs that will no longer be used in the PAO could be used with the former RDA tanks being reconfigured to operate with three PMTS so that the Micro Array would behave identical to the PAS SD at the CLF. An advantage to that option is that the PAS SBC has proven to take data reliably with the CLF DAQ, and Micro Array data collected in this configuration would be more able to be

79 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA directly cross calibrated with the data already collected by the PAO. Another option is that spare Auger Prime electronics currently under development may be available to conﬁgure the Micro Array SDs as Auger Prime SD types. An advantage to this setup is that Micro Array data would be more able to be directly cross calibrated with the data that will be collected by the new Auger Prime array, and technical support for the new electronics would extended to a broader group within the Pieere Auger Collaboration. If either the LSC, SBC, or Auger Prime electronics are used the planned Micro Array communication system would function identically so long as a serial port connection to a real time stream of local trigger data can be established.

5.2.5 Software Control

Control of the radio communication system is facilitated by two python scripts, one for the DAQ station, and one for each of the SD stations. The DAQ script is designed to keep the DAQ station in continuous radio reception mode, but can allow for keyboard interrupts from a user in order to transmit specific commands to each SD. As radio data is received at the DAQ it is sorted into logs for each station and held for later analysis. Each SD station’s raspberry pi stores a local log of data limited to only the 100 most recent events that met the conditions of a local trigger. The value of 100 events was chosen based on initial performance results of the communication scripts while being tested in a laboratory environment, and can be easily altered if actual field operation finds that a different number of events to be logged would be more optimal. If a threshold coincidence number of SDs send data within a set time period, the DAQ interprets this as a potential global trigger and orders all SDs to send their locally stored data logs of the 100 most recent events in their entirety. The data logs that the DAQ builds are designed to be comprehensive including both the real time and requested events with the only limit to how much data is recorded being set by the media device used to store it.

80 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA

In order to facilitate simultaneous transmission and reception of data it is necessary for the modems to operate in a full duplex configuration. That is, to function- ally transmit and receive radio messages at the same time. However, the Digi Xbee modems are not designed with full duplex capability, but instead utilize simulated full duplex. This is implemented by the SD modem scripts rapidly alternating between looking for data packets coming in from the LSC via the serial port, and looking for radio commands coming in from the DAQ station, while only transmitting data if an LSC packet is received and it is confirmed that no DAQ command is attempting to come in. The modem firmware enables short term holds on data, station hopping of packets, and repeated transmission attempts that overall take longer to execute than any single DAQ originating command would take to transmit, and thus can “hold” data for a short time in order to ensure it is received by a modem that is busy transmitting. This scheme of rapid alternation allows for a simulated full duplex that was tested to be fully reliable in the lab when using test data streams from a pair of laptop computers standing in for the LSCs. All the relevant setting detailing how the modem units’ firmware handles simulated duplex is provided in Appendix C. When a data packet is received from the serial port the SD script records the data on a log that is designed to hold a finite number of entries with the oldest entries being deleted to make room for the newest entries. It is from this log that each SD will send data in the event that the DAQ requests a check for a global trigger. This same packet of data is then immediately transmitted to the DAQ with a time stamp and station identifier added to it. In the event that an SD receives a user specified command from the DAQ the script will execute that command based on instructions detailed within the script. Examples of these commands include halting operations, setting the script and modem into receive only or transmit only modes, return to normal operations, send all data logs, or restarting/shutting down the Raspberry Pi or LSC. The SD scripts are queued into the Raspberry Pis’ boot sequences so

81 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA that in the event that an unexpected power cycle occurs the normal communication operations will begin again without the need for user intervention. In fact, this makes the basic operation of a station very easy as one only needs to apply suﬃcient power to a Raspberry Pi and Xbee modem for them to self initiate the SD script.

Figure 5.6: A cartoon scheme of the Micro Array SD communication network linking individual stations to the central data acquisition system [107].

5.2.6 Comms Conﬁguration within the Planned Micro Array

Each SD of the Micro Array will be a PA North Type SD that is designed to operate on a 12VDC system powered by a single 12VDC solar panel and deep cycle lead acid battery. The proposed communication system will add a load to the power supply that was previously not foreseen, and, if not accounted for, could jeopardize the SD’s data collection ability. Appendix B outlines the power demands of the XBee modem in its various transmission and reception states. The Raspberry Pi 3 is rated to draw up to 2.5 A of current during peak operation loads. The combined maximum loads of the Raspberry Pi and XBee modem are very small compared to the total power

82 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA load available and already being utilized by the PAN SD, and the majority of the SD communication system’s duty cycle time will be spent in passive states of idling while monitoring for serial port data or transmitted instructions from the DAQ unit. The addition of the new communication system’s hardware is not anticipated to exceed the current power budget of the PAN SDs [111]. Once the Micro Array is completed the PA and TA collaborations will move on with Phase II into direct detection allowing for the direct comparison of observations. Whether it operates from an RDA type LSC or PAS type SBC, or an Auger Prime type control system, the radio communication system designed for the Micro Array will be compatible. This radio system has already proven that it has the range, packet relaying ﬁdelity, and robustness through simplicity of design to function in the intended Micro Array.

83 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA

Figure 5.7: Members of the Pierre Auger and Telescope Array Collaborations posing in front of the TA CLF on a August 2017 ﬁeld expedition. Work included calibrating the trigger rates and thresholds of the PAS doublet, as well as installing new electronic in the CLF. Note the TA SDs to the left of the group, and directly over the head of the gentleman second to the left. These are, respectively, the Global and Local triggering TA SDs. The PA doublet is visible on the right side of the CLF building. The single solar panel of the North Type SD, and the Double Panel of the South Type SD are clearly visible. Less visible is the ASCII boiler plate mounted on top of the South Type SD.

84 5.2. AUGER@TA PHASE II: THE MICRO ARRAY CHAPTER 5. PA@TA

Figure 5.8: The former RDA SDs staged at the Cosmic Ray Center in Delta, Utah with their superstructures reconﬁgured for Micro Array operation.

85 Chapter 6

Cosmic Ray Signal Waveform Comparisons of Auger vs. Telescope Array Surface Detectors

With the scientific background, and scientific goals of this project laid out, the task of cross calibration of the Pierre Auger and Telescope Array surface detectors begins in this chapter. With both types of the collaborations’ surface detectors collocated at the Telescope Array CLF, raw voltage pulse signals (i.e. voltages vs. time) of identical extensive air showers can be collected from both types of detectors and compared to identify systematic uncertainties and offsets between the two different setups. The Telescope Array’s surface detectors utilize an all-firmware based operating system that does not provide a mechanism for directly collecting local trigger signal data from the station1. This limitation was overcome by the installation of a local oscilloscope and computer system that allowed raw signal pulses to be read directly as local triggers occured. This system is the Picoscope.

6.1 Picoscope

Characterizing the Telescope Array (TA) surface detectors (SDs) is part of the eﬀort to cross calibrate the Pierre Auger (PA) and TA equipment in order to better understand each system’s built-in uncertainties with relation to the calibrated energy measured

1The TA SDs do provide signal pulses in the event of a global trigger, but these events are too rare for the purpose of cross calibration

86 6.1. PICOSCOPE CHAPTER 6. SIGNAL COMPARISON by each type of station which should allow for a direct comparison of existing data from the two collaborations. For this purpose we use our own custom designed local trigger and data-acquisition system as a replacement for the stock TA SD electronics module. This new system allows us to directly measure the voltage pulse responses from the two TA SD PMTs for any given local trigger. The TA collaboration has provided a spare SD, known colloquially as TA Local (TAL), which is located in the TA central lasing facility (CLF) approximately 20 m away from the two PA SDs. Due to the close proximity of the three detectors it is a certainty that all three will see the same portion of extensive air showers (EAS) with suﬃciently high primary energy near simultaneously. The direct measurement of the TA SD PMTs is facilitated by a small digital oscilloscope and computer, known as the Picoscope, installed inside TAL’s electronics box by our joint collaboration work group in January 2018. The Picoscope is a Pico Technology 2206B USB oscilloscope with a 50 MHz sampling bandwidth connected to a Minnowboard Turbot single board computer (SBC)2. The scope’s SBC includes a hard drive that allows for calibration data and event wave forms to be stored locally. The scope acts as a new triggering and data acquisition system for the TAL SD located at the CLF, which can be tuned to a customized voltage pulse height threshold. Time stamps, measured in seconds out to eight deci- mal places in resolution, on the order of 10s of nsecs, from local triggers are sent to the DAQ located in the TA CLF via the local network to build a log of events to be analyzed later against events recorded by the two PA SDs [100]. The Picoscope was installed in January of 20183, however software and network issues prevented it from collecting data until June of 2018 when a repair expedition was made to rectify the problems and reset the triggering thresholds. The Picoscope

2Picoscope hardware is exhibited in Figure 6.1, the oscilliscope unit, and Figure 6.2, the computer and major system components of Picoscope inside of its aluminum housing. Figure 6.3 Shows the entire Picoscope system as installed in the TA Local SD. 3Figure 6.4 gives a cartoon schematic of how the installation of the Picoscope altered the TA SD hardware.

87 6.1. PICOSCOPE CHAPTER 6. SIGNAL COMPARISON

Figure 6.1: Closeup of the Pico Technology 2206B USB oscilloscope from Pico Tech- nology [112].

Figure 6.2: Layout of the internal conﬁguration of the Picoscope with major components highlighted, from Quinn (2017) [100]. has since then been successfully collecting scope traces continuously, netting more than 48,000 traces over the course of the month of June 2018 at a triggering rate

88 6.1. PICOSCOPE CHAPTER 6. SIGNAL COMPARISON

Figure 6.3: The Picoscope unit inside of its housing with power control unit, hard drive, and computer control board visible during its January 2018 installation of ∼ 90 mHz with a triggering threshold about -0.05 V. The oscilloscope was set to record traces in 5028 ns windows with a 5 ns times axis resolution operating at a voltage input range of ±5 V. It is this June 2018 data set that will be the main subject of Picoscope analysis presented in this thesis. The methods developed here for the June 2018 data set will eventually be applied to the larger data set collected from August 2018 onward. The Pico Technology 2206B USB oscilloscope runs on proprietary software that requires a Windows operating system, installed on the Minnowboard SBC, and records each trace in a .psdata format file on an external hard drive. For the purpose of analysis these files are batch converted to .txt files. Each resultant file contains three columns of data representing the time axis recorded on the trace, given in the form of zero being at the triggering channel’s peak voltage with negative time before the peak and positive after, and two columns for the recorded voltages of the two PMTs of the TAL SD with PMT1 being the top layer scintillator, and PMT2 being the

89 6.1. PICOSCOPE CHAPTER 6. SIGNAL COMPARISON

Figure 6.4: Cartoon from S. Quinn, et al. (2018) representing the TAL SD’s flow of signal pulse data and power supply. The left figure utilizes a pair of discriminators and a logic gate to collect signals from the two PMTs. The right figure shows the Picoscope oscilloscope and SBC system swapped in place. Note that modifications to the station’s power distribution system were required in order to supply the needs specific to the Picoscope [109].

bottom layer. The PMT channel pulses are measured as negative voltages with a 5 ns resolution readout4. Analysis of the collected data is conducted with a Python script that records the maximum peak voltage of a each of the PMT channels from each triggering event that has been recorded. These voltage peaks have their signal strength quantified by means of evaluating the full-width at half-maximum (FWHM) of the pulse. The FWHM is evaluated by fitting the raw data of each channel’s trigger event pulse with a Gaussian curve fit, and then determining the width of this fitted pulse at half of the peak voltage strength5. This is done for both channels for each recorded trace, and a histogram of maximum peak voltages and the FWHM of each channel is built.

4The Pico Tech 2206B has a bandwidth of 50 MHz corresponding to a ∼20 nsec response time. The Picoscope’s sampling rate is 500 Mega-samples/sec, corresponding to a sample every 5 nsecs. 5See Figure 6.5 for ﬁtted peak example

90 6.2. PICOSCOPE DATA CUTS CHAPTER 6. SIGNAL COMPARISON

6.2 Picoscope Data Cuts

In order to improve sensitivity to EAS events the Picoscope is operated at a threshold that is set as close to the background noise level as possible for signals from TAL. While analyzing the Picoscope data it was necessary to make cuts to the data in order to produce clean results that only represented actual EAS traces, by removing events that were likely the result of background radiation triggers. As Figure 6.5 illustrates, triggered events that were near the threshold triggering energy produced voltage pulse heights that are barely discernible from the background noise. This generally results in pulse fits that are fitted to noise or fail to fit entirely. The simple remedy to this issue is to cut events that fail to produce a quality fit, and to focus analysis only on events that produce traces which can be cleanly fit with a model trace in order to eliminate background noise events and events that fit too poorly to produce meaningful data. Two cuts are made to the Picoscope data in order to eliminate low voltage back-

2 ground noise events from the data set. The ﬁrst cut is a reduced chisquare, χν, cut made while ﬁtting a Gaussian curve to the signal voltage pulse traces. The second cut comes from analysing the trend of peak signal voltages of the two PMT channels to determine a voltage cut in order to remove events that are likely to be the result of background noise.

6.2.1 Reduced Chisquare Cut

2 2 The χν cut was made by evaluating the χν values generated while ﬁtting the June

2 2018 data set. The resulting histograms and scatter plots of the χν values of the two

2 channels are shown in Figure 6.6. Based on bin width values of one, a low end χν cut value was chosen with values less than one represent data which produced straight line ﬁts due to the ﬁtting model not recognizing a pulse, that is to say accidental

91 6.2. DATA CUTS CHAPTER 6. SIGNAL COMPARISON

Figure 6.5: Three examples of triggered events on the Picoscope with their respective Gaussian fits. The top left figure shows a recorded event that failed to fit and does not appear to have a discernible pulse above background noise. The top right figure did produce a fit though it also does not appear to have a discernible pulse above background noise. The bottom figure shows a strong and clear pulse with an excellent fit.

2 noise triggers. Based on the χν versus voltage scatter plots, values greater than 25 for channel 1, and 30 for channel 2 were cut as those represented poor ﬁtting outlier

2 events. Therefore, all traces producing a ﬁt χν value equal to or greater than one, and below 25 and 30 for PMT channels one and two respectively passed the ﬁrst cut, while all other traces are discarded. This cut removed approximately half of the event traces from the data set. The resulting data is shown in Figure 6.9 where the peak voltages of PMT channels 1 and 2 show that despite the removal of low quality events there is still a great deal of low voltage events present that do not necessarily represent actual EAS events. A trend of events above about one or two

92 6.2. DATA CUTS CHAPTER 6. SIGNAL COMPARISON

volts in both channels can be seen emerging which represents actual EAS events, and so an additional voltage cut must be made in order to isolate those events for further analysis in order to characterize the performance of the TAL SD with the Piscoscope acting as a triggering system.

2 Figure 6.6: χν histograms resulting from the event trace ﬁtting of the June 2018 2 TAL data set for the two PMT channels, as well as a χν versus peak signal voltage 2 plot showing how χν values relate to signal strength. The trend of high voltage 2 events correlating to relatively low χν values indicates that these events represent non-background noise events.

PMT Voltage cuts were considered by directly comparing the peak amplitude signals of the two TAL PMTs on a scatter plot of observed maximum peak amplitudes from both channels for each triggered event shown in Figure 6.9. Analysing the linear regression ﬁt slope values produced from the data as a function of cut voltages (Figure 6.7) revealed the voltage regimes dominated by background noise versus that dominated by likely cosmic ray signals for PMT channel 1 and 2. Cut determination

93 6.2. DATA CUTS CHAPTER 6. SIGNAL COMPARISON was made by cutting data in steps ranging from the highest recorded peak voltage value in the channel downward in 0.25 V increments to zero volts, recording the linear fit slope values for the data included in the cut value of each step. Slope values that remained fairly constant marked where a linear fit was more likely dominated by cosmic ray pulse signals, whereas fluctuating values with large errors are very likely dominated by background noise and outlier events. The result of this analysis is presented in Figure 6.7 were it appears that peak voltages above a magnitude of 1.5 V for PMT channel 1 correspond to an actual cosmic ray signals, while voltages below this level distort the correlation and therefore should be discarded. This procedure was repeated with the channels reversed on the x and y axis in order to determine the voltage cut value for channel 2, which was determined to be 1.0 V. When fitting the data from the two PMTs all signals less than 1.5 V in magnitude channel 1, and 1.0 V for channel 2 are removed. Figure 6.9 shows the results of this cut with a comparison of all data versus cut data. The resulting data produces a linear fit with a slope of 1.06 ± 0.04, suggesting that the channels are responding linearly with one another. However, it should be noted that as channel one required a larger voltage cut and the intercept of the linear fit returned a value of −0.67 ± 0.04 the PMT 1 is recording events at a higher voltages than PMT 2. This may be the result of the new Picoscope system having not yet had both channels calibrated against each other, which may be accomplished in a future expedition utilizing the built in LED flasher system on board the TA station.

6.2.2 Peak Voltage Cut

2 The post χν cut values of the peak voltages of the two PMT channels were plotted into histograms as an additional veriﬁcation on the TAL-Picoscope system (Figure 6.10). In the case of the PMT peak voltage histograms a similar analysis technique was used evaluating the power law ﬁt index values as a function of cut bins ranging

94 6.2. DATA CUTS CHAPTER 6. SIGNAL COMPARISON

Figure 6.7: The results of the slope of linear fits of the PMT1 vs PMT2, and PMT2 vs PMT1 peak voltages plot as a function of voltage cuts, used in order to determine the best cuts for channel 1 and 2 respectively. The x-axis denotes the voltage value below which data was excluded. The slope values suggests that all data points below 1.5 V for channel 1 and 1.0 V for channel 2 should be excluded from a final fit.

from only the five highest voltage valued bins to all 50 bins utilized in the histograms in five bin increments. Fairly constant values of power law index with relatively small fit errors indicate the voltage regime likely dominated by cosmic ray pulses, while fluctuating values with large fit errors indicate background noise. The results of this cut fitting analysis is presented in Figure 6.8 where it appears that the first 15 bins of both channels are dominated by background noise. In order to estimate measurement uncertainties in the peak voltage values we need to consider the variable amplitude corresponding to the dynamic range for each of the two Picoscope channels. The voltage values of each bin is dependent on the maximum voltage recorded in the data set, with binning currently being 50 divisions of the difference of the maximum and minimum peak voltage values. In the data presented here each bin of PMT 1 and PMT 2 correlates to ≈0.08 V, with the cuts eliminating voltages below ≈1.2 V from the power law fits. This voltage cut value agrees with the values found in the cut performed in determining the voltage cuts of the two channels for used in Figure 6.9.

95 6.3. TA LOCAL SD ANALYSIS CHAPTER 6. SIGNAL COMPARISON

Figure 6.8: The results of the index values of power law fits of the PMT1 and PMT2 peak voltages histograms as a function of voltage bin cuts. The x-axis denotes the voltage bin value below which data was excluded. From these plots it appears that power law fits should exclude the first 15 voltage bins of the PMT1 and PMT2 peak voltage histograms.

6.3 TA Local SD Analysis

From the histograms in Figure 6.10 the performance of the TA Local SD may be evaluated further. The peak voltages histograms of PMT 1 and PMT 2 shown in Figure 6.10 follow roughly the same shape and can be fitted with a power law fit that show a power law index of -4.05±0.26 for PMT 1 and -2.73±0.39 for PMT 2 in the post cut bins. The power law form of the detected signal spectrum is what would be expected from secondary cosmic ray particles that result from a primary cosmic ray energy spectrum with a power law form. In the cosmic ray energy power spectrum the power index for energies above the “knee”, which is the energy regime of interest for this project, is -2.95. Because we have no measurement to reconstruct EAS (distance from shower core, indications of arrival direction, etc.) we cannot directly infer the cosmic ray primary energies associated with individual pulse height measurements at the TAL. However, we expect that most measurements at TAL correspond to relatively low energy EAS (≈ 1 − 10 × 1015 eV) that land relatively close to the SD (likely within 20 m). The exact form of the measured voltage signal strength power law spectra will also depend on the specific response details of the detector.

96 6.3. TAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

Figure 6.9: Raw Picoscope data for June 2018 comparing the peak voltages of the two channels for the two TA Local PMTs is shown in the top left graph. The top right 2 graph shows the results of the first χν based cut which removes several high voltage outlier event and a little less than half of the low voltage events that were likely the result of background noise triggers. After a voltage cut is applied to the two PMT channel a fit of higher voltage events is made in the bottom graph, which produces a linear fit with a slope of 1.06±0.04, indicating that the two channels respond linear with respect to each other though channel 1 tends to record events at higher volts than channel 2.

Nonetheless, the reasonably close agreement between the known power law index for

97 6.3. TAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

Figure 6.10: Picoscope peak voltage histograms for June 2018. The two channels for the two TAL PMTs are shown with linear fits that exclude the first three bins. The index of the power fit to PMT 1 and 2 are -4.05±0.26, and -2.73±0.39 respectively. cosmic rays and our measured pulse height spectrum is qualitatively reassuring and confirms our expectation that measured signals represent real cosmic ray air showers. Figure 6.11 shows the FWHM profiles of the uncut data for both layers of scin-

2 tillator panels, followed by the post χν and voltage cut results. The bottom ﬁgure shows remarkably narrow signals are being produced while the top panel shows a strong peak for narrow voltage signals, but also reveals several hundred events that produced incredibly broad and messy pulses. These broad events are very likely the result of background noise interfering with data collection, and are another motivator for the cuts described in the previous section. Once the cuts described in Section 6.2 were applied the new post-cut FWHM results show a narrow distribution of peaks centered around ≈20.25 nsecs. These narrow width events, which represent the post cut peak voltage signals in each PMT, signify successfully recorded EAS. When overlaid

98 6.3. TAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

Figure 6.11: Picoscope histograms for June 2018. The two channels for the two TAL PMTs FWHM values before and after the two cuts are applied. Signals representing reliable EAS traces fall into a very narrow pulse width band centered around 0.25 µsecs. with each other very similar distribution proﬁles are seen, suggesting a fast response time for the TAL scintillator-pmt system.

99 6.4. PA AND TA COINCIDENCE CHAPTER 6. SIGNAL COMPARISON

The results of this analysis suggests that the TAL SD with the Picoscope triggering and event trace recording system is operating as designed, successfully recording EAS events with a fast signal response time. This ensures that the following evaluation of events that were recorded in coincidence between TAL and the PA doublet at the TA CLF will provide a meaningful cross calibration of the two diﬀerent types of surface detector systems.

6.4 PA and TA Coincidence

With the Picoscope collecting data, the CLF DAQ was configured to compile trigger events from the TAL, PAS, and PAN stations that met a trigger time coincidence window of 30 µs. Unfortunately, during the June 2018 data run PAN was not collecting data due to power supply issues, but PAS successfully collected 317 coincidence with TAL events, henceforth referred to as the CTAL events. However, none of these events survive the TA Collaboration specific reconstruction cuts, and so no direct comparison between global TA events and those recorded by Picoscope traces is possible at this time. In the months proceeding the data represented in this thesis, and continuing into the near future, we expect CTAL events to continue to be logged at a rate that will allow for a statistically significant cross calibration to be made in the coming year. Nonetheless a sufficient number of CTAL events from the June 2018 data set allows for an initial feasibility check of cross calibration to be made [100][108].

6.4.1 Local Trigger Time Oﬀset

It is the case that even with identical GPS common time stamping identical events, the TA SDs and PA SDs record local event triggers at slightly different times. This is due to systematic differences in the trigger hardware, with different on board station

100 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON clocks being utilized and different signal path-lengths within the station circuitry, and different procedural methods of generating and labeling timestamps for data packets between both types of detectors. Although it is not feasible to determine an “absolute time” to compare these two sets of time stamps to in order to determine their offset differences, it is possible to determine a time offset for one system relative to the other that allows for cross calibration of the stations’ time tagging. Time offset measurements were made by comparing the 317 coincident events that were recorded by the TAL SD via the Picoscope, and by the PAS SD during the same time period that both SDs were successfully recording events. A total of 306 events were utilized for this analysis as 11 recorded triggers indicated random noise triggering that produced a 2:1 trigger event for a single coincidence window from either the PA doublet or the TAL station. These 306 events were compared by subtracting the reported time stamps of PAS from TAL and building a histogram of the time differences. This time difference distribution should have a random and a systematic component, with the random component being the result of timing uncertainties due to variations in EAS arrival directions and also minor variations on individual event measurements made by each station, while the much more dominant systematic component, being the result of station-type calibration offsets, should emerge as a strong peak on the histogram. Figure 6.12 shows a histogram of the distribution of time offsets at a bin resolution of 5 nsecs per bin, in order to match the 5 nsec timescale resolution of the Picoscope, with a strong peak centered on the systematic time offset. From a distribution fit to this histogram it was found that the TAL station records times -1.098±0.1995 µsecs ahead of the PAS station with a standard deviation of σ=0.113 µsecs. This time offset figure will statistically improve as CTAL events continue to be logged over the operation of the CLF detectors. With this time offset identified we can make a direct comparison of future data recorded by the two different types of SDs located at the TA CLF by matching triggered event

101 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON times in coincidence with respect to the speciﬁed oﬀset, as well as introduce a data comparison quality measure to be utilized in cross calibration of the two types of surface detectors.

Figure 6.12: The time offset between PAS and TA Local recorded in microseconds. The left histogram has bin resolutions of 52 nsecs and shows a dominant offset time at ∼1.1µsecs. The right histogram utilizes a bin resolution of 5 nsecs per bin to match the time axis resolution of the Picoscope data, and was fitted with a Gaussian optimized fit. The peak of the fit suggests an offset of -1.098±0.1995 µsecs, with a σ of 0.113 µsecs.

6.4.2 Cross Calibration

In order to cross calibrate the TA type SD against a PA South Type SD, the raw event trace data recorded by the two diﬀerent types of detectors must ﬁrst be converted to the appropriate calibrated energy units, those being MIP and VEM respectively. The PAS station traces are reported in terms of an uncalibrated charge collected by an analog-to-digital converter (ADC) which after a pedestal subtraction gives a dynamic range of ADC counts reaching as high as 500 counts. Over the course of a day the PAS station reports a charge count histogram based on the ADC counts being converted into a charge count. This charge count histogram provides the ADC pedestal value and the pedestal to muon peak value by which the trace ADC count minus the pedestal count converts to a VEM value for an individual event. An

102 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON example of a charge count histogram is provided in Figure 6.13. The VEM is found from the ADC trace of the PMT according to the formula

ADC − P edestal NumberofV EMS = peakvalue (6.1) P edestal − Muon

Figure 6.13: From the CTAL June 2018 data set. Over the course of several hundred events the converted ADC count to charge values of all three PAS PMTS show the pedestal value of each event trace for a PMT, as well as the diﬀerence between the muon peak from that pedestal value. These values allow for the conversion of a raw ADC trace into a calibrated VEM value.

The MIP value of the TAL traces is found by plotting the peak voltages of the two PMT channels of the TAL station in a histogram. The results of these histograms are show in Figure 6.14. PMT channel 1 shows the emergence of a strong muon pulse that has been ﬁtted with a Gaussian curve. While PMT channel 2 does not show a distinguishable peak yet emerging from the limited data, based on the analysis of the previous section, characterizing the TAL Picoscope data, it is reasonable to assume that the two channels would have a nearly one to one response, and so the Voltage to MIP calibration found in channel 1 is applied to channel 2 for this analysis. As CTAL data continues to be logged, it is a certainty that both channels will have

103 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

improved statistics which will allow for channel 2 to be directly calibrated. For now, the assumption that the calibration of channel 1 is suﬃcient for channel 2 will continue for the remainder of this analysis in order to prove that a cross calibration will be able to be made.

Figure 6.14: The peak voltages of the 306 CTAL events of TAL PMT1 and PMT2. The histogram of PMT1 shows a strong muon pulse that has been ﬁtted with a Gaussian distribution.

In order to ensure that only strongly coincident events representing EAS triggers successfully witnessed by both station are evaluated, a series of quality cuts must be made. The “zero cut”, or Cut 0, is applied while the event traces from the TAL and

2 PAS PMTs are being ﬁt. Applying the same standards utilized in Section 6.3, χν values that do not ﬁt within the accepted range are removed. As a result no events were cut, hence “zero cut”. This strongly indicates that each event being analyzed represents a real EAS event that produced a clean pulse trace. However, it may still be the case that by chance low energy EAS simultaneous triggered both stations within the time coincidence window, and produced a CTAL event that does not represent traces that should be included in cross calibration. So an additional time quality cut is made Cut I is made to determine that the evaluated traces represent a single high energy EAS that trigger both stations simultaneously. The mean peak voltage values of the two TAL PMTs, and the mean peak ADC count values of the three PAS PMTs are

104 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON found, along with the corresponding trigger time stamp of the two stations’ mean peak values. These values are contrasted against each other to find the time difference of the coincident event. The trigger time offset from Section 6.4.1 is applied removing all but the 109 events that correlate to the trigger time offset peak. These events should represent EAS that were sufficiently high enough in energy to guarantee that both stations were triggered nearly simultaneously, and a sample of the raw traces of one of these events, Figure 6.15, indicates that this is indeed the case.

Figure 6.15: Raw Voltage from TAL and ADC traces from PAS for a single post Cut I CTAL event. The high peak values above background with narrow pulse widths indicates a relatively high energy EAS event.

The mean peak voltage and peak ADC values are converted to MIP and VEM respectively, and the mean MIP value of the two TAL PMT channels are plotted against the mean VEM values of the three PAS PMTs (shown in Figure 6.16). In order to cross calibrate the TA and PA detectors a multiplicative factor must be found that would cause the MIPs and VEMs of identical events to ﬁt to a one-to-one relation. As can be seen in Figure 6.16 there is still a large number of low MIP events that may be dubious to attempt ﬁtting for a cross calibration. While higher MIP

105 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

events do seem to show a correlation between MIP and VEM emerging, there are far too few points of data at this time to make an assertion that a deﬁnitive cross calibration can be made. However, it is possible to apply two more alternative cuts in order to make an estimation of the upper and lower bounds of what a MIP to VEM cross calibration factor may be.

Figure 6.16: (Left) Mean peak MIP and VEM values of the TAL and PAS stations from all 306 CTAL events. (Right) The 106 local triggers found to be within ≈1100 nsecs of each other after the application of Cut I. For higher MIP and VEM value events in the post Cut I data a trend may be beginning to emerge, but more data is necessary to determine if that is true.

The first of these two additional cuts, Cut IIA, is based on the Picoscope voltage cut determined in Section 6.3. Here TAL events with peak voltages less than 1.5 V in channel 1 and 1.0 V in channel 2 are removed. This results in only three points surviving, shown in Figure 6.18. Three points, of course, does not represent conclusive results. However, these three points represent the most likely high energy EAS candidates successfully observed by the two stations in this data set. An upper bound to the cross calibration factor is found by making a linear regression fit to the data with the intercept held at zero. The slope of this fit represents the cross calibration factor, MIP = C × VEM (6.2)

with “C” being the cross calibration factor, and is found to have a value of 1.22±0.34.

106 6.4. CTAL ANALYSIS CHAPTER 6. SIGNAL COMPARISON

The second additional cut used to determine the lower bound of the cross calibration factor is called Cut IIB. This cut replaces the previous cut, Cut IIA, by instead removing low MIP value events. The value chosen to make this cut came from evaluating a histogram of the MIP values reported in the two TAL PMT channels, Figure 6.17, where it was found that at greater than two MIPs the two channels produced histograms of roughly the same shape, while below this value the two channels show sharp peaks that do not necessarily follow each other. Cut IIB removed all sub two MIP events which left seven surviving events. The same linear regression ﬁtting rou- tine utilized in evaluating Cut IIA was applied and a reported lower bound result is found with a “C” value of 0.77±0.20.

Figure 6.17: Histograms of the MIPs of the 306 CTAL events as reported by both TAL PMT channels. This was utilized to determine the cut values of Cut IIB.

107 6.5. TA GLOBAL SD ANALYSIS CHAPTER 6. SIGNAL COMPARISON

Quinn (2018) reports in his thesis to have identiﬁed a MIP-VEM relation of

+0.12 1.00±0.08 SMIP = 0.55−0.10 SVEM (6.3) +0.41 1.00±0.08 SVEM = 1.81−0.33 SMIP based on a combination of air shower Monte Carlo simulations modeled on real SD response characteristics. These constraints on the cross calibration factor, based on real EAS CTAL data, predicts a cross calibration factor within the limits of

SMIP = C × SVEM

CUpperBound = 1.22 ± 0.34 (6.4)

CLowerBound = 0.77 ± 0.20

While Quinn’s value just falls within the upper and lower bound values predicted here, within the tolerance of the reported uncertainties for both sets of numbers, it must be emphasized that the values reported here are based on preliminary data with very few points of data fitted. As such, the confidence in these bounds is very low, and they should not be taken as the actual cross calibration factor. As data continues to be recorded and analyzed the statistics of this analysis will continue to improve to the point that a definite trend between MIP and VEM from real high energy EAS events recorded by the two SDs will emerge. The calibrated energy reconstruction and comparison techniques demonstrated here will then be used to determine a reportable cross calibration factor [100].

6.5 TA Global SD Analysis

The TA station at the CLF designated to record nearby TA global trigger time stamps and relay those time stamps to the CLF DAQ is known as TA Global, or TAG. Unfortunately the TAG station while operating cannot simultaneously provide any

108 6.6. PICOSCOPE NEAR FUTURE CHAPTER 6. SIGNAL COMPARISON

Figure 6.18: (Left) The result of Cut IIA applied to the data showing the best estimated fit line resulting from an estimated upper bound of the cross calibration factor with a value of 1.22±0.34. (Right) The result of Cuts IIB being applied to the data showing the best estimated fit line resulting from an estimated lower bound of the cross calibration factor with a value of 0.77±0.20. local trigger information to our data acquisition systems at the TA CLF, however the TAG does allow for the PA doublet to mark doublet events that are within 100 µsecs coincidence of nearby global triggers. This coincidence window was chosen based on a 13±0.2µsecs time stamp offset between PAS and TAG identified by our group6 [109]. As Figure 6.19 shows the calibrated VEM values of the two PA doublet SDs agree with each other for local triggers (i.e. both PA stations only) with a nearly 1:1 agreement being maintain for CTAG events. This provides evidence that the results of the PAS and PAN stations can be directly correlated with each other, and calibrated against the TAL results. This result remains true for the subset of local triggers that are also global triggers (coincident with TAG) as shown with the black dot events [107][109].

6.6 Picoscope Near Future

The Picoscope-TAL system has been continually recording event signals in concert with the PA doublet since August 20187 to the date of this writing, and will continue

6See: Quinn PhD thesis [100] 7Data collection was brieﬂy suspended in July 2018 due to DAQ CLF low power issues.

109 6.6. PICOSCOPE FUTURE CHAPTER 6. SIGNAL COMPARISON

Figure 6.19: From Covault, et al. (2018); Calibrated VEM of local triggers for the PA doublet with black dots representing events that registered on the TA Global trigger. A power law fit of the PA doublet local trigger VEM values shows an index of 1.1±0.1 (solid red line), with a fit of 1.01±0.02 for the global events (dashed red line)[107]. to do so into the indefinite future. August 2018 alone generated a new set of ≈50,000 events that can be batch converted and analyzed using the methods described in this thesis over the course of several weeks. Likewise, an additional 220 CTAL events were recorded in the same month. Analysis of these events is ongoing. Further analysis of these events, and the events generated in subsequent months, will improve the statistics of the analysis presented in this chapter and refine conclusions to be made about the systematics of both the PA and TA hardware. The next step in cross calibration between TAL and the PA doublet will be to establish event triggering between the two stations sets. The current CTAL data available comes from recordings of all local triggers made by both TAL and the PA doublet with events marked as being within a 30 µsec window of each other being compiled into a log to be analyzed later. By utilizing the PA doublet as an external

110 6.7. TAL AND AUGER PRIME CHAPTER 6. SIGNAL COMPARISON trigger for the TAL station, with the identified time offset correction factored, only events that are certain to be in coincidence between the stations can be compiled into a more efficient log that will expedite analysis. With the tools of this analysis already built future members of the work group will be able to conduct this analysis over a relatively short time scale.

6.7 TAL and Auger Prime

The ability to analyze the two TA SD PMTs directly has the potential to extend the collaborative work of cross analyzing TA and Pierre Auger Observatory (PAO) data into the era of the upcoming PAO upgrade, Auger Prime. Auger Prime will feature major electronics upgrades to the SDs in the PAO, and will also feature a hardware upgrade with the addition of a large scintillator panel mounted on top of each SD known as the Auger Scintillator for Composition II (ASCII). The purpose of the ASCII is to provide the observatory with the ability to better analyze the diﬀerent components of an observed EAS. Lower energy electrons, the electromagnetic component of the shower, will tend to deposit most of their energy in the scintillator panel, while higher energy muons will generally pass through the scintillator and will deposit their energy in the water of the Auger Prime SD. This phenomenon will allow researchers to compare the energy deposited in the two diﬀerent detection mediums which can lead to conclusions about the ratio of EAS particles that are electrons or muons, and can lead to clues about primary particle species, and better modeling of particle interactions. The TA SDs already have the capability of making this sort of analysis to a limited degree as their two layered scintillator design similarly receives the more of the electromagnetic component of an EAS in the top layer of detection medium, with the higher energy muon component passing through and depositing in the bottom layer.

111 6.7. TAL AND AP CHAPTER 6. SIGNAL COMPARISON

This is illustrated in Figure 6.9 where both the top and bottom PMTs show approximately the same number of events, but high voltage events are recorded preferentially by the top PMT, PMT 1, with PMT 2 producing a cleaner profile. The initial run of Picoscope data suggests that an EAS particle component type distinction could be made with the TAL SD. With the upcoming Auger Prime upgrade, and the ability to upgrade the PAS SD at the TA CLF to a prime type tank8 there is a future potential to continue cross calibration of data, and verification of analysis made by both observatories past the Auger Prime upgrade. This chapter represents the heart of the research outlined in this thesis. As a result of the installation of the Picoscope at the TA CLF we have direct access to the local TA station response and can compare our expected VEM and MIP station responses for both TA and PA type stations. We have generated a coincidence lists of triggering events between the TAL and PAS stations at the CLF, which we have used used to determine the timestamp offset between the the TA and PA SDs, quantify the differences of calibrated energy observed by the two different types of station, and to independently verify quality of the performance of the TA SDs. With the Picoscope providing direct voltage pulse information from TAL with the DAQ providing the ability to identify coincidence triggering information for PAN, PAS, and TAL SD cross calibration between TA and PA hardware can be conducted. From the limited data available from June 2018 the upper and lower bounds of the TA MIP to PA VEM cross calibration factor has been identified. As data continues to compile a high confidence cross calibration factor value will emerge, which will be utilized to begin making more direct comparisons of the existing TA and PA data sets.

8See Chapter 7

112 Chapter 7

Air Shower Reconstruction with Detector Simulations

As part of the effort to bring Pierre Auger (PA) type surface detectors (SDs) to the Telescope Array (TA) Observatory, and make direct comparisons between the two different methods of detection against identical extensive air showers (EAS), an independent means of reconstructing EAS was highly desirable. EAS data interpretation is almost always dependant on the application of Monte Carlo air shower simulations, CORSIKA being a primary example, that predict particles on the ground from detailed modeling of shower particles propagating within the atmosphere. However, and especially in the case of high energies, these simulations are computationally expen- sive requiring days or even weeks of dedicated CPU time to execute a run of a single shower. The goal of this effort was to allow for PA members to analyze EAS observed by either or both the TA and PA type SDs in an expedient way that demands on relatively light computer resources, and could reliably determine an estimate of primary energy and arrival direction independent of particle interaction models. This should allow researchers to characterize EAS based on triggering data from the Micro Array and can include the neighboring TA SDs, or can be used to reconstruct the same EAS with only one set of data or the other, and to do so without introducing bias. This work is additionally motivated by operational constraints placed on the availability of

113 7.1. MCCRRS CHAPTER 7. MCCRRS instrumental details due to the terms of the memorandum of understanding (MOU) between PA and TA Collaborations. This MOU limits PA working group members from accessing any detailed pulse waveform data from individual TA stations that participate in EAS global triggers. Our goal here is to provide an estimate based on details limited to global trigger participation.

7.1 MCCRRS

We have developed a Python based simulation program to determine preliminary EAS parameters based on measurements from both PA and TA stations at and around the TA CLF. The Monte Carlo Cosmic Ray Reconstruction Script (MCCRRS) reconstructs an EAS by using a chain of Monte Carlo calculations in four sequential phases and utilizes particle parameterization in order to expedite computation time to the point that specialized research grade computers are not necessary in order to execute a full run of the program. The MCCRRS is ﬂexible enough that it can be utilized to analyse data from either TA or PA type SDs in any array formation or nearest neighbor spacing. The only input requirements from real data are the location of the SDs, a list of which SDs recorded local triggers in a global trigger, and what the timestamps of those triggers were for each SD. The data used for the reconstructions presented here were provided by the TA Collaboration as part of a memorandum of understanding (MOU) agreement between the relevant work groups of the PA and TA Collaborations. Here it must be emphasized that the MOU data provided by the TA Collaboration is preliminary and that these reconstructions are provided solely for the purpose of initial comparison to measurements made by the Auger type SDs in the TA CLF, and to determine the viability of the following EAS reconstruction technique. Once per annual quarter EAS events that met the criteria established in the MOU are published for study within the PA at TA work group. The criteria for selection is that an identiﬁed EAS

114 7.1. MCCRRS CHAPTER 7. MCCRRS must trigger both the local TA station and the PA type stations, both co-located at the TA central lasing facility (CLF), and must have an estimated primary energy of at least 1018 eV based on TA reconstruction of the event. The MOU provides both the TA reconstruction estimates of primary energy, arrival direction, and core location, and the identity of each SD in the TA SD array that participated in observing the EAS as well as the MIP value and time stamp recorded by each participant SD. Using the MOU with the MCCRRS provides a quick initial comparison between TA’s reconstruction to what the PA work group might predict prior to conducting a full detailed reconstruction, and allows the PA work group to compare the performance of the two CLF located PA SDs to the TA SDs with only limited information on the full details of TA’s reconstruction methods. Table 7.1 outlines each of the phases of the MCCRRS and how each subsequent phase is designed to improve the estimates made in the previous phase.

Phase Inputs Products I Random Number Generation and Primary Energy and Core Location real data SD trigger list II Phase I Energy and Core Location Reﬁned Energy and Core Location III Random Number Generation and Arrival Direction real data SD timestamps IV Phase III Arrival Direction New Primary Energy and Core Lo- cation Final Products of Phases I, II, III, and IV Mean Primary Energy, Core Loca- tion, Arrival Direction

Table 7.1: An outline of the processing method of the MCCRRS broken down by phase.

7.1.1 Phase I

Phase I begins by assuming a vertical shower with a core located at the center of the SD array. The SD array in the script is built as an 8x8 array with the three CLF SDs located at its appropriate coordinates near the center. This array size was

115 7.1. MCCRRS CHAPTER 7. MCCRRS chosen from the MOU data where it was observed from the TA reconstructions that the steepest inclined EAS showers that would successfully trigger the CLF SDs would never trigger stations further away than three stations away from the CLF. With the 1.5 km TA SD spacing being reproduced and a half spacing around the outermost SDs this produces a ﬁducial area for the calculations with the total area represented in the simulated array being 144 km2. The only reconstructed parameter initially being evaluated in Phase I is the primary energy of the EAS, with a minor random wobble of the core location for each primary that does not exceed 500 radial meters from the center location1. This wobble distance was chosen so as to be less than the half SD spacing in order to prevent triggering results outside of the scope of what this phase of the script could utilize. The MCCRRS generates EAS particle density proﬁles for a range of primary energies from 1016 eV to 1021 eV in steps of 0.5 of the base value (0.5 × 1016, 1.0 × 1016, 1.5 × 1016, etc). Candidate energies are selected by calculating whether an SD in the script’s array would produce a local trigger, or not. Determining whether a station would produce a local trigger comes from sequentially calculating the lateral distribution function (LDF) of the EAS in both vertical equivalent muon (VEM) for the PA type SDs and minimum ionizing particle (MIPs) for the TA type SDs. The LDF calculation is a key feature of this program as it provides the “thinning” that allows the code to run expediently, especially when compared to atmospheric Monte Carlo simulations2.A method of thinning commonly employed in air shower simulations of that type is to

1The purpose of the core wobble is to introduce minor variability of the VEM or MIP densities at each SD site as there would otherwise be no minor variations of the results measured by the SDs, and it would be possible to produce a “stagnant run” where a reconstruction totally fails to generate any results due to a particular primary energy, by chance, failing to trigger the desired number of SD stations. 2In an ideal situation this sort of program would run as a fully explicit simulation in which each particle from the primary all the way to the last generation of daughter particles reaching the ground would be accounted for with their trajectories and particle interaction models being calculated in full detail. In reality such a program would require an extremely powerful computer stack and take a prohibitively long time to complete a single simulation run.

116 7.1. MCCRRS CHAPTER 7. MCCRRS only include representative particles that account for a certain number of particles of that species, and to cut away parts of the interaction models that aren’t relevant to the desired end product. In the case of the MCCRRS there is no direct consideration of individual EAS particles in the atmosphere. The LDF represents the expected calibrated particle density that would be seen on any given point on the ground based on the primary energy, arrival direction, and distance from the center of the EAS footprint on the ground, known as the core location. Our method of LDF calculation allows the calibrated particle density for a particular EAS seen at all SDs to be found nearly simultaneously which greatly reduces both the power of the computer and computation time needed to the point that a consumer grade desktop PC can execute a full run of the MCCRRS in only 45 to 60 minutes. For both VEM and MIP, an Nishimura-Kamata-Greisen (NKG) type LDF was used as this form has been found to best match real data [113][114][115][116]. The form of the VEM LDF comes from Barnhill, et al. (2005), and from Roth (2003) which is:

r −β r −β S = k 1 + (7.1) (E,r,θ) 700m 700m where r is the distance from the shower core, 700m is a characteristic distance where the value of the scaling factor k, expressed as

S1000 k(E,θ) = (7.2) 170 −β 49 where β represents a zenith angle dependant function expressed as

β(E,θ) = 2.23 + (0.082) ln (S1000) − 0.95 (sec (θ) − 1) (7.3)

117 7.1. MCCRRS CHAPTER 7. MCCRRS

[115][116]. Here E is the primary energy, and θ is the zenith angle component of the arrival direction. The S1000 factor was found from D.W. Newton (2005), and is expressed as log E +0.79 [ ( EeV ) ] S1000(E) = 10 1.06 (7.4)

[117]. Note that primary energy is expressed in both the scaling factor and the zenith angle factor, but is by far dominant in the scaling factor with only minor inﬂuence imposed in the zenith angle factor. The form of the MIP LDF came from Ivanov’s thesis (2012) analyzing the energy spectrum measured by TA. Ivanov describes the form of the MIP LDF, measured in terms of MIP density, as follows,

r −1.2 r −(η−1.2) r 2−0.6 ρ = A 1 + 1 + (7.5) (E,r,θ) 91.6 91.6 1000 with r being the radial distance from the shower core, and η being the zenith angle dependant factor expressed as

η(θ) = 3.97 − 1.79 (sec (θ) − 1) (7.6) with θ being the zenith angle [118]. The scaling factor, A, is not explicitly stated in Ivanov’s paper, nor readily found in any other relevant TA collaboration published work. Deciphering the scaling factor was critical for reconstructions as this factor is the energy dependant term of the MIP LDF, without which the EAS primary energy cannot be reconstructed. A scaling factor was derived by utilizing the work done in our group and published in Quinn’s thesis (2017) where Quinn identiﬁed a relation between VEM and MIP based on measurements taken at the TA CLF with co-located PA North and South Type SDs, and the TAL SD using a detector response model

118 7.1. MCCRRS CHAPTER 7. MCCRRS

provided by the Pierre Auger Collaboration [100]. This relation was found to be

+0.12 1.00±0.08 SMIP = 0.55−0.10 SVEM (7.7) +0.41 1.00±0.08 SVEM = 1.81−0.33 SMIP

Using this relation a Python computer script was written to calculate what the expected MIP strength should be for a particular zenith angle and a core distance of 600 m by calculating a profile from the well known VEM LDF and multiplying it by the MIP-VEM factor. The distance of 600 m was chosen since, unlike the VEM LDF, the MIP LDF does not have a characteristic distance where the scaling factor does not vary with primary energy, but at distances at or greater than 600 m all sans scaling factor MIP values approach an asymptotic value that is roughly the same regardless of energy or zenith angle. This was used to generate a plot of predicted MIP values at a variety of primary energies (See Figure 7.1). This plot was then fitted with a best fit line, and with trial and error a scaling factor was derived as

E A = 10η(θ)+0.8 (7.8) (E) EeV

. This scaling factor was found to produce excellent matching with the predicted values generated by Quinn’s relation at zenith angles up to 30o, however at angles greater than 45o the scaling factor wanders away from the predicted value signiﬁ- cantly enough to make steep angle reconstructions unreliable. Although this limit is discouraging it is the case that the current methods of reconstruction utilized by the PA Collaboration also produce increased errors for EAS with zenith angles at or greater that 45o, and so isn’t unusual. From the TA reconstructions provided in the MOU, EAS zenith angles above 40o are scarcely reported, and so it may be inferred that the TA Collaboration’s methods of reconstruction is constrained by a similar

119 7.1. MCCRRS CHAPTER 7. MCCRRS limit. Due to the MOU EAS almost never exceeding the 45o limit present in the MCCRRS this zenith angle induced error is not viewed as a problem.

Figure 7.1: Plots comparing predicted value of MIP scaling factor from Quinn’s VEM- MIP relation with derived scaling factor at zenith angles of 0o, 15o, 30o, 45o, and 60o. For nearly vertical showers the derived scaling factor matches exactly with the Quinn correlation prediction. At around 45o a divergence appears making this scaling factor unsuitable for reconstructing EAS with steep zenith angles.

120 7.1. MCCRRS CHAPTER 7. MCCRRS

In the MCCRRS the VEM or MIP density is found for each SD, and multiplied by the effective surface area of each type of SD to find the local VEM or MIP value. That value corresponds to an expected number of ground particles in the detector and is then fed into a Poisson Probability Distribution to find the probability of a successful trigger, which is subsequently fed into a binomial distribution to determine whether a local trigger occurred or not. The results of this sequence builds a binary trigger list of simulated station data. The decision as to whether a particular EAS calculation was successful or not is based on a generated binary trigger list that is compared to a binary trigger list from actual data. When real EAS data from the MOU is input into the reconstruction script it is in the form of a binary list of ones and zeros with each position on the list representing a particular TA SD location. A one represents a TA station that produced a trigger, while a zero represents a station that did not participate in the triggering. As each part of each phase of the script is completed each SD that is calculated to trigger is populated as a one on a simulated data trigger list in the appropriate location while the remainder of the SDs populate the list with zeros. We generate a “quality factor” that represents the matching between simulated and real triggering lists: The sum of the root squared difference of the two lists is then calculated as an indication of the extent to which SDs are out of place between the real and simulated data, with zero being a perfect match, one representing a single SD mismatch between the two lists, and etc. The parameter value being calculated is then saved in a list appropriate for the quality of the match. This is used to generate histograms for the user representing the specific parameter value distributions for each set of quality fittings. In the case of the first part of Phase I a mean primary energy as well as the standard error of the mean is continuously being calculated for each list of primary energies being produced for the first six orders of fitting. That is the perfect match

121 7.1. MCCRRS CHAPTER 7. MCCRRS case, the one off case, all the way through to the five off case. The energy estimation of Phase I will repeat continuously in a while loop until a statistical quality factor is met for one of these fitting orders. The particular quality factor being utilized as of this writing is that the mean value of all successful energy matches must be at least 300 times greater than the standard error of that mean. The number of trials needed to achieve this is recorded and used as the number of times each subsequent part of the script must run in order to ensure that each part of the script is evaluating an equal number of data points in order to ensure statistical consistency across all phases of the script. This quality factor was based on trial runs of the MCCRRS and was found to meet a balance between resolution of an accurate reconstruction while maintaining a relatively short computation time. This quality factor can easily be tweaked to either shorten the computation run time, or increase the resolution of the final results if future users desire to do so. The second portion of Phase I holds the found mean value of primary energy from the first portion of Phase I fixed, while still assuming a vertical shower, but now varies the EAS core location3. Core locations are measured in Cartesian Coordinates in terms of meters from the center of the array, with positive values for East and North, and negative values for West and South. The locations are chosen using random number generation to pick a coordinate, and using the method described above from the first part of Phase I a binary trigger list is generated and compared to the actual data’s binary trigger list. The end result of Phase I will be a mean primary energy and core location.

3The EAS core empirically represents the “impact point” on the ground of the original cosmic ray primary and is generally estimated in experiments as the “center-of-mass” average position of secondary particles as measured on the ground.

122 7.1. MCCRRS CHAPTER 7. MCCRRS

7.1.2 Phase II

Phase II repeats Phase I, but will only look at primary energies and core locations near the reconstructed values found in Phase I. In this sense Phase II represents a “ﬁne tuning” iteration of Phase I. Random primary energies will be selected from values in a range two orders of magnitude less than or greater than the Phase I energy. Random core locations will be selected from values in a range ±600 meters from the Phase I core. Once again a binary trigger list is generated and compared to the actual data’s binary trigger list with the same method utilized in Phase I. When acceptable matches are found the candidate primary energy, and core location are recorded. Once Phase II is complete an identical number of trial runs as was established in Phase I a reﬁned mean primary energy and core location is reported.

7.1.3 Phase III

Phase III of the MCCRRS reconstructs the arrival direction of the EAS. This is accomplished by building a list of the time stamps of each SD from the actual data, and subtracting the earliest recorded time plus one second (to avoid issues with inadvertently dividing by zero) in order to build a list of the time diﬀerences of when each SD experienced a local trigger from the actual data, with the earliest station reporting 1 sec and the latest station reporting the delta t of its raw reported time minus the earliest station’s raw reported time plus one second. Phase III scans all possible zenith and azimuth angles in the range of interest, 0o to 45o for zenith and 0o to 359o for azimuth, and builds a vector indicating the arrival direction for a particular zenith and azimuth combination. The script then builds a geometric plane that is perpendicular to the vector and resides far outside of the SD array. The distance from this plane to each SD that triggered in the real data is calculated, and assuming that an EAS travels very nearly at the speed of light, a travel time is found. This time correlates to a time stamp within the script, and is built into a list. This list is then

123 7.1. MCCRRS CHAPTER 7. MCCRRS normalized in a way identical to how the real data’s time diﬀerences list was built, with the earliest time being 1 sec and the latest being [(c/d)latest − (c/d)earliest] + 1 sec, and the two lists are compared item by item. If the two time values are within 75% agreement of each other, then a match is accepted. If each item of the generate list is accepted, then that arrival direction is recorded. This is repeated until the desired number of trials, set by Phase I, is completed and Phase III then produces a mean zenith angle and mean azimuth angle, respectively reported as degrees from zenith and unit circle degrees with due East being 0.

7.1.4 Phase IV

Phase IV uses the zenith and azimuth angle produced in Phase III as a fixed arrival direction, but allows for a randomly selected primary energy and core location. The sequence of calculations are the same as in Phase I and II with the only difference being the parameters that are held fixed and allowed to vary. These calculations will repeat, with a new random energy and core location selected for each one, until the number of trials matches the number of trials generated in Phase I. Phase IV produces a new estimate for the EAS primary energy and core location based on the arrival direction determined in Phase III. At the conclusion of Phase IV the arrival direction found in Phase III is reported as the final arrival direction estimate, and the primary energies and core locations found in Phases I, II, and IV are averaged, and these averages are reported as the final primary energy and core location estimates. Each phase generates its own standard error of the mean for its reported values and these errors undergo an uncertainty propagation to generate a final standard error of the mean to be reported with the final estimates.

124 7.2. RECONSTRUCTED PRIMARIES CHAPTER 7. MCCRRS

7.2 Reconstructed Primaries

Events that met the criteria outlined in the MOU for the last three quarters of 2017 and as of this writing the ﬁrst two quarters of 2018 were reconstructed and a set of tables comparing the TA MOU reconstructions against the MCCRRS reconstructions along with the standard error of the mean of the MCCRS values can be found in Appendix D. The 1st Quarter of 2018 failed to produce more than two events due to a low power issue within the CLF DAQ as the result of a 12 VDC battery reaching the end of its service life and failing to maintain suﬃcient charge throughout a 24 hour duty cycle. This issue was addressed in a June 2018 expedition to the TA CLF by members of HEA Lab, and no power issues have since arisen. Of the 25 total events 18 were successfully fully reconstructed with the MCCRRS, with seven events only partially reconstructed or only able to be reconstructed with cuts to the MOU triggering data. Of these seven exception events; the 1st Event of the 4th Quarter of 2017 was not a viable candidate as one of the triggered SDs near the center of the triggered cluster had an anomalous time stamp outside of the tolerances to be reconstructed by the script, which made for a problematic data set that the MCCRRS could not rectify. The 2nd Event of the 4th Quarter of 2017 failed to generate anything other than a course Phase I primary energy estimate. The 3rd Event of 4th Quarter of 2017 continued to fail to resolve a Phase IV estimate, resulting in a course estimation of primary energy and core location. The 3rd Event of the 2nd Quarter of 2017, the 2nd Event of the 1st Quarter of 2018, and the 5th Event of the 2nd Quarter of 2018 had to exclude a SDs near the edge of the triggering cluster that had an anomalous time stamps, and the 2nd Event of the 2nd Quarter of 2017 had an SD cut due to it not being adjacent to any of the other triggering SDs. The MCCRRS shows no signs of introducing a bias to its calculations when the values of the reconstructed parameters of core Northing, core Easting, zenith angle, and azimuth angle from this preliminary run is plotted against the reconstructed

125 7.2. RECONSTRUCTED PRIMARIES CHAPTER 7. MCCRRS

Figure 7.2: Plots of the preliminary reconstructed core Northing and Easting values, including absolute values, and zenith and azimuth angles versus reconstructed energy show no sign of a bias in reconstructed parameters as a result of the MCCRRS calculation methods. energy as is shown in Figure 7.2. Had the energy estimate, the ﬁrst estimate found and the kernel value used for ﬁnding several other estimate, introduced a bias in the calculation of the values of these parameters at trend of some sort would be expected to emerge in these plot. The random scattering of these plots strongly indicate that

126 7.2. RECONSTRUCTED PRIMARIES CHAPTER 7. MCCRRS no such trend exists. This will be confirmed as MOU data continues to arrive each quarter which will improve the statistics of these plots. Evaluating how the TA and MCCRRS reconstructions differ is handled by building a series histograms of the differences of reconstructed parameter values between the two reconstructions. These histograms are presented in Figure 7.3, where the MCCRS values are denoted with ‘hea’, and the TA values are denoted with ‘ta’. The parameters of core location and arrival direction show a fairly close match between the MCCRRS and TA reconstruction values with core location differences being less than the nearest neighbor distances between TA stations, and differences in zenith and azimuth angles being comparable to the evaluated stepping angle resolution utilized in the MCCRRS reconstruction. The estimates for primary energy show that the MCCRRS is estimating primary energies that are generally lower than the TA estimates by somewhere between one quarter and one half of a decade in energy. The reason for this lower primary energy estimate is not yet known, however the MCCRRS is not intended to reproduce preliminary TA MOU results, but rather make its own estimates from a process that utilizes multiple Monte Carlo techniques to avoid introducing a user, or calculation bias. Looking at the results of the energy fitting of Phase I and II of the 1st Event of the 3rd Quarter of 2017 (Figures 7.4 and 7.5) shows that for Phase I a zero fit is favored with the energy distribution of the zero fit producing a well defined distribution. For Phase II the fitting is dominated by scenarios with a mismatch of station triggers on the order of 20. The MCCRRS does not consider mismatches this large to constitute a valid reconstruction and focuses only on fits producing mismatches in the 0-5 range. Even with this quality control measure it can be seen in Figure 7.5 that the energy distributions of fits 0 and 1 are biased towards lower energies instead of being symmetric, and the distribution of fit 3 begins to diverge into two peaks. It may be that the wide spread of MCCRRS energy distribution is introduced in Phase II. The TA MOU to MCCRRS reconstructed

127 7.2. RECONSTRUCTED PRIMARIES CHAPTER 7. MCCRRS

Figure 7.3: Diﬀerences between the preliminary TA MOU EAS parameter reconstructions versus the MCCRRS reconstructions. Energy reconstructions show a bias towards lower energy estimates than the TA method. Fit values provided in Table 7.2

128 7.2. RECONSTRUCTED PRIMARIES CHAPTER 7. MCCRRS primary energy oﬀset, could be the result of the derived MIP scaling factor, or the probability of local trigger calculation utilizing bad assumptions that may be causing lower MIP densities to trigger than what is actually being utilized by TA for their reconstructions. More MOU data, or fuller disclosure of the operation of the TA SDs will be required to determine if that is the case.

Figure 7.4: Histograms of primary energy ﬁts for Phase I. Notice that the perfect match ﬁt is dominant and that the perfect match energy distribution produces a very symmetric Gaussian distribution.

Figure 7.5: Histograms of primary energy ﬁts for Phase II. At Phase II the number of station mismatches approaches the number of stations participating in the trigger, and the energy distributions are no longer symmetric.

As MOU event lists continue to be published the statistics of this percentage diﬀerence will continue to improve, which may lead to identifying the reasoning behind this energy estimation diﬀerence. It may be that the method of Phase II will need to be updated, or that Phase II may be dropped all together.

129 7.3. VEM AND MIP MATCHING CHAPTER 7. MCCRRS

7.3 VEM and MIP Matching

While it is not yet clear what the exact reason for the reconstructed energy disparity between the MCCRRS and TA MOU primary energy reconstructions may be, a possible solution may be from a yet to be implemented Phase V of the MCCRRS. Since the MCCRRS already calculates LDF VEM and MIP densities at the site of each SD, it would be fairly trivial to make energy, core location, and arrival direction reconstructions within the MCCRRS by matching the simulated VEM and MIP values to the calibrated values found at each SD from a real global trigger event. This matching would be similar to what was described in Phase III where a percent difference is calculated between the simulated and real value, and percent differences that fell within a designated threshold value would be tallied as a match. This Phase could probe primary energy, core location, and arrival direction simultaneously, or treat each parameter separately in a step by step progression like what is used in Phase I. Only results that produced an exact match of stations hit would be utilized in finding the final reconstructed parameter values. This Phase V has been written, but is at this time untested, and was not utilized in this dissertation work as the MOU does not currently provide the calibrated MIP values reported by the triggering stations. A future negotiated revision of the MOU may allow for this phase to be implemented. We also note that while the energy mismatch that results between the MCCRRS and the TA MOU reconstructions remain unexplained, a qualitatively similar mismatch is observed in studies done by other members of the Pierre Auger at the Tele- scope Array work group using an atmospheric Monte Carlo method. Quinn (2017) reports signal strengths measured at PA SDs to be 30-50 % lower than predicted by TA reconstructed parameters. This suggests that TA reconstructions may be systematically higher than those expected from TA data consistent with the MCCRRS results. Although a final analysis of these atmospheric Monte Carlo results are in progress, a detailed shower-by-shower comparison between TA station energy results,

130 7.3. VEM AND MIP CHAPTER 7. MCCRRS the MCCRRS, and these EAS Monte Carlo results are planned for the near future [100]. With the MCCRRS future researchers working on the Micro Array will be able to rapidly and independently generate TA and Micro Array EAS reconstruction estimates which may be used to expedite the process of direct data set comparison and allow for conclusions to be made about the possible origins of UHECRs from northern hemisphere observations. The MCCRRS also has the potential to remain a relevant data analysis tool beyond the Micro Array project as its method of calculations are general enough to be used for any sized water Cherenkov or scintillator panel type SDs in any array size or spacing conﬁguration.

131 Chapter 8

Supporting Work Related to the Pierre Auger Observatory

The following chapter outlines dissertation work that was relevant towards making contributions to the operation of the Pierre Auger Collaboration, both in its current and future conﬁguration, and towards the designing of the Micro Array. These projects do not ﬁt neatly into other chapters and do not contain enough depth to warrant their own chapters, so they are presented here as a miscellaneous compiling.

8.1 Wireless Communications Monitoring

The Pierre Auger Observatory (PAO) currently operates with a custom designed radio network of individual SDs relaying data and status information to Base Station Units (BSUs) which then sends the collected data to the observatory’s main DAQ. The group at CWRU is responsible for the critical task of monitoring the performance of this wireless communication system. Periodically throughout each day diagnostic and status data is collected by each BSU and compiled into a raw data log. This data includes:

• SDs with an ARQ7 error correction fault rate above 0.1%.

• Uplink/Downlink signal strength of each BSU.

• Maximum/Minimum temperatures recorded by BSUs.

132 8.1. COMMS MONITOR CHAPTER 8. SUPPORT WORK

• Fraction of ARQs per packet by BSU.

• Stations experiencing disconnections.

Automatic repeat requests (ARQs) are requests from the BSU to individual stations to resend data when the BSU detects an incomplete or corrupt data packet was received. The process of requesting and responding to ARQs creates delays in the normal operation of the observatory and could potentially cause an indeﬁnite hangup in data collection. In order to prevent this, the BSUs are programmed to make only seven resend requests, ARQ7, for any individual data packet. If the packet is not successfully received after seven requests then the data packet is dropped and lost in order to allow a resumption of normal operations. It is important to track both the rate of ARQs and ARQ7s to ensure that the observatory’s communication system is functioning properly. During communications performance data analysis it is sometimes necessary to vet the quality of the data against the status of individual SDs, BSUs, and the overall communications system during the period in which the data was gathered. This is done by transcribing the above raw data log into a log that describes the status of the array in terms of qualitative and quantitative parameters. Qualitative parameters include:

• Uplink/Downlink signal strength behavior within normal range.

• Uplink/Downlink signal strength abnormalities occurring in clusters.

• Station disconnections occurring in clusters.

Quantiﬁed parameters include:

• Percentage of ARQs against all packets sent.

• BSUs indicating ARQ percentages over 10%.

133 8.2. REMOTE SHIFT STATION CHAPTER 8. SUPPORT WORK

• BSUs indicating abnormally high or low temperatures.

• BSUs indicating higher than average ARQs

• Number of SDs that experienced disconnections.

The transcribed log is stored on a cloud drive and can be made available to any member of the PA collaboration in order to provide a tool to verify the quality of data from any particular date. A sample section of this data log is provided in Figure 8.1.

Figure 8.1: A Sample of the PAO communication system’s status log.

8.2 Remote Shift Station

As part of HEA Lab’s duties and obligations as members of the Pierre Auger Col- laboration it is necessary to periodically send personnel to Malargüe,Argentina in order to conduct FD monitoring shifts at the observatory. Tasks conducted during these FD shifts include monitoring the status of the FD stations while they are in operation, and ensuring that the instruments remain properly calibrated. While the personnel on a shift are the first people to flag any problems, they generally do not physically correct these issues on their own, but instead bring these issues with the relevant technicians on site who then mobilize to address them. Because the necessity

134 8.2. RSS CHAPTER 8. SUPPORT WORK for shift personnel to be physically co-located with the observatory is minimal, it is possible for a FD monitoring shift to be conducted at a remote location. There are strong motivations for conducting a monitoring shift remotely if it is possible. The cost of airline tickets, visa fees, processing passports and other international travel paperwork, and factoring in travel reimbursements, multiplied by the number of people sent on each shift along with the nearly annual recurrence of each collaboration member group’s shift obligation makes the FD monitoring shift duty financially burdensome. By comparison, a single hardware investment of the computers and peripheral equipment needed to build a mirror to the observatory’s FD monitoring shift station has the potential to mitigate future costs incurred by physically sending personnel to shifts, and so can save member groups a great deal of money over the course of only a few years. European members of the Pierre Auger Collaboration have already built and utilized remote shift stations and successfully conducted monitoring shifts with them. In 2014 HEA Lab at Case Western Reserve University procured the necessary hardware to build and operate its own remote shift station, known as the RSS. The RSS was constructed from guidance provided by collaboration colleagues who had already built and successfully ran their own remote shift stations. The RSS is virtually identical to these other remote shift stations with only minor differences due to hardware and software advancements that occurred between the building of the previous stations and the RSS. Challenges to bringing the RSS into operation were caused by the security requirements established to access the PAO control computers from a remote site. One particular challenge was obtaining a CERN grid certificate which validates the RSS computers as being vetted as trustworthy remote access sites. This requirement was established by the European members of the collaboration, most of whom are members of CERN. However, being a North American organization HEA Lab did not

135 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

have ready access to this certification, and cooperation with European representa- tives within the Pierre Auger Collaboration was necessary in order to obtain grid certification. Another security related challenge to bringing the RSS into operation came from building permissions in the firewalls of both the on site control computers and the RSS. To prevent unwanted eavesdropping both sites utilize a white list type firewall that prevents any and all access to the station computers except for the IP addresses that allow for the RSS to talk directly to the observatory and no other computers. Writing the appropriate firewall was trivial, but testing its efficacy requires special coordination between the RSS and the observatory in order to test communication between the two sites which will ensure that the RSS is capable of running a shift. As of this writing the RSS is fully built (See Figure 8.2) and configured and only requires a test shift, known as a shadow shift, to be run where a normal on site shift is conducted with the RSS operating while monitoring, but not participating in providing input to the FD telescopes in order to ensure that it is fully capable of handling a full shift without intervention or aide from the on site shift station. Pending funding approval this could be executed during any future shift and would certify the RSS as being fully capable of conducting FD monitoring shifts for PAO, and would serve as a valuable asset not only for HEA Lab, but also for any other North American member of the collaboration willing to rent time on the RSS in order to fulfill their shift manning obligations without the burden of international travel.

8.3 Monte Carlo Prototyper

During the early conceptualization phase of the Micro Array1 it was desirable to estimate expected detector trigger event rates for particular spacings and layouts of SDs at the TA CLF. To resolve this question a Monte Carlo type simulation program was

1See Chapter 5 for Micro Array

136 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

Figure 8.2: The RSS in ready to operate configuration located in AW Smith Room 11, Case Western Reserve University. written to produce estimates of detection rates for a small sized array. As work on this simulation script continued it was expanded to larger sized arrays that included both TA and PA type SDs, SDs that would observe and record local triggers but not participate in global triggering, determine what the energy thresholds of a particular array configuration would be, and what sort of demands these SD spacing distances would make on a radio communication system. The Monte Carlo Prototyper (MCP) script is very similar to the Monte Carlo Cosmic Ray Reconstruction Script (MC- CRRS) outlined in Chapter 7 in that it uses a lateral distribution function (LDF) calculation as a method of thinning in order to avoid unnecessarily resource and time consuming calculations, and utilizes the same chain of calculations to find vertical equivalent muon (VEM) or minimum ionizing particle (MIP) densities at each SD site and whether a local trigger would be generated2. The major differences between the two scripts is that the MCP generates and samples from a random population of cosmic ray primary energies that represents the cosmic ray energy spectrum at and

2See Chapter 7 for LDF and trigger calculation details

137 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

above “the knee”, does not include binary trigger list matching as it does not attempt to compare to real data sets, and utilizes an additional computational step of calculating the integration of the differential cosmic ray spectrum in order to determine detection rates. This script begins by prompting the user to designate the number of SDs they desire to include in the array, where they should reside on a test field of a certain fiducial area, and what coincidence levels are required to produce a global trigger. Al- though this script was initially intended to test Pierre Auger type SDs, the script has been expanded so that the user can also specify whether they want water Cherenkov tanks, or scintillator panels, what sizes these SDs should be, and if SDs should be included that do not participate in triggering, but still record observed coincidences. This makes this script a very useful and more generic tool for planning not just the Micro Array, but other future proposed SD based cosmic ray observatories as well. After the initial user inputs the script then generates a random population of cosmic ray primary energies to sample from. This is done by utilizing the differential cosmic ray energy spectrum outlined by Fowler, et al (2001).

" #ω(β−α) E α E 1/ω J(r) = Jk 1 + (8.1) Ek Ek

,where Jk is a normalization parameter chosen to ﬁt the form of the function to

15.3 real data, which is 10 eV, Ek is the energy at the cosmic ray spectrum “knee”, ≈ 2.0 × 1015eV, E is the primary energy of a particular cosmic ray, α is the power law index for energies less than Ek, which is -2.72, β is the power law index for energies greater than Ek, with the value of -2.95, and ω is the half-width, in decades, of the knee which observed cosmic ray data suggests has a value of 0.25 [119]. The

−2 −1 −1 −1 differential flux, J(r), is given in number of particles m sr s GeV . Random energies and flux values are selected and matched against the profile of the cosmic

138 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK ray spectrum, with values that are above the spectrum’s power law fit for a given energy being vetoed and values that lay within the spectrum being placed into a population of cosmic rays to be evaluated. This process runs until the cosmic ray primary energy population reaches a number value designated by the user. Once the cosmic ray sample population is built a cosmic ray primary energy is randomly selected to be evaluated. This particular sample is erased from the population list in order to ensure that repeated entries are not included in the simulation. On the test area a Northing and Easting value is selected at random, and then an extensive air shower (EAS) is simulated in a method nearly identical to the one used in the MCCRRS3. An LDF is propagated, a local VEM or MIP density is found for each SD, depending on SD type, a Poisson and Binomial Distribution is used to determine if there was a local trigger at each SD or not, and then the number of local triggers is compared to the user defined global trigger coincidence threshold. If a global trigger is produced, the primary energy of that event is recorded which will be used to determine the array’s energy threshold. The number of global triggering events versus the numbers of cosmic rays sampled will be used to determine the array’s detection rate. The determination of SD array detection rates is made by first integrating the Fowler differential cosmic ray energy spectrum. Simpson’s rule of numerical integration is utilized, integrating over energy, to produce a function representing the number of particles being generated over an area, per steradian, per second. Assuming observational reconstructions that utilize approximately one steradian in their evaluation, which is indeed the case with real data, the MCP is left with the number of cosmic rays generated per area, per second. The area is factored out by calculating the fiducial area utilized in the simulation, leaving the the total number of cosmic rays produced per second. The final step in determining detection rates is determining detection

3See Chapter 7

139 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK efficiency. This is a ratio of the number of cosmic rays that produced a global trigger versus the total number that were sampled within the range of primary energies that produced global triggers. Multiplying the total number of cosmic rays produced per second within that same energy range, determined from the integrated differential cosmic ray energy spectrum, by the detection efficiency finishes the calculation chain and leaves the user with the detection rate. By parsing the simulation’s results into energy decades detections rates can be determined for each decade of energy. The simulation also produces histograms graphi- cally showing the number of detected cosmic rays versus the number of those sampled as a function of energy, scatter plots illustrating an array’s detection ‘center of mass’, a histogram showing the number of triggered events by coincidence level, and can report the percentage of EAS cores landing withing a particular array’s “footprint”. Two proposed configurations of the Micro Array are currently being discussed by the Micro Array work group. One in which six PA SDs are co-located with six TA SDs in a 3x3 right triangle, and another in which seven PA SDs are arranged in a triangular pattern with 1.5 km spacing identical to PAO. The proposed configurations of the Micro Array, with each PA SD being co-located with a TA SD, was tested in the MCP with a minimum three-fold coincidence trigger and the following characteristics were found:

Array Geometry 6 SD Rectangular 7 SD Triangular Nearest Neighbor Spacing 1.2 km 1.5 km Global Trigger Energy Threshold ∼ 1 × 1016eV ∼ 1.5 × 1016eV Detection Rate 1016 < E < 1017 eV 0.00004/min 0.00004/min Detection Rate 1017 < E < 1018 eV 0.0002/min 0.0003/min Detection Rate E >1018 eV 0.0003/min 0.0003/min Detection Rate All E 0.0014/min 0.0010/min Detection Eﬃciency at Threshold 17.4% 19.9% 100% Detection Eﬃciency Threshold ∼ 3 × 1017 eV ∼ 2 × 1017 eV

Table 8.1: Comparing the global triggering rates and array eﬃciencies of the two proposed Micro Array conﬁgurations.

140 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

The trend in detection rates per energy decade seems paradoxical as higher energy events are rarer than lower energy events according to a decaying power law function with an index values of -2.97, so one would expect higher energy events to have a much lower detection rate than lower energy events. The estimated trend is consistent due to the fact that while lower energy events are extremely more common than higher energy ones, it is the case that the higher energy events cast a much larger LDF VEM density footprint around their core location and so are much more successful at producing global triggers than the lower energy events. From this analysis rates of UHECR showers at or above 1018 eV observed by the Micro Array may be ∼3 per week. An early version of the MCP was utilized in the lead up to an August 2016 expedition to the Telescope Array during which our team utilized a small muon telescope composed of four 0.1 m2 scintillator panels with only a little over two meters of separation between the panels arraigned in a square array to test the PA SD doublet at the TA CLF. The intent was to utilize the muon telescope next to the SD doublet to to act as an external trigger for the doublet, measuring local cosmic ray detection rates directly and to compare those measurements to what was being measured by the doublet. There was concern that due to the drastic diﬀerences of size and detection materials being used by the doublet and the muon telescope that the results may not correlate in a meaningful way. The relevant parameters for the telescope and doublet were entered into the MCP with the muon telescope generating global triggers while the doublet only observed and recorded local triggers without participating in global triggers. The results from the simulation, shown in Figure 8.3, suggested that in the energy range of interest the PA doublet and muon telescope would see better than 90% in coincidences with each other. This boosted the conﬁdence of the expedition team, and indeed the observed rates agreed with this prediction [100][120].

141 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

Figure 8.3: From the 2016 comparison between the PA doublet and the small muon telescope co-located at the TA CLF. The red ”Detected” counts denote triggers produced with the muon telescope, while the blue ”T2s with Coincidence” denote EAS witnessed by the PA doublet simultaneously while not participating in triggering.

The MCP generated Figures 8.4, 8.5, 8.6, 8.7 and 8.8. These figures provide a side by side comparison of different performance parameters of the two proposed Micro Array configurations. From this analysis of the two proposed Micro Array configurations it would appear that the triangular 1.5 km configuration would be more successful in recording larger number of events due to its higher detection efficiencies, and reaches 100% efficiency before the rectangular configuration. The Triangular configuration also has the advantage of producing data more readily comparable to that already generated by the PAO. However, the rectangular configuration has nearly identical rates and energy thresholds, and has the advantage of continuing direct local trigger comparisons with the TA SDs which would greatly enhance the statistics necessary for cross calibration of the two observatories. In colloquial terms; the

142 8.3. MONTE CARLO PROTOTYPER CHAPTER 8. SUPPORT WORK

Figure 8.4: From a trial run of 100,000 cosmic rays between the energies of 1015.8−1019 eV for the rectangular configuration (left), and 1016 − 1019 eV for the triangular configuration (right) showing the number of cosmic ray EAS detections versus total number of simulated cosmic rays generated. For the rectangular configuration energies below 1016 fail to trigger, while at ∼ 3 × 1017 eV and greater nearly all EAS landing in the fiducial area produce a trigger. For the triangular configuration energies below ∼ 1.5 × 1016 fail to trigger, while at ∼ 2 × 1017 eV and greater nearly all EAS landing in the fiducial area produce a trigger.

Figure 8.5: From a trial run of 100,000 cosmic rays between the energies of 1015.8−1019 eV for the rectangular conﬁguration (left), and 1016 − 1019 eV for the triangular conﬁguration (right), breaking down the number of triggering events that occurred as a function of number of stations triggered.

Triangular conﬁguration would allow for an “apples-to-apples” comparison of the Micro Array to the PAO, while the Rectangular conﬁguration would allow for an “apples-to-oranges” comparison of the Micro Array to TA. Further deliberation by

143 8.4. ASCII BOILERPLATE CHAPTER 8. SUPPORT WORK

Figure 8.6: From a trial run of 100,000 cosmic rays between the energies of 1015.8−1019 eV for the rectangular configuration (left), and 1016 − 1019 eV for the triangular configuration (right), showing the trend in triggering coincidence levels as the energy of cosmic ray primaries increases. the Micro Array work group will be needed to determine which on of these two configurations would best suite our science goals. The MCP served as a useful tool for prototyping the design to the Micro Array, and will continue to do so in the event that further changes to the Micro Array layout, or upgrades to a Milli Array are considered. With the capability of accommodating both PA and TA type SDs future collaborative work in integrating the arrays may continue to be processed by the program, and with minor tweaks any size of SD that is calibrated by either VEM or MIP may be included for consideration as well.

8.4 ASCII Boilerplate

A major part of the Auger Prime PAO upgrade is the additional hardware add-on to the SDs known as the Auger Scintillator for Composition II (ASCII). ASCII is a project that began test inception into the PAO in 2010 by introducing small 0.25 m2 scintillator panels mounted on top of a PA SD which piggy-backed one of the three SD PMT channels for data collection. in 2012 a larger 2 m2 prototype was installed with its own independent DAQ system. The purpose of this setup is to provide a

144 8.4. ASCII CHAPTER 8. SUPPORT WORK

Figure 8.7: From a trial run of 100,000 cosmic rays between the energies of 1015.8−1019 eV for the rectangular configuration. Lower energy triggers remain concentrated near the center of the Micro Array, as they do not produce a sufficiently high enough VEM density to produce a three-fold coincidence without straddling multiple SD positions. By contrast higher energy events successfully produce triggers outside of the array due to their much larger LDF footprint. mechanism to differentiate the muon component of an EAS from the lower energy electron portion. The basic principle is that lower energy electrons will be more likely

145 8.4. ASCII CHAPTER 8. SUPPORT WORK

Figure 8.8: From a trial run of 100,000 cosmic rays between the energies of 1016 −1019 eV for the triangular configuration. Lower energy triggers remain concentrated near the center of the Micro Array, as they do not produce a sufficiently high enough VEM density to produce a three-fold coincidence without straddling multiple SD positions. By contrast higher energy events successfully produce triggers outside of the array due to their much larger LDF footprint. to deposit most of their energy in the scintillator panel, which they encounter first, while the higher energy muons will be more likely to pass through and deposit most

146 8.4. ASCII CHAPTER 8. SUPPORT WORK of their energy in the water tank. In 2014 seven of these 2 m2 units were installed in the PAO infill and successfully demonstrated this principle as an array [121]. As part of HEA’s and The Colorado School of Mines’ effort to support the Auger Prime upgrade, in 2015 a working group assembled and installed an ASCII scintillator housing unit on top of the PAS SD at the TA CLF. This unit carried no instrumentation, but rather served as a boilerplate to test the efficacy of the upgrade’s intended mounting system, as well as the structural integrity of the hardware and its ability to withstand a windy and arid desert climate. The housing unit was light and weather sealed, and ventilated in order to dissipate hot gasses that built up inside of the structure throughout the hot days to the same specification that the intended ASCII units were intended to be built to. A sun shield structure was also brought to the location. The sun shield was intended to provide a thermal stand-off between the surface of the unit being heated by the sun and the scintillator housing itself, but was not install during this trip as no thermal instruments were being installed to record temperature cycling in order to quantify the thermal loads. As of this writing, the ASCII boilerplate is still in place on top of the PAS SD. It has proven that the mounting design is successful in holding the scintillator housing securely to the SD without obstructing access to the SD electronics, and that the housing itself is mechanically robust enough to withstand the high winds, dust, heat, rain, and even occasional blizzards (as occurred in January 2018) that frequent the climate that the observatories are built in. Beyond fulfilling the purpose of structurally testing the ASCII hardware, the ASCII boilerplate is noteworthy as the PAS SD is now potentially ready to receive the ASCII scintillator panels and electronics. Removing the current boilerplate and SBC to upgrade the PAS SD to an Auger Prime SD would be relatively trivial. Completing such an upgrade would allow the PA SD doublet at the TA CLF to continue its mission of providing cross calibration of the TA SDs and PA SDs into the era of Auger Prime.

147 8.4. ASCII CHAPTER 8. SUPPORT WORK

Figure 8.9: The ASCII upgrade prototype as installed at the PAO inﬁll (top image), and the ASCII boilerplate located at the TA CLF atop the PAS (further away) SD (bottom image).

148 Chapter 9

Conclusion

The ultimate scientiﬁc goal of experiments such as the Pierre Auger Observatory and the Telescope Array is to understand of the origins and nature of UHECRs. In principle a better understanding can be achieved by systematically observing the entirety of the sky to monitor UHECRs with primary energies at or above the GZK suppression limit, that is primary energies > 1019 eV, to record their arrival directions and statistically model their chemical composition as a function of primary energy. Should it be the case that an anisotropy is found from the arrival directions, and a composition analysis reveals either proton-like or iron-like cosmic ray particles dominating the highest energies of observed cosmic rays then astrophysicists and astronomers may be able to correlate the source of UHECRs to a particular astrophysical object which may then conﬁrm current hypotheses of UHECR generators. These hypothetical generators may be AGN emitting extremely energetic protons beyond our GZK horizon, SBGs emitting either high energy protons or iron-like particles either within or outside of our GZK horizon, or possibly rare, young, highly magnetic and extremely fast spinning collapsars within our own galaxy. The Pierre Auger and Telescope Array observatories have the capability of making these necessary observations, but are handicapped as each only observes approximately one half of the sky in hemispheres opposite to each other. They are not able to directly observe identical events, and comparing each collaborations’ data sets is

149 CHAPTER 9. CONCLUSION a challenge as each observatory uses very different types of surface detectors, as well as different techniques and hadronic interaction models when analyzing cosmic ray chemical composition. Both observatories have reported the emergence of possible arrival direction anisotropy, however one collaboration cannot confirm these findings for the other. The goal of this dissertation work was to establish that the discrepancies between Pierre Auger, and Telescope Array data could be addressed by establishing hardware and data analysis methods to provide cross calibration between the different types of detector instruments, and to build the infrastructure necessary to establish direct detection of EAS using the same surface detectors utilized by the two observatories at a single location. Thanks to the contributions made by the HEA group at CWRU as well as other members of the cross-calibration working group, asymmetric cross calibration of Pierre Auger and The Telescope Array has begun by characterizing the Telescope Array’s surface detector co-located with the Pierre Auger surface detector doublet at the Telescope Array CLF. Initial analysis shows that the TA SD does an excellent job of recording the muon component of EAS, and records timestamps of local triggers with an offset of about -1.098±0.1995 µsecs compared to the PA SDs. These results were used to analyze events recorded by both the TA SD and the PA South Type SD at the TA CLF, and found an upper bound to the cross calibration factor of 1.22 ± 0.34, and a lower bound of 0.77 ± 0.20. Cross calibration efforts will continue into the near future as coincidence data continues to be compiled, and will be enhanced by establishing an external trigger for the TA Local SD with the PA doublet, which will lead to final determination of the cross calibration factor between TA MIPs and PA VEMs. While cross calibration is being completed the next step in unifying the observations made by Pierre Auger and Telescope Array, that being direct detection of identical EAS, may begin. With the former RDA surface detectors being re-purposed

150 CHAPTER 9. CONCLUSION

to serve as a Micro Array the ground work has been set to co-locate Auger type surface detectors with Telescope Array surface detectors within the Telescope Array Observatory so the simultaneous and independent observations and reconstruction of EAS may be made. This Micro Array will utilize a robust off the shelf radio modem communication system to collect data from individual stations and relay those data packets to a central DAQ station which will record the Micro Array’s data and issue user commands to the individual stations as needed. This system has been found to be reliable in a field environment. The Micro Array has been analyzed for two different configurations using the MCP simulation to predict determine if the desired detection energy threshold and detection rates for observations involving UEHCR EAS energy and arrival direction reconstructions are obtained. Fro the six SD and seven SD configurations the values are predicted to be ∼ 1 × 1016 eV and ∼ 1.5 × 1016 eV for energy detection threshold, and about 2 and 1.5 global triggers per day for the rate of detection above this threshold with detection efficiency approaching 100% for primary energies above ∼ 3 × 1017 eV and ∼ 2 × 1017 eV respectively. The MCP may continue to be utilized as the Micro Array moves into the construction phase if array spacing or layout changes are needed to be considered, or if future arrays or the expansion of the Micro Array into a Milli Array are planned. Once the Micro Array is completed the MCCRRS program will be utilized as a computationally expedient means of estimating EAS primary energy and arrival direction reconstructions for the Micro Array, and for the Telescope Array as well based on data provided by the Telescope Array Collaboration as part of the Memorandum of Understanding agreement. Meanwhile, so long as HEA Lab remains a member of the Pierre Auger Collabora- tion it will continue to fulfil its duties towards enabling successful data collection, by continuing to monitor and log the health of the observatory communication systems, and by continuing to fulfil FD monitoring shift duties. With the completion of the

151 CHAPTER 9. CONCLUSION

RSS the shift monitoring task will be completed eﬃciently on the campus of Case Western Reserve University in Cleveland, Ohio, and other US members of the collaboration will have the beneﬁt of utilizing the RSS to execute their monitoring shift duties as well. With the currently underway upgrades of the Pierre Auger Obser- vatory to Auger Prime, as well as the upcoming expansion of the Telescope Array’s surface detector array to quadruple the current surface area covered, and the now established capabilities to cross calibrate and directly compare data sets collected by both observatories it seems clear that the study of UHECRs is entering a new era where the mystery of the origin of UHECRs may very soon be solved.

152 Appendix A

Acronyms

153 APPENDIX A. ACRONYMS

AGN Active Galactic Nuclei ARQ Automatic Repeat Request ASCII Auger Scintillator for Composition I BSU Base Station Units CLF Central Lasing Facility CTAL Coincident with TAL CTAG Coincident with TAG CWRU Case Western Reserve University EAS Extensive Air Shower FD Fluorescence Detector GZK Greisen-Zatsepin-Kuzmin Limit HEA The High Energy Astrophysics Lab (CWRU) LDF Lateral Distribution Function MCCRRS Monte Carlo Cosmic Ray Reconstruction Script MCP Monte Carlo Prototyper MIP Minimum Ionizing Particle MOU Memorandum of Understanding NKG Nishimura-Kamata-Greisen PA Pierre Auger PAC Pierre Auger Collaboration PAN Pierre Auger North Type SD PAO Pierre Auger Observatory PAS Pierre Auger South Type SD PMT Photo Multiplier Tube RSS Remote Shift Station TA Telescope Array TAG Telescope Array Global Trigger SD TAL Telescope Array Local Trigger SD TAO Telescope Array Observatory SBG Star Burst Galaxy SD Surface Detector UHECR Ultra High Energy Cosmic Ray VEM Vertical Equivalent Muon

Table A.1: Table of acronyms used in this thesis.

154 Appendix B

Digi XBee RF Modem Specs

Voltage Supply Range 2.6 to 3.6 VDC Sleep Current 2.5 µA Reception Current 40 mA 350 mA @ 21.5 dBm Transmission Current 640 mA @ 24 dBm 900 mA @ 30 dBm Frequency Range ISM 902 to 928 MHz 10 kb/s (LOW) RF Data bitrate 110 kb/s (MED) 250 kb/s (HI) Channels 10 hopping sequences share 50 frequencies Available channel frequencies 101 (LOW and MED), 50 (HI) Wireless bandwidth 1100 b/s Physical Dimensions 3.38 x 2.21 x 0.32 cm Weight 3 g Antenna Impedance 50Ω unbalanced Operating Temperature -40oC to 85oC Analog-to-digital converter (ADC) 4 10-bit analog inputs

Table B.1: Relevant Digi XBee RF Modem parameters as reported from the manu- facturer technical speciﬁcations [111].

155 Appendix C

Digi XBee Firmware Settings

Modem Conﬁguration DAQ Unit Product Family XBP9X-DM Function Set XBee Pro SX Firmware Version 9004 Broadcast Multi-transmits 3 RF Data Rate 250 kbps TX Power Level 30 dBm Unicast Retries A (Default non-zero valued hex code) Network Hops 7 Mesh Unicast Retries 1 Network Delay Slots 3 Serial Baud Rate 9600 Parity None Stop Bits One stop bit API Mode API Enabled without Escapes

Table C.1: Relevant XCTU Firmware settings for the XBee Modem DAQ Unit. All other settings are either default for this version of the ﬁrmware, or classiﬁed for network security reasons.

156 APPENDIX C. DIGI XBEE FIRMWARE SETTINGS

Modem Conﬁguration SD Unit Product Family XBP9X-DM Function Set XBee Pro SX Firmware Version 9004 Broadcast Multi-transmits 3 RF Data Rate 250 kbps TX Power Level 30 dBm Unicast Retries 5 Network Hops 7 Mesh Unicast Retries 5 Network Delay Slots 3 Serial Baud Rate 9600 Parity None Stop Bits One stop bit API Mode API Enabled without Escapes

Table C.2: Relevant XCTU Firmware settings for an XBee Modem SD Unit. All other settings are either default for this version of the ﬁrmware, or classiﬁed for network security reasons.

157 Appendix D

MCCRRS Reconstructions

158 APPENDIX D. MCCRRS RECONSTRUCTIONS

2017 Q2 Event 1 TA MCCRRS SEM Energy (EeV) 10.18 2.85 0.07 Easting (m) 1550 1929 141 Northing (m) -205 -36 145 Zenith (deg) 35.3 33 0.01 Azimuth (deg) 38.73 34 0.02 Event 2 TA MCCRRS SEM Energy (EeV) 7.57 3.31 0.33 Easting (m) -407 -503 137 Northing (m) 1667 2438 108 Zenith (deg) 22.23 20 0.0 Azimuth (deg) 102.83 103 0.08 Event 3 TA MCCRRS SEM Energy (EeV) 4.58 3.21 0.07 Easting (m) 1443 1294 74 Northing (m) -1212 -1812 62 Zenith (deg) 28.5 25 0.0 Azimuth (deg) 25.66 20 0.0 Event 4 TA MCCRRS SEM Energy (EeV) 3.3 0.59 0.01 Easting (m) -966 -2206 41 Northing (m) 279 -241 43 Zenith (deg) 5.41 40 0.03 Azimuth (deg) 202.2 99 0.0 Event 5 TA MCCRRS SEM Energy (EeV) 1.23 0.56 0.01 Easting (m) 1537 1430 35 Northing (m) -963 -1253 40 Zenith (deg) 30.59 53 0.01 Azimuth (deg) 55.96 104 0.02 Event 6 TA MCCRRS SEM Energy (EeV) 2.22 1.38 0.06 Easting (m) 836 1242 228 Northing (m) -431 -1182 216 Zenith (deg) 38.66 34 0.01 Azimuth (deg) 304.97 302 0.04

Table D.1: MCCRRS reconstructions for available 2ndQuarter of 2017. Event 2 included a local trigger from an SD that was not adjacent to any of the other triggering SDs and so was excluded from the reconstruction. Event 3 included an SD with a time stamp that was an extreme outlier too large for the MCCRRS to account for and so was excluded from the reconstruction. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS.

159 APPENDIX D. MCCRRS RECONSTRUCTIONS

2017 Q3 Event 1 TA MCCRRS SEM Energy (EeV) 4.84 1.32 0.06 Easting (m) -328 -144 283 Northing (m) 234 961 627 Zenith (deg) 31.94 23 0.02 Azimuth (deg) 280.52 278 0.07 Event 2 TA MCCRRS SEM Energy (EeV) 4.86 1.23 0.04 Easting (m) -8 -142 44 Northing (m) -1686 -2275 39 Zenith (deg) 25.37 20 0.0 Azimuth (deg) 77.8 100 0.0 Event 3 TA MCCRRS SEM Energy (EeV) 3.36 0.57 0.01 Easting (m) 1621 1659 29 Northing (m) -417 -544 58 Zenith (deg) 30.67 47 0.02 Azimuth (deg) 125.59 307 0.04 Event 4 TA MCCRRS SEM Energy (EeV) 4.96 1.85 0.05 Easting (m) 129 -600 194 Northing (m) 1548 1667 208 Zenith (deg) 39.92 31 0.01 Azimuth (deg) 302.2 309 0.03 Event 5 TA MCCRRS SEM Energy (EeV) 2.2 1.63 0.07 Easting (m) 1546 1683 77 Northing (m) 682 778 100 Zenith (deg) 36.33 24 0.02 Azimuth (deg) 95.4 93 0.07

Table D.2: MCCRRS reconstructions for 3rd Quarter of 2017. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS.

160 APPENDIX D. MCCRRS RECONSTRUCTIONS

2017 Q4 Event 1 TA MCCRRS SEM Energy (EeV) 2.46 Easting (m) -287 Northing (m) -33 Zenith (deg) 32.4 Azimuth (deg) 35.71 Event 2 TA MCCRRS SEM Energy (EeV) 4.93 5.8 0.02 Easting (m) 613 Northing (m) 512 Zenith (deg) 32.64 Azimuth (deg) 124.51 Event 3 TA MCCRRS SEM Energy (EeV) 8.37 5.77 8.07 Easting (m) 1797 1669.5 358 Northing (m) 874 543 304 Zenith (deg) 43.33 37 0.04 Azimuth (deg) 333.16 332 0.07 Event 4 TA MCCRRS SEM Energy (EeV) 1.64 1.01 0.03 Easting (m) -757 -1145 80 Northing (m) 1749 1830 93 Zenith (deg) 43.62 32 0.01 Azimuth (deg) 252.85 174 0.04 Event 5 TA MCCRRS SEM Energy (EeV) 5.72 0.55 0.01 Easting (m) -14 1068 128 Northing (m) -254 -505 62 Zenith (deg) 31.32 32 0.01 Azimuth (deg) 358.36 199 0.16 Event 6 TA MCCRRS SEM Energy (EeV) 8.04 5.30 0.19 Easting (m) -722 -1600 199 Northing (m) 1858 1936 201 Zenith (deg) 34.92 32 0.03 Azimuth (deg) 291.51 287 0.05 Event 7 TA MCCRRS SEM Energy (EeV) 3.46 0.66 0.01 Easting (m) 1797 1463 20 Northing (m) -466 -757 36 Zenith (deg) 25.57 28 0.01 Azimuth (deg) 74.57 59 0.03

Table D.3: MCCRRS reconstructions for 4th quarters of 2017. Event 1 was not reconstructable in the MCCRRS due to an extreme outlier time stamp at an SD near the center of the trigger cluster. Event 2 could not reconstruct beyond a course primary energy estimate. Event 3 failed to reconstruct at Phase IV and thus only produced a rough primary energy and core location estimate. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS.

161 APPENDIX D. MCCRRS RECONSTRUCTIONS

2018 Q1 Event 1 TA MCCRRS SEM Energy (EeV) 1.92 2.13 0.06 Easting (m) 1061 892 326 Northing (m) 395 1174 396 Zenith (deg) 35.83 25 0.0 Azimuth (deg) 305.79 303 0.05 Event 2 TA MCCRRS SEM Energy (EeV) 2.74 0.51 0.01 Easting (m) -875 -1117 55 Northing (m) -431 -1430 102 Zenith (deg) 44.53 43 0.01 Azimuth (deg) 273.44 276 0.03

Table D.4: MCCRRS reconstructions for 1st Quarter of 2018. Event 2 excluded an outlier SD with an anomalous timestamp that could not be rectiﬁed by the MCCRRS. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS.

162 APPENDIX D. MCCRRS RECONSTRUCTIONS

2018 Q2 Event 1 TA MCCRRS SEM Energy (EeV) 1.74 0.388 0.008 Easting (m) 1522 1362.6 22.9 Northing (m) 124 460.8 36.6 Zenith (deg) 28.57 35 0.013 Azimuth (deg) 246.08 238 0.025 Event 2 TA MCCRRS SEM Energy (EeV) 1.06 1.66 0.069 Easting (m) 1440 2201.8 90.4 Northing (m) 190 -20.2 68.6 Zenith (deg) 33.25 28 0.015 Azimuth (deg) 167.64 165 0.049 Event 3 TA MCCRRS SEM Energy (EeV) 3.13 0.947 0.045 Easting (m) -246 -1171 607 Northing (m) -37 -943 435 Zenith (deg) 40.82 40 0.021 Azimuth (deg) 322.09 328 0.027 Event 4 TA MCCRRS SEM Energy (EeV) 4.88 0.648 0.013 Easting (m) -88 -472 36 Northing (m) 1741 2003 28 Zenith (deg) 36.28 32 0.008 Azimuth (deg) 275.1 265 0.008 Event 5 TA MCCRRS SEM Energy (EeV) 2.01 0.685 0.015 Easting (m) -1381 -2133 28 Northing (m) 377 712 33 Zenith (deg) 18.77 25 0 Azimuth (deg) 337.73 20 0

Table D.5: MCCRRS reconstructions for 2nd Quarter of 2018. In Event 5 two SDs were excluded from the reconstruction as their timestamps were extremely outside of the coincidence range of the other participant SDs. All TA reconstructions are preliminary and are presented only for the purpose of evaluating the viability of the MCCRRS.

163 Bibliography

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