University of Nevada, Reno Automated Nightly Observations for the Long-Term Monitoring of KIC 8462852 a Thesis Submitted in Part

University of Nevada, Reno

Automated Nightly Observations for the Long-Term Monitoring of KIC 8462852

A thesis submitted in partial fulfillment of the Requirements for the degree of Master of Science in Physics

Jacob M. Fausett

Dr. Melodi Rodrigue/Thesis Advisor

May, 2019

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

JACOB MONROE FAUSETT

Entitled

Automated Nightly Observations for the Long-Term Monitoring of KIC 8462852

be accepted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Melodi Rodrigue, Ph.D., Advisor

W. Patrick Arnott, Ph.D., Committee Member

Mathew J. Tucker, Ph.D., Graduate School Representative

David W. Zeh, Ph.D., Dean, Graduate School

May, 2019

Abstract

The star KIC 8462852 has puzzled astronomers since its aperiodic fluctuations in brightness and long-term secular dimming were discovered in 2016. Based on that initial data from the Kepler mission, an international collaboration has sought to better understand these two phenomena. Over the past three years, a network of observatories has been monitoring the star nightly in order to see these fluctuations in real time and send alerts to the community for further observation. The research presented here is in an effort to contribute to the continuous monitoring of KIC 8462852 using the Great Basin Observatory.

Nightly observations are scheduled with ACP Observatory Control Software and an automated pipeline, using Python’s Astropy and IRAF (Image Reduction and

Analysis Facility) has been setup to process the images and perform photometry in order to quickly recognize a dimming event. Since March of 2018, our data show the largest such event since the initial Kepler data and is consistent with other observations during that time. It also confirms the chromatic nature of these events, suggesting that the material obstructing the stars light is optically thin. Additionally, the secular dimming has not been witnessed and conversely, the star has portrayed a slight rising trend over this period.

ACKNOWLEDGMENTS

First, I would like to thank my advisor, Dr. Melodi Rodrigue. She has supported and encouraged me throughout this entire process and gave me the confidence to do my research in a field that is not typically researched in our department but is a passion for both of us. Her door was always open when I had questions or concerns and she has been incredibly invested in my personal and professional development.

I would also like to thank Dr. Paul Neil and the entire physics department at the

University of Nevada, Reno for approving me to complete a thesis in astronomy and for their continued support throughout the process

Additionally, I would like to thank Dr. Tabetha Boyajian from Louisiana State

University and Dr. Jon Swift from the Thacher School in Ojai CA. Their support and mentorship throughout this process has been crucial to my success and growth within the field. I would not have been able to complete this work without their guidance and direction.

Finally, I would like to thank my wife and children for their continued support of my education, research, and completion of this thesis. It has been a long and challenging journey, but their continued encouragement has provided me with the drive to pursue my passion. iii

Table of Contents

Page

ABSTRACT i

ACKNOWLEDGEMENTS ii

TABLE OF CONTENTS iii

LIST OF TABLES v

LIST OF FIGURES vi

CHAPTER

1 INTRODUCTION 1

1.1 The Kepler Mission 1

1.2 Where’s the Flux? 3

1.3 Post Kepler Dips 6

1.4 Great Basin Observatory 6

2 THEORY 8

2.1 Literature Review 8

2.2 Photometry 20

3 METHODS 25

3.1 Instrumentation 25

3.2 Image Processing 27

3.2 IRAF, DS9, and Reference Stars 30

3.3 Python Automated Pipeline 36

4 RESULTS 40

4.1 2018 Light Curve 40 iv

4.2 Evangeline 42

4.3 Long Term Behavior 45

4.4 Pipeline and Future Work 46

5 CONCLUSIONS

5.1 Summary 49

REFERENCES 50

APPENDECIES

A IRAF 53

Installation 53

Configuration 55

Parameters 60

B Pipeline 65

Crontab 65

Tabby_API.bash 66

Tabby_API.py 66

Tabby_pipe.py 67

List of Tables

Table 1. Apparent magnitude for various astronomical objects 22

Table 2. Sample IRAF coordinate table 34

Table 3. Table of magnitude information produced by IRAF 34

Table 4. Reference star information from Bruce L. Gary 35

List of Figures

Figure 1. Kepler Field of View 1

Figure 2. Transit Light Curves 2

Figure 3. Where’s the Flux Light Curves 4

Figure 4. Fourier Transform of Kepler Data 8

Figure 5. Variability found in Kepler Data 8

Figure 6. Possible Companion star 9

Figure 7. Keck Image with Companion Star 9

Figure 8. LCO 2017 Events 12

Figure 9. Elsie Mosaic 13

Figure 10. LCO Elsie Zoom 14

Figure 11. GTC Event Depth vs. Average Depth 16

Figure 12. GTC AAC vs. Particle Radius 16

Figure 13. Archive Century Secular Dimming 19

Figure 14. Kepler Secular Dimming 19

Figure 15. Sloan Filters Spectrum 25

Figure 16. Johnson-Cousins Filters Spectrum 26

Figure 17. Calibration Frames 28

Figure 18. Raw Science Image 29

Figure 19. Reduced Science Image 30

Figure 20. IRAF Radial Plot 31

Figure 21. IRAF 1-D Gaussian 32

Figure 22. Annulus and Check Stars Image 33 vii

List of Figures

Figure 23. Bruce L. Gary Star Finder 35

Figure 24. 2018 Reference Star Plot 36

Figure 25. GBO 2018 Light Curve 40

Figure 26. LCO 2018 r’ Light Curve 41

Figure 27. GBO 2018 r’ Light Curve 41

Figure 28. GBO Evangeline Zoom 42

Figure 29. LCO Caral_Supe and Evangeline 43

Figure 30. LCO All Post-Kepler Dips 43

Figure 31. Evangeline Egress 44

Figure 33. 2018 Trend (g’ and r’ filters) 45

Figure 33. 2018 Trend (i’ and V filters) 46

Figure 34. Extinction Plot 47

Introduction 3.4 – The Kepler Mission Before discussing KIC 8462852 in detail, it is important to talk about the mission that from which its peculiar behavior was discovered. The Kepler spacecraft was launched on March 7, 2009 with a mission to explore the structure and diversity of planetary systems in a specific region of our galaxy in order to estimate these qualities throughout the Milky Way (Johnson, 2015). The spacecraft targeted a single field of view (FOV) (figure 1) and continuously monitored nearly 156,000 stars in this field to detect planetary transits (when a planet passes in front of a star and blocks some of its light) (Borucki et al., 2011).

At the time of writing this, 2338 planet discoveries by the Kepler mission have been confirmed (“Exoplanet Archive Planet Counts,” n.d.).

Figure 1. Shows Kepler’s field of view (FOV); which, crosses between the Cygnus and Lyra constellations. Depicts the array of 50x25 mm CCDs with 2200x1024 pixels each (Johnson 2015). https://exoplanetarchive.ipac.caltech.edu/docs/counts_detail.html 2

These planetary discoveries were made by identifying small variations in a star’s intensity that occurred on a periodic basis (transits). These transits are generally verifiable as planets, since they exhibit a regular period based on the planet’s distance from the star, and the amount of light blocked (depth) is also constant. Typical examples of these light curves are shown in figure 2 below.

Figure 2. This figure shows the first five plant discoveries made by the Kepler mission. It shows the depth of the light curves, the inclination of the planets orbit (how far from center), and the orbital period (Johnson 2015). The period is governed by Kepler’s third law

푎3 퐺(푀 + 푚) (1) = 푇2 4휋2 where 푎 is the ellipse semi-major axis, 푇 is the orbital period, 퐺 is the

푚3 gravitational constant (6.674 ∗ 10−11 ), 푚 is the mass of the planet, and 푀 is 푘푔∗푠2 the mass of the star (“Kepler’s Laws,” n.d.). Additionally, the size of the planet determines the amount the light curve dips during a transit. This is given by 3

2 푅푝 (2) 퐷푒푝푡ℎ = ( ) 푅푠 where 퐷푒푝푡ℎ is the percentage of light blocked, 푅푝 is the radius of the planet, and 푅푠 is the radius of the star (“Transit Light Curve Tutorial,” n.d.). The original

Kepler mission came to an end in 2013, when a second reaction wheel failed, and the craft could no longer remain fixed on the original FOV. Over the four- year mission, the Kepler spacecraft is responsible for discovering 4770 possible exoplanets (“Exoplanet Archive Planet Counts,” n.d.). Approximately half of these are still yet to be confirmed (“Exoplanet Archive Planet Counts,” n.d.).

1.2 – Where’s the Flux?

While most of Kepler’s discoveries were easily recognizable as exoplanet transits, the data for KIC 8462852 did not fit with any known theories. The behavior was so unusual that in January 2016, Dr. Tabetha Boyajian and the

Planet Hunters project titled a paper “Where’s the flux?,” as a clever play on the acronym WTF (T. S. Boyajian et al., 2016). Figure 3 shows the Kepler data light curve; which highlights the aperiodicity of the dips in addition to their highly irregular depth (T. S. Boyajian et al., 2016). Based on our equation above, even the largest known planet (radius = 2*radius of Jupiter) orbiting the KIC 8462852

(radius = 1.4*radius of the sun) would only block 2.2% of the light (Siegel, 2015)

(T. S. Boyajian et al., 2016). This is obviously much less than the roughly 22% dip that is seen in these plots, shortly after day 1500 of the mission. 4

Figure 3. Time-series montage of the Kepler mission data for KIC 8462852. B) shows the entire light curve for the roughly four-year mission, while c), d), e), and f) are zoomed in to show significant dips 5-10 as numbered in b). This was data obtained from “the ‘corrected’ Pre-search Data Conditioning” database for which a detailed analysis of its precision is given in (Christiansen et al., 2012) (T. S. Boyajian et al., 2016). While there were many possible explanations that were investigated in their paper, most of them were addressed and few possibilities remained.

Shortly before this paper was published, Jason Wright from Penn State

University offered one extremely remote possibility for the data they were seeing, 5 when he suggested they would have to consider the light was being blocked by some artificial structure surrounding the star, (Wright & Sigurdsson, 2016). Of course, everyone involved was skeptical; however, the data couldn’t rule it out.

From that point on, KIC 8462852 became known as “Tabby’s Star” or “Boyajian’s

Star” and known to popular science readers as, “The Alien Megastructure Star”

(Siegel, 2018).

This newsworthy hypothesis thrust the star’s research into the headlines and sparked huge interest from amateurs and professionals alike. Considering the fame of the star, and the fact that there would be a huge amount of follow up observation required to discover its true nature, Dr. Boyajian started a Kickstarter campaign for people to contribute to the cause. The campaign was extremely successful and to this point has raised over $107,000 from 1,762 supporters

(“Where’s the Flux?,” n.d.). This allowed ground-based observations to begin in

2016 using the Las Cumbres Observatory Global Telescope Network (LCO) (T.

S. Boyajian et al., 2016). LCO is a private network of telescopes placed strategically across the globe in order to offer continuous observing of targets.

Dr. Boyajian used the money to pay for observing time using this network and also invited other teams around the world to contribute their own data. This was the beginning of a world-wide collaboration to better understand the nature of

Tabby’s Star.

While looking for a project of my own, Dr. Boyajian invited me to join in their efforts. We communicate via “Slack”; which is an app that offers threads or

“Channels” based on desired topics. It also offers a robust private and group 6 messaging system for communicating and sharing data. LCO and GBO post regularly updated light curves, with occasional updates from other observatories, in the “Photometry” channel.

1.3 – Post Kepler Dips:

The 2018 ground-based observations provided a lot of insight into the behavior of Tabby’s star. The data eliminated some of the proposed explanations and offered better constraints on the system (Tabetha S. Boyajian et al., 2018). The prize information the data show, is the material blocking the light is optically thin, i.e., dust. This is evidenced by the wavelength dependence seen during the dips (Tabetha S. Boyajian et al., 2018).

While this information was used to eliminate the possibility of a megastructure surrounding the star, there was still two key pieces of information that needed further study. The first unknown: the source of the dust surrounding the star. Additionally, shortly after Boyajian (2016), another paper was published using archive data, and showed that the star also exhibited long-term secular dimming (Schaefer et al., 2018). This could still not be explained and Boyajian

(2018) did not offer any further constraints on the topic. Further observations would be needed to determine explanations for these two remaining puzzles.

1.4 – Great Basin Observatory:

The Great Basin Observatory (GBO) is located in the Great Basin National

Park and is operated by a cooperative partnership. The cooperative includes: 7

Great Basin National Park, Great Basin National Park Foundation, University of

Nevada, Reno, Western Nevada College, Concordia University, and Southern

Utah University. Even though the telescope is not particularly large (0.7m), the fact that it is located in an “International Dark Sky Park” makes it an excellent research grade facility (“Astronomy - Great Basin National Park (U.S. National

Park Service),” n.d.).

The Tabby’s Star project was a perfect opportunity to utilize the newly built

(GBO) in order to immediately contribute to an ongoing research project. The unique nature of the GBO and its connection with the University of Nevada,

Reno, allowed access to a research quality telescope as well as an opportunity to educate the partner institutions about how to take advantage of its capabilities.

The flexibility of the GBO schedule and current user demand made this site an excellent option to attempt nightly observations. Because astronomy is so dependent on the weather, it is desirable to have multiple sites in different locations to avoid missing a night of observing and consequently, missing a dimming event. By utilizing the GBO, we were able to provide a project for students wishing to use the observatory, now and in the future, as well as contribute to the alert efforts should any event be witnessed. The GBO has fulfilled this goal perfectly as we have helped provide coverage during LCO downtime and also provided data to confirm events witnessed by other observatories.

Theory

2.1 – Literature Review:

KIC 8462852 is a main sequence F3 star with V = 11.7 magnitude, located

1470 light years away in the Cygnus constellation (Tabetha S. Boyajian et al.,

2018) (Collaboration et al., 2018). It is rotationally variable with a base period of

.88 days shown in Figure 4 and an irregular period of 10 – 20 days seen if Figure

5 (T. S. Boyajian et al., 2016). It is speculated that the longer period could

Figure 4. Figure 5

Figure 4. (Above) Shows the Fourier transform of the Kepler data and labeled with the harmonic numbering (T. S. Boyajian et al., 2016).

Figure 5. (Right) Shows a series of zoomed in plots with the base period of .88 days visible in each of the plots and the 10-20 day period visible in all but C) (T. S. Boyajian et al., 2016). just be the variability of the faint M-type star seen in Figures 6 & 7. Observations of KIC 8462852 obtained with the United Kingdom Infrared Telescope (UKIRT)

(Figure 6) were asymmetrical; which led to the belief there could be a companion. This was confirmed with the Keck image seen in Figure 7 (T. S.

Boyajian et al., 2016). Their proximity prevents Kepler from resolving the two. 9

Figure 6. (Left) “UKIRT image for KIC 8462852 and another bright star for comparison, showing that it has a distinct protrusion to the left (east). For reference, the grid lines in the image are 10” × 10” (arcsec x arcsec). The color coding is logarithmically scaled” (T. S. Boyajian et al., 2016).

Figure 7. (Right) “Keck AO H-band image for KIC 8462852 showing the companion was detected with a 2” separation and a magnitude difference ∆H = 3.8. The color coding is logarithmically scaled” (T. S. Boyajian et al., 2016). In Boyajian (2016), it was unclear whether the M-dwarf star was a physical binary or a visual binary but was determined to not produce a measurable effect on the aperiodic dips, even if it was physical. Further observation by another team, reported that the proper motion of the M-dwarf is similar to the proper motion of Tabby’s Star but distinctive enough to claim that it is not a true physical binary system (Clemens et al., 2018). They measured the proper motion of

Tabby’s Star to be 45 km s-1 along equatorial position angle (EPA) ~260° while the M-dwarf is traveling 29.4 km s-1 along EPA 229.4 ± 2.5° (Clemens et al.,

2018). This shows the M-dwarf could be responsible for the 10-20 day periodic fluctuations as discussed in (T. S. Boyajian et al., 2016); however, it confirms there is no physical connection and therefore cannot be transferring material to

Tabby’s star and contributing to the aperiodic dips.

In an attempt to discover the nature of the aperiodic and asymmetric fluctuation in brightness exhibited by Tabby’s Star, Boyajian (2016) proposed 10 several possible explanations and discussed how they fit the data. They first examined instrumental and data reduction artifacts but found no evidence that could explain the data and therefore concluded that the light curve was

“astrophysical in origin” (T. S. Boyajian et al., 2016).

Next, they examined the possibility of intrinsic variability and looked at several possibilities that could explain the light curve. They explored the possibility of Tabby’s star being a R Coronae Borealis (RCB) type variable which are “highly evolved F-G supergiants”; which have light curves that exhibit irregular and deep dips (Clayton, 2002) (T. S. Boyajian et al., 2016). These dips are associated with “clouds that obscure the photosphere” and typically show a rapid decrease in flux and a gradual rise as the clouds dissipate. However, the time scale for Tabby’s Star dips did not match the typical times for RCB stars and the dip near day 800 of the Kepler data showed a gradual decrease in flux; which was not consistent with an RCB dip. Additionally, the radial velocity and luminosity spectral signatures for Tabby’s Star were not consistent with the signatures of a supergiant (T. S. Boyajian et al., 2016).

Another intrinsic variability that was explored was the possibility of the star producing an excretion disk that is seen in Be stars. This was ultimately dismissed due to the lack of IR excess and H emission that is usually seen in

Be stars. Additionally, it was determined that the temperature of KIC 8462852

(푇푒푓푓 = 6750 퐾) was too cold for this possibility (T. S. Boyajian et al., 2016). As for extrinsic variability, they considered the possibility of the companion M-dwarf star producing an accretion disk, but determined the maximum effect this could 11 have on the light curve would be ~30 mmags (millimagnitudes); which is not substantial enough to explain the data (T. S. Boyajian et al., 2016).

Boyajian et al. (2016) also discussed the characteristics that could be expected from occultations of a few different varieties. One scenario they discussed was, occultations by clumps of material leftover from collisions and the break-up of one or more massive exocomets. The idea of a collision in an asteroid belt and the aftermath of a giant planetary impact was also considered; however, this was challenged by the lack of IR excess that should be present under those circumstances. The strongest evidence for the exocomet scenario, is that the dip around day 1500 was produced by the same material that caused the day 800 dip and was sheared out over the approximately 750-day orbital period. Two challenges to this are, one, the unexplained nature of the smaller dips around day 140 and day 260 and second, that it did not repeat 750 days later (T. S. Boyajian et al., 2016).

The leading explanation in (T. S. Boyajian et al., 2016) was the dips were caused by occultation from the “break-up of one or more massive exocomets (or planetesimals on comet-like orbits).” They provided constraints on this theory and stated that the” original body may have been in excess of 3 × 1021 grams

(only ∼0.3% the mass of Ceres, and perhaps ∼100 km in diameter).”

Additionally, they suggest if these remnants were disrupted by the nearby M- dwarf star, it could have triggered the irregular orbit and flung the material towards Tabby’s Star; however, this is very unlikely given the more recent proper motion paper, (Clemens et al., 2018). 12

The follow up observational data reported in Boyajian et al., (2018), provided even more insight to the nature of these short-term dipping events.

Regular observations began in March 2016; which were funded by the

Kickstarter campaign mentioned above and acquired using the LCO network of telescopes. The first “significant” dipping event was observed in May 2017 and a total of four dips were detected over a roughly 160 day period (Tabetha S.

Boyajian et al., 2018). These are shown in Figure 8 below. As part of the

Kickstarter campaign, it was decided to allow contributors to vote on names for any dips that were discovered. The first of these dips was given the name Elsie and was also the first successful such observation to trigger an alert and allow other observatories to begin their own observations. The result of this extremely successful trigger is seen in Figure 9.

Figure 8. “Time-series photometry of KIC 8462852 in the r’ band from 2017 May through December showing the Elsie dip family. Each point is a daily average from the LCOGT 0.4 m stations indicated in the legend. Near the midpoint of Skara Brae, short-term variability seen over a few hours is indicated as open blue points” (Tabetha S. Boyajian et al., 2018). Spectral response curve for r’ can be found in Figure 15. 13

Figure 9. Time-series photometry for the first post-Kepler dip. Each plot is from a different observatory and is normalized using data from just before the dips. This plot also shows the first evidence that the light is not blocked uniformly throughout the visible spectrum (chromatic) (Tabetha S. Boyajian et al., 2018). The Boyajian (2018) dips provide excellent confirmation of LCO data by having so many observatories witness the event and also offered the first 14 evidence for the chromatic nature of these dips. Clear evidence of this is seen in

Figure 10, which zooms in on the Elsie dip and provides direct verification of the wavelength dependence. In addition to the results in Boyajian (2018) two separate papers were published around the same time and provided targeted constraints on the chromatic nature of the dips and the overall data obtained in

2017.

Figure 10. Zoomed in plot of the Elsie dip from the LCO network. Clearly visible variation in the depth between the B, r’, and I’ filters (Tabetha S. Boyajian et al., 2018). In (Deeg, Alonso, Nespral, & Boyajian, 2018), they use Gran Telescopio

Canarias (GTC) long-slit spectrograph data, obtained based on alerts from LCO, to carefully examine the color dependence during the Elsie family of dips. Deeg et al. (2018) uses a term they call the Ångström Absorption Coefficient (AAC) as opposed to Ångström Extinction Coefficient. They use results from (Alonso,

Rappaport, Deeg, & Palle, 2018); which showed that an optically thin body of 15

uniform optical depth 휏푛, covering a fraction f of the star’s surface area will produce a dimming depth of

퐷푛 = 휏푛푓 (3) at wavelength 휆푛. For dust-like particles with an extinction cross-section 휎푒푥푡,

Alonso et al. (2006) assumes wavelength dependence of the form

휎 ∝ 휏 ∝ 휆−훼 (4) leading to

−훼 퐷푛 ∝ (휆푛) (5)

Deeg et al. (2018) relates modelled depths 퐷푚표푑,푛,푖 at 휆푛 to reference depths 퐷푤,푖 at 휆푤 by

퐷 + ∆ 휆 −훼 (6) 푚표푑,푛,푖 = ( 푛 ) 퐷푤,푖 + ∆ 휆푤 where 푛 represents the five bands from GTC data, 𝑖 is the individual nights of observing, ∆ is an offset to account for uncertainties in normalization across the bands, and 퐷푤,푖 is obtained from the average depth across all bands on a given night. Figure 11 shows the measured depth vs average depth for each of the five

2 bands. They resolve 퐷푚표푑,푛,푖 and perform a 휒 minimization for

2 (퐷 (훼, ∆) − 퐷 ) (7) 휒2 = ∑ 푚표푑,푛,푖 푛,푖 휎2 퐷푛,푖 where, 2 is the error for each observed depth . They find a numerical 휎퐷푛,푖 퐷푛,푖 solution for the free parameters 훼, ∆, and 휆푤 with 휆푤 = 709푛푚 (weighted mean of the five bands) as an initial value and calculate an AAC of 훼 = 2.19 ± 0.45.

Using the relationship between the AAC and material cross-section shown in 16

Figure 12, they calculate a particle radius for the dust causing the short-term dips to be between 0.0015-0.15 휇m (Deeg et al., 2018).

Figure 11. “Plot of the measured depths 퐷푛 in the five colour-bands, indicated by the same colours as in Fig 2, versus the white (average) depths 퐷푤, for the 14 pointings that were analysed. The straight lines indicate the joint-fit to all bands, with the best common value for the AAC of 훼 = 2.19” (Deeg et al., 2018).

Figure 12. “Calculated absorption Ångström exponents α as a function of particle size for four different minerals. For each mineral we used a log- normal particle size distribution, where 푅푚 is the median particle size in the distribution. The two grey horizontal lines denote the ±1휎 bounds to the Ångström exponent obtained from the GTC observations.” The plots for these materials were calculated from the materials’ cross sections at the average wavelength between the two bluest and reddest bands (Deeg et al., 2018). 17

In (E. Bodman, Wright, Boyajian, & Ellis, 2018), they reported similar results for the dust using the LCO data; which they reported to be < 0.5 휇m and further cited the dust must be newly formed, as particles < 1 휇푚 would be blown out of the system by the star.

There were a few major results that came out of Dr. Boyajian’s 2017 observing campaign. First, this was the first crowd-funded effort to study an astronomical object. They also presented the “Elsie family of dips”; which was the first real-time detection of dips for Tabby’s Star and was able trigger world- wide follow up observations. Finally, the data show that the dips are chromatic and suggest an occultation of an optically thin dust (Tabetha S. Boyajian et al.,

2018). They also place constraints on the mean particle size; which they calculate to be ≪ 1 휇m and which was further confirmed by the results in (Deeg et al., 2018).

(E. H. L. Bodman & Quillen, 2015) investigates the possibility of a large body of comet-like objects in order to explain the short-term dips seen in the

Kepler data. They point out that while a single comet is not capable of causing the larger dips, it is possible that a “series of large comet swarms” could in fact provide a good fit for some of the dips seen in the Kepler data (E. H. L. Bodman

& Quillen, 2015). Thompson (2016) found that this could be a plausible explanation for some of the larger dips seen in the Kepler light curve and suggest this would require “total amount of dust comparable to ~30 Comet Haleys”. They also placed constraints on the circumstellar dust surrounding Tabby’s Star,

−6 considering three hypothetical geometries. They report ≤ 3.0 × 10 M⊕ (M⊕ = 18

−3 mass of the earth) of dust at 2-8 AU; ≤ 5.6 × 10 M⊕ out to a radius of 26 AU; and ≤ 7.7 M⊕ within 200 AU. They claim, this makes the possible planetary collision explanation from Boyajian (2016) highly unlikely (Thompson et al.,

2016).

The exocomet hypothesis is modeled further in (Wyatt, van Lieshout,

Kennedy, & Boyajian, 2018). They suggest “a scenario in which one massive exocomet fragmented into multiple bodies which, due to their slightly different orbits, are now at different longitudes but close to the orbit of the original parent body.” They assert the secular dimming could be caused by “Keplerian shear” of the large gravitationally bound dust and “dust recently released from the fragments is responsible for the short duration dips” (Wyatt et al., 2018).

Even with the possible explanation for the secular dimming reported in

(Wyatt et al., 2018), there is still no consensus as to true nature. The century long archive data can be seen in Figure 11 (Schaefer, 2016). Further analysis of the Kepler data in (Montet & Simon, 2016) showed a decline in flux of approximately 3% over the four year mission. They reported the stars flux fading approximately 0.9% over the first 1000 days of the mission, then a sharp decline

(dropped by more than 2%) over the next 200 days (Montet & Simon, 2016).

This was confirmed using GALEX data over that same time period (Davenport et al., 2017). These results can be seen in Figure 12, where they show the GALEX and Kepler data (Davenport et al., 2017). 19

Figure 13. “The 5-year binned DASCH light curve of KIC8462852 (large blue diamonds). The star shows highly significant fading from 1890 to 1989. The averaged light curves for all 12 check stars is displayed, with a small vertical offset, in the figure with grey squares. The thin line is a simple linear trend connecting the two end points with a slope of 0.203±0.032 mag/century, while the thick line is the chi-square fit result with a slope of 0.164±0.013 mag/century.” Data obtained from the “archival photographic plates at Harvard” (Schaefer, 2016)

Figure 14. “Comparison of the 2011 and 2012 fluxes for KIC 8462852 as measured by GALEX (blue circles), with the Kepler FFI data shown in Montet & Simon (2016) as reduced with the new “f3” package from Montet et al. (2017) for comparison (grey squares). The amplitude of variability over this time window is nearly identical between the two surveys.” (Davenport et al., 2017) 20

2.2 – Photometry:

Astronomical photometry is the science of measuring the electromagnetic radiation (EMR) produced by astronomical objects. In this case, we are performing stellar photometry; i.e. studying the light from stars. Our main goal is to accurately determine the amount of the star’s EMR reaching us here on Earth.

EMR is given off in photons, and they have a definite energy; which is related to their wavelength and given by

ℎ푐 (8) 퐸 = 휆 where 퐸 is the energy of the photon, ℎ is Plank’s constant, 푐 is the speed of light, and 휆 is the wavelength. We are attempting to very precisely count photons using a detector to get a sense of the EMR for our source. In this case, we are using a charge-coupled device (CCD), at the Great Basin Observatory to collect photons and measure the flux (power/area). The physical cgs units of flux for a particular wavelength, 휆 are given by

−1 −2 −1 푓휆 = 푒푟푔 푠 푐푚 Å (9)

(Romanishin, 2006). When a photon hits a CCD pixel, it produces a charge

(electron) which is converted to Analog-Digital Units (ADU’s or counts). This conversion has some noise associated with it and additionally, not every photon will produce an electron. This is determined by the quantum efficiency (QE) which is simply

푁퐷푒푡푒푐푡 (10) 푄퐸 = 푁푖푛 21

where 푁푖푛 is the number of photons incident on the detector and 푁퐷푒푡푒푐푡 is the number of detections (Chromey, 2016). There is also a gain for each detector which relates the counts to electrons. In our case, the STX-16803 has

9푒− 푔푎𝑖푛 = . (11) 퐴퐷푈

So, for every 9 electrons produced by photons, we get one count. Because the gain and QE for a given CCD is constant, the detection and conversion process is also constant, and it can be said that counts are directly proportional to the photons. Each pixel also has a constant size; therefore, when we want to measure flux for some set of wavelengths in terms of power per area, what we are actually measuring is

푓 = 푐표푢푛푡푠 푠−1푝𝑖푥푒푙−2 (12) and this is directly proportional to photons per second per area.

Often times, when discussing observational astronomy, we talk about apparent magnitude. This is really a difference of magnitudes; which is related to a ratio of fluxes. The equation for apparent magnitude is given by

푓1 푚1 − 푚2 = −2.5log ( ) 푓2

or (13)

푓1 = 10−0.4(푚1−푚2) 푓2 where 푚1 , 푚2 are the magnitudes of two objects, and 푓1 , 푓2 are their respective fluxes. Since this is a negative logarithmic equation, the resulting magnitudes are lower for brighter objects and higher for dimmer ones, as seen in Table 1. 22

The system was originally standardized by choosing the magnitude of Vega to be

0.0 then the equation becomes

푓1 (14) 푚1 = −2.5log ( ). 푓푉푒푔푎

Table 1 below shows the inverse relationship between apparent magnitude and brightness, due to the negative logarithm in the equation (Romanishin, 2006).

Table 1. “Apparent V magnitude for a broad range of sources (Romanishin, 2006).

The apparent magnitude equation governs the relationship between magnitude and flux. What we really observe is the flux, and that’s what is measured and reported in the plots here. This project uses circular aperture photometry to measure the flux. An aperture radius, r1 is chosen for the target star based on its point spread function in the image. Additionally, a sky annulus 23

with an inner radius r2, and an outer radius r3 is chosen and should contain an area of the sky that is considered dark. This makes it possible to quantify the atmospheric EMR detected. We can then correct for this by subtracting it from the target flux. The area of the sky annulus is just

2 2 퐴푠푘푦 = 휋푟3 − 휋푟2 . (15)

If 퐶푠푘푦 is the total sum of counts inside radii r2 and r3 , the sky value per pixel is

퐶푠푘푦 (16) 푆푘푦 = . 퐴푠푘푦

This value is then subtracted from every pixel inside the target aperture and the target flux 푓푇, is given by

2 퐶푡푎푟푔푒푡 − (푆푘푦 ∗ 휋푟1 ) (17) 푓푇 = 2 푒푥푝 ∗ 휋푟1 where, 퐶푡푎푟푔푒푡 is simply the total counts inside the target aperture and 푒푥푝 is the exposure time. This leaves us with a target flux in units

−1 −2 푓푇 = 푐표푢푛푡푠 푠 푝𝑖푥푒푙 (18) as expected from above; however, some atmospheric effects have now been removed.

The reported fluxes in plots produced by this pipeline are relative fluxes and normalized to some baseline. The term “relative” is used because the flux data are calculated from a ratio of the target flux and an average from some chosen reference stars. For this project, we choose three check stars denoted as c1, c2, and c3; which are close to the target and similar in magnitude and color.

The relative flux, 푓푟푒푙 is given by 24

푓푇 (19) 푓푟푒푙 = 푎푣푔(푓1, 푓2, 푓3) where 푓푇 is the measured flux from our target, and 푓1, 푓2, 푓3 are the measured fluxes for c1, c1, and c3 respectively.

By using stars in the same FOV, many atmospheric effects can be reduced. Prior to the large FOVs common to CCD’s, astronomers were required to alternate between target images and standard star images and hope that the effects from a change in time and atmospheric conditions were minimal. Here, we are observing the target and references at the same exact time and with a very small change in path through the atmosphere. This greatly increases the precision for ground-based observing.

Methods

3.1 – Instrumentation

Data from the GBO are produced using a 0.7-meter Planewave CDK700 telescope and imaged with the SBIG STX-16803 CCD. The data are taken with

1X1 binning for maximum resolution, resulting in 4096 x 4096 images. An observing plan has been setup for scheduling with a program from ACP

Observatory Control Software, called DC-3 Dreams. The plan takes 5 images for each of the 5 filters selected, producing 25 images. This is then repeated 3 times to provide a good chance of completing one full set of filters in case any weather issues arise. If the entire plan is completed, 75 images are obtained, containing

15 images from each filter. For the majority of the 2018 data, a mixture of two standard photometric systems are used. The g’, r’, i’, and z’ from the “Sloan” filter set, as well as the V filter from the “Johnson-Cousins” filters. The spectral response curves for these sets is shown in Figures 13 and 14.

Figure 15. Transmission spectrum for the Sloan filter set (“Astrodon Photometrics Sloan Filters – Astrodon,” n.d.). 26

Figure 16. Transmission spectrum for the Johnson-Cousins filter set (“Astrodon Photometrics UVBRI Filters – Astrodon,” n.d.). After each image is obtained, DC-3 adds the data to the observer’s Dropbox folder.

The STX-16803 CCD is capable of cooling the detector -50° from ambient temperature for optimal performance and noise reduction. For summer observing the temperature set point for the cooling system is generally at -25° C.

During winter this is lowered to -35° C. By cooling the chamber the noise associated with detections can be reduced. During the 2018 campaign

Calibration frames (Bias, Dark, and Flat frames) were taken every 2-4 weeks.

This generally provided consistent calibration coverage for the two temperature periods. This pipeline has been setup to only choose calibration frames that match the cooling conditions for the science image to ensure similar detector performance. It then chooses the date of calibration frames closest to the science image date. 27

3.2 – Image Processing

Before any reliable information can be taken from the GBO images they have to be calibrated to remove instrumental artifacts. This requires the use of master bias, master dark, and master flat calibration frames.

Bias frames are images produced by taking a 0 second exposure. The resulting image will have to be subtracted off of every other calibration frame, as well as the science image (Chromey, 2016). The bias (Figure 15) contains the noise and gain associated with reading the CCD detector and is present in every image from the camera. A master bias is produced by taking the median values from a number of individual frames.

A dark calibration frame is an image taken with a similar exposure time as the science image but with the shutter closed so it does not contain any light.

After the bias is subtracted from the dark it leaves the values associated with heat that accumulates over the exposure time (Chromey, 2016). The dark is divided by the exposure time to yield units of counts per second; which is generally linear and can be applied to varying exposure times. Similar to the bias, a master bias is created by taking the median from individual frames. In addition to reducing noise, this mitigates the effect of any cosmic rays that might have been detected during the calibration exposure.

Finally, a flat frame is used to determine how light is affected by the system and recorded by the CCD. This contains the artifacts that arise from things like dust, optical path alignment, and vignetting (Chromey, 2016). The final master flat is produced after removing the bias and correcting for dark 28 current in each of the individual frames. Just as with the bias and dark, the master is then produced using the median values. The master is then normalized by dividing the mean value for the entire image.

Figure 17. The master bias (top left) the signal produced from reading the CCD. There are visible hot regions along the left and right edges from bottom to top. Additionally, there are noticeable vertical lines (read-lines) from reading the pixel counts. The master dark (top right) has the bias removed and is given in counts per second. The master flat (bottom) has been bias and dark corrected, as well as normalized. The donut shape dark spots are the result of dust on a mirror or filter. There is also noticeable vignetting from the hot spot around (1900, 1900). 29

A final calibrated image is obtained by

푟푎푤−푏푖푎푠−(푑푎푟푘∗exp) 퐹𝑖푛푎푙 = . (20) 푓푙푎푡 where the 푟푎푤 is the science image from GBO, 푏𝑖푎푠, 푑푎푟푘, and 푓푙푎푡 are the master calibration frames, and exp is the 푟푎푤 exposure time. The numerator removes the bias and dark signal from the science image, while the denominator corrects for the artifacts caused by the optical system and seen in the flat from

Figure 15. Figures 16 and 17 show the before and after of this reduction method.

Figure 18. A sample raw science image from the GBO. Notice the visible read-line towards the right of the image, in addition to the hot region around the bottom left and right of the image. Also note, the scale for dark sky is near 1000 counts due to the bias present in the image. 30

Figure 19. The result of figure 16 after applying the reduction equation above. Notice the read-line and hot regions are no longer visible. Additionally, the scale is much closer to zero, which is what we would expect for dark sky and no atmosphere. Since we are observing through atmosphere, there is generally some positive average dark sky value for every image.

3.3 – IRAF, DS9, and Reference Stars

Initial analysis was performed using IRAF and SAOImageDS9 (DS9) to determine the radii of the circular aperture and sky annulus to be used when calculating flux. An aperture radius to use during photometry was chosen visually by examining several radial plots and using the IRAF IMEXAMINE package for image statistics. Based on plots like those seen in Figure 18, an 31 aperture radius of 10 pixels was chosen as it encompassed the majority of the targets light but did not include too much dark sky. This value ended up being

Figure 20. Radial plot of Tabby’s Star taken with IRAF. Radius is in pixels and the FWHM is the lower right number (4.79) (IRAF IMEXAMINE). slightly larger than twice the average FWHM (Full-Width at Half Maximum) of the targets point spread function (PSF); which was 4.92. Additionally, this plot was used to select the radii for the dark sky annulus. The annulus was defined to have an inner radius of 25 pixels and an outer radius of 30 pixels. This area was consistently flat for the target and reference stars. The atmospheric conditions play a large role on the PSF for the stars in our images. An example of the PSF for the radial plot above can be seen in Figure 19 below. After visually analyzing 32 many images taken on different nights, the values were chosen to provide the best signal to noise for a variety of conditions. A sample image showing Tabby’s

Star, the reference stars, and their radii can be seen in Figure 20 which was created with DS9.

Figure 21. Sample 1-D gaussian for Tabby’s Star’s PSF (IRAF IMEXAMINE).

Based on (Romanishin, 2006), the signal to noise (S/N) is calculated by

퐶 S/N = 푠푡푎푟 (21) √퐶푠푡푎푟+ 2퐶푠푘푦 where 퐶푠푡푎푟 is the counts from the target per pixel and 퐶푠푘푦 is the average sky value per pixel. For 30 second exposures, the signal to noise ranged from ~ 25 –

30 throughout the filters for typical nights. This does not include nights where the weather was too bad, and the data were rejected completely. 33

Figure 22. Sample image showing the selected inner annulus radius of 10 pixels and an outer sky annulus from 25-30 pixels. These values remain constant for all targets (DS9). In addition to the aperture and annulus size, accurate coordinate data had to be obtained for Tabby’s Star and the three reference stars. After the DC3 software takes an image, it is run through a plate solver to identify the section of sky it belongs to. It then encodes the FK5 JD2000 coordinate information into the header for the image. IRAF then uses this information to map the coordinate inputs to the corresponding physical pixels in the image. A sample of the 34 coordinate file is seen in Table 2. IRAF’s PHOT package uses this to analyze the

Table 2. Text file with two columns, containing the FK5 world coordinates in degrees for Tabby’s star (row 1), followed by reference stars c1, c2, and c3 (row 2,3, and 4 respectively)

Table 3. From left to right: Line1: Begins with part of the image name followed by the physical pixel location for the target star, then the name of the coordinate file, and ends with the ordered number of that file. Line 2: Revised coordinates and statistics from centering iterations. Line 3: Contains image statistics such as mean background and it’s RMS value around the target star. Line 4: Shows the exposure time, followed by the airmass, then filter used, and finally the exact Julian Date of the image. Line 5: The aperture radius around the target, followed by the sum of counts inside the radius, followed by the area of the aperture. Then comes the value used for our flux (counts inside aperture minus the average sky value per pixel). After the flux comes the calculated instrument magnitude, followed by the error in that calculation, then finishes by identifying if there were any flags raised. This is then repeated for each reference star in order of the coordinate file (IRAF PHOT) targets in the order they are given from Table 2, and with the aperture and annulus radii from above. It then produces a file like the one seen in Table 3.

This contains all of the necessary data from the image that can now be parsed, visualized, and saved by the pipeline.

It is important to choose good reference stars when calculating relative flux. Luckily, a lot of work was done by (Bruce L. Gary, n.d.) to classify a majority of stars in a typical FOV around Tabby’s Star; the result of which can be seen in 35

Figure 23. Tabby’s star is seen inside the red box. Numbered and circled stars are observed and the resulting information is given in Table 4 (Bruce L. Gary, n.d.).

Table 4. Relevant data from possible reference stars shown in Figure 21. A final “score” is assigned in the last column based on how good the star is as a reference. This research uses stars 1, 4, and 10, due to their score, magnitude, color, and proximity to Tabby’s Star (Bruce L. Gary, n.d.). 36

Figure 21 and Table 4 above. This project uses stars 1, 4, and 10 from these results. These stars are similar to Tabby’s Star in magnitude and color. They are also stable and located close to our target within the image. Figure 22 shows how these stars have varied over the 2018 campaign.

Figure 24. Shows the reference star flux from May 2018 to December 2018. The reference stars are plotted with blue, orange, and green. They are shifted slightly down in order to be seen clearly. The red plot is the average of the three references and is the value used in our relative flux equation. It is clear that the average of these stars was very stable throughout the year.

3.4 – Python Automated Pipeline

There are several scripts and pieces of software that are used to make up the entirety of the pipeline for this project. Appendix B contains some of the scripts and procedures involved in addition to a GitHub link to the pipeline. The top-level software used is a Linux tool called crontab; which allow you to schedule tasks. In this case, it is setup to run a bash script for the Tabby pipeline every morning at 7AM PT. The bash script activates the virtual environment used to run IRAF, then calls a secondary script called Tabby_API.py. This script 37 is used to examine new, and existing data and if necessary, call various routines to process any new images. It begins by looking in Dropbox to see if any data were added by the GBO the night before. If so, it copies and parses the data into a separate folder containing all archive Tabby data, organized by their observation dates. The script then checks that data folder against previously processed dates. If there aren’t any new data to process, the script completes.

However, if there are new data, the script begins calling routines from

Tabby_pipe.py to reduce, and analyze them.

Tabby_pipe.py is the back end to the pipeline and contains most of the major routines used to process the GBO images. The first routine called, is used to extract the valuable data from the images. First, it finds all images for the first filter and extracts the date the images were taken on. It then uses this to find the available calibrations frames based on the CCD set temperature discussed in section 3.1. From the dates available with this temperature, it finds the required bias, dark, and flat frames taken closest to the date of the image.

It then creates master frames for the existing filter set using some very valuable functions written by Jon Swift, from The Thacher school. Each calibration frame is read and stacked together after making any necessary corrections (bias or dark correction). A master is produced using the median value of the resulting stacked frames. The master dark frame is given in counts/second, so it has to be multiplied by the exposure time of the image when reducing. Additionally, the master flat is normalized during the creation process, so no further care needs to be taken when dividing out the flat. 38

The master frames are then used in the reduction equation above to produce calibrated images for processing. The newly calibrated images are passed into IRAF’s PHOT package, where it uses the supplied radii for the circular aperture (10 pixels) and annulus (inner radius of 25 pixels and outer radius of 30 pixels) to surround the targets based on the coordinate file. It measures the flux for our target and reference stars based on the procedure described in section 2.2. It produces a file similar to Table 3, with all of the relevant information about the four stars. These files are saved in a new folder labeled by the observation date and placed in the pipelines output folder

(pipe_out). The calibrated image is then deleted, leaving only the unaltered data and the files produced by PHOT.

The pipeline then repeats this process for every filter used during the observing session, resulting in an output folder containing a single data file for every image taken.

The next routine is called to sort through the files produced by PHOT and extract the data for Tabby’s Star and the three reference stars. It uses these data to derive Tabby’s relative flux based on the equation above. The routine also extracts the stars instrumental magnitudes, their error, the filter used, exposure time, and the Julian Date for the image. It creates a data frame with all of this information then starts calculating means and errors for each filter based on the entire night’s data. The data frame is then written (pickled) to a file that can be easily called later on. The pickled file is stored with the output files from

IRAF’s phot package in the pipe_out folder. 39

The next routine searches all dates containing this output file to determine which dates have been fully processed. It then creates a summary csv (Comma- separated Values) file for each filter containing all of the basic statistics for each filter on each day. This is then easily used to produce light curves with a built-in plotting routine.

Finally, once all observing dates have been processed and their data applied to the summary files, the pipeline runs a Linux tool named Rsync. This creates a backup of all new data and pipeline output and saves to two separate drive services (Google Drive and Box Sync).

Results

4.1 – 2018 Light Curve

Figure 25. The entire 2018 observing campaign in the g’, r’, i’, and V filters. The campaign began at the low point of a dimming event which has been named Evangeline. Evangeline is the largest dip that has been witnessed since the original Kepler data. The breaks in data are from various technical and weather issues that came up over the course of the year. The result of the entire 2018 observing campaign, with all of the filters, can be seen in Figure 23 above. The first night of usable data coincided with the largest dip since the Kepler mission and was ultimately named Evangeline by the

Kickstarter contributors. The Evangeline dip also exhibited the chromatic behavior seen in Boyajian (2018) and provides more evidence supporting the assertion that the light is being blocked by something optically thin (dust). This is noticeable in the separation between the g’ and r’ filters the during the event.

There were some periods throughout the year that not all filters could be utilized. The i’ filter wasn’t installed until an upgrade near the midway point in the graph (around 520 on the x-axis). Additionally, z’ data have been taken for this period; however, the results vary greatly and need further review before any 41 confidence can be placed in their validity; therefore, the z’ data have been omitted from these results. Also, the V filter was not used at the beginning of the campaign but was added on JD 2458273. The overall plot is normalized using the period from JD 2458330 to JD 2458350 (530-550 on the x-axis).

Figure 26. (Top) LCO data for Tabby’s Star in 2018. It shows r’ data from the ELP, OGG, and TFN observatories. The vertical dashed line marks a change in LCO’s pipeline (Boyajian’s Star Obs Squad, Slack Group, Photometry channel, 2019).

Figure 27. (Bottom) r’ data taken with GBO and processed with this pipeline. 42

The GBO and LCO data above match extremely well during the

Evangeline event. Additionally, there is a clear periodic shape in the GBO data, just after 550 on the x-axis of Figure 25. This is also recognizable in the ELP data from Figure 24. Overall, based on the 2018 light curve, the processing of

GBO data by this pipeline, seems to be producing reliable and verifiable results.

4.2 – Evangeline

Figure 28. Zoomed in plot for Evangeline dip, highlighting the chromatic nature and shape of this event. This is especially noticeable for the first and second day of observing. Evangeline was the first event captured by this observing campaign; however, there was another event (“Caral-Supe”) immediately prior. Figure 27 highlights the shape of these two events and Figure 28 highlights their depth compared to the 2017 events reported in (Tabetha S. Boyajian et al., 2018). 43

These two newer events reached further depths than the 2017 dips but their shapes are also quite different. The 2017 dips seem to last longer than Caral-

Supe or Evangeline with longer ingress and egress. The GBO observing

Figure 29. (Top) Zoomed in plot for the Caral-Supe and Evangeline events in March 2018 (Boyajian’s Star Obs Squad, Slack Group, Photometry channel, 2018).

Figure 30. (Bottom) LCO r’ data for 2017 and the first part of 2018 (Boyajian’s Star Obs Squad, Slack Group, Photometry channel, 2018). campaign began just after the Evangeline’s minimum and the quick egress mentioned above can be witnessed by plotting individual observing sessions. 44

Figure 31. Plots showing the g’, r’, and z’ filters during the egress of Evangeline. The rising behavior can be seen in each of the three plots. Additionally, wavelength dependence this event is clearly seen as the chromatic behavior is minimized throughout egress. 45

4.3 – Long-term Secular Behavior

While Schaefer (2016) showed a general drop in flux over century long periods, the GBO data show 2018 experiencing a slight rising trend which could be contributed to requiring a larger sample set, i.e., more time. Based on Montet

& Simon (2016) and the Kepler data in Figure 12, the secular dimming is somewhat variable and can produce similar trends. Therefore, it’s possible that our data do not cover a long enough period to say anything conclusive with regards to its nature. The LCO data from Figure 24 show this trend as well.

Figure 32. 2018 GBO data without the Evangeline dip. Solid black line is the best fit for the g’ (Top) and r’ (bottom) data. 46

Figure 33. 2018 GBO data without the Evangeline dip. Solid black line is the best fit for the i’ (Top) and V (bottom) data.

4.4 – Pipeline and Future work

If an observing run is completed successfully, one night produces 75 images from 5 different filters. Doing the complete reduction, photometry, and analysis for this data set manually takes approximately 90 minutes. The pipeline created for this project is able to process this same data set in roughly 3 minutes.

This greatly increases the ability to quickly recognize a dimming event and send an alert to the community; however, some improvements can still be made. 47

First, the routines require a lot of user input to constrain things like which filter is being used and the code should be able to handle these things based on the image headers. Incorporating this would make interactive use more user friendly and robust. Additionally, an algorithm for atmospheric extinction still needs to be applied. The current version does not consider airmass (how much atmosphere the light from our image had to pass through based on angle from zenith) and its wavelength dependent effects seen in Figure 32. The slope from

Figure 34. Plot showing the decrease (positive slope due to inverse nature of apparent magnitude) in flux as with greater airmass. The diverging slopes are due to the difference in the B (blue) and V (orange) filters. The atmosphere causes the bluer light to be blocked more (larger slope) than the redder light. plots like this can are extinction coefficients. They can be used to apply small corrections based on the filter and airmass in order to increase the precision in detecting chromatic behavior.

Finally, as mentioned above, the collaboration communicates via Slack and updated light curves are posted there as well. Slack has a feature that makes use of “bots” that allow you to automate certain tasks. I believe it is 48 possible to use this feature to post updated light curves produced by the pipeline.

Shortly after 7AM when the pipeline runs, it would check for updated plots and post them automatically. This would help with consistency and allow for flexibility with a researcher’s schedule.

Conclusions

5.1 – Summary

The pipeline created for this project has allowed for successful nightly observations of KIC 8462851 in order to recognize dimming events and provide virtually real time updates to the community. It has done this with the ability to process a night worth of data in minutes, compared to hours if done manually.

This pipeline will allow for GBO to continue monitoring Tabby’s Star with minimal maintenance and therefore should enable observations to continue for the foreseeable future. Additionally, this pipeline can be modified to support other monitoring projects.

The results produced by this pipeline consistently agree with data coming from other teams using several different observatories. This increases overall confidence in its output. Some work can still be done to improve its precision and accuracy; however, it is flexible enough that any improvements can be applied to the entire data set without much additional effort.

Data from this campaign confirms the chromatic nature of the short-term dipping events witnessed by Kepler and the LCO 2017 data. Our data during

Evangeline’s egress can be used to constrain future models and offers unique insight to the shape of these events. The data also show a small rising trend throughout the 2018 data. This is in contrast to the archive data; however, the length of this campaign is not long enough to make any claims with regards to century long decline considering the variability of its behavior across the Kepler mission. 50

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Appendix A

IRAF

Installation:

Before installing IRAF, there is a couple of other programs that need to be installed. I found a website that helped with a lot of the setup but some is out of date (http://www.astrobetter.com/wiki/Setup+a+New+Mac+for+Astronomy). I will summarize the majority of what is needed. IRAF is only available for Linux and

MacOS machines. The directions here, apply to a new (2018) mac.

First, you’ll want to make sure your terminal is set to “bash” (it should be by default). Next, you’ll need to install XQuartz in order to get terminals that allow for IRAF graphical output. XQuartz can be downloaded from https://www.xquartz.org . Next, you’ll need to install XCode: Command Line tools and Compilers. You can do this from the app store or from the terminal by typing

Xcode-select --install then agreeing to the Xcode License.

Then you’ll need to install Anaconda; which will install a new version of Python in addition to many other useful tools. This will also be necessary for installing

IRAF. Anaconda allows you to create virtual environments for working on different projects. In our case, we’ll use it to create an environment where IRAF will be installed.

STScI is the group originally responsible for IRAF and they have now created a channel with numerous useful software modules and compatible with Anaconda.

The following directions can currently be found at https://astroconda.readthedocs.io/en/latest/installation.html.

Additionally, you will notice the term “Conda”; which is used to complete various anaconda commands through the terminal and often the name used when referring to Anaconda tasks.

For the following instructions, the $ symbol is there to represent what your terminal output should look like. You shouldn’t need to type it manually, it should already be in the command line. After successfully installing Anaconda, open a terminal and type

$ conda config –-add channels http://ssb.stsci.edu/astroconda

That command will configure Anaconda to use the STScI Astroconda channel.

Next you’ll type something similar to

$ conda create -n iraf27 python=2.7 iraf-all pyraf-all stsci

The only part that you may want to change is “iraf27.” This is the name of the environment and you can name it whatever you like. You will need to activate and deactivate this name whenever you use the environment. The line starts with “conda create -n.” This is telling conda to create a new (-n) environment.

This is followed by the name of the environment (iraf27), the version of python

(python=2.7) (IRAF is only compatible with Python 2), and then the packages that are to be installed in the new environment (iraf-all pyraf-all stsci). This installs a 55 complete version of IRAF, Pyraf, and other STScI tools.

You’ll activate this environment by typing

$ source activate iraf27

(or whatever name you chose) and deactivate it by typing

$ source deactivate

Configuration:

The first thing you’ll have to do before using IRAF is create a login.cl file and parameter folder. This is done automatically by typing the following command in a terminal.

$ mkiraf

You will be prompted with a question like

Initialize uparm? (y|n):

Type y, then enter. Next you will be asked to enter the terminal type. This is where you’ll enter the new terminal downloaded from XQuartz. Type xgterm then press enter. The xgterm terminal is required for graphical output from IRAF so be sure to use it when trying to use IRAF. Immediately after pressing enter, you should get a message saying

Additionally, you should see this file in your home folder, along with a folder named uparm that will contain parameters you update in IRAF. 56

IRAF uses the login.cl file to set parameters and sign in to IRAF. Therefore, you must be in the home folder where the file is located to initialize IRAF. This can be done using typical terminal commands such as “cd name/of/directory”. There is also a couple of changes that need to be made in order for IRAF to work with the .fts images that the GBO produces. Below is a copy of my login.cl file

# LOGIN.CL -- User login file for the IRAF command language.

# Identify login.cl version (checked in images.cl). if (defpar ("logver")) logver = "IRAF V2.16 March 2012" set home = "/Users/jfausett/" set imdir = "/tmp/jfausett/" Set’s the home directory and location set cache = "U_CACHEDIR" for the uparm folder. set uparm = "home$uparm/" set userid = "jfausett"

# Set the terminal type. We assume the user has defined this correctly # when issuing the MKIRAF and no longer key off the unix TERM to set a # default. if (access (".hushiraf") == no) print "setting terminal type to xgterm..." stty xgterm

# Uncomment and edit to change the defaults. #set editor = vi #set printer = lp #set pspage = "letter" Set stdmage to imt4096 to handle #set stdimage = imt4096 #set stdimcur = stdimage larger GBO images #set stdplot = lw #set clobber = no #set imclobber = no #set filewait = yes Add fts to fxf: for .fts files. Also remove #set cmbuflen = 512000 #set min_lenuserarea = 64000 # befor set to initialize #set imtype = "its" set imextn = "oif:imh fxf:fts,fits fxb:fxb plf:pl qpf:qp stf:hhh,??h"

# XIMTOOL/DISPLAY stuff. Set node to the name of your workstation to # enable remote image display. The trailing "!" is required. #set node = "my_workstation!"

# CL parameters you mighth want to change. #ehinit = "nostandout eol noverify" 57

#epinit = "standout showall" showtype = yes

# Default USER package; extend or modify as you wish. Note that this can # be used to call FORTRAN programs from IRAF. package user task $adb $bc $cal $cat $comm $cp $csh $date $dbx $df $diff = "$foreign" task $du $find $finger $ftp $grep $lpq $lprm $ls $mail $make = "$foreign" task $man $mon $mv $nm $od $ps $rcp $rlogin $rsh $ruptime = "$foreign" task $rwho $sh $spell $sps $strings $su $telnet $tip $top = "$foreign" task $awk $vi $emacs $w $wc $less $rusers $sync $pwd $gdb = "$foreign" task $xc $mkpkg $generic $rtar $wtar $buglog = "$foreign" #task $fc = "$xc -h $* -limfort -lsys -lvops -los" task $fc = ("$" // envget("iraf") // "unix/hlib/fc.csh" // " -h $* -limfort -lsys -lvops -los") task $nbugs = ("$(setenv EDITOR 'buglog -e';" // "less -Cqm +G " // envget ("iraf") // "local/bugs.*)") task $cls = "$clear;ls" task $clw = "$clear;w" task $pg = ("$(less -Cqm $*)") if (access ("home$loginuser.cl")) cl < "home$loginuser.cl" ; keep

# Load the default CL package. Doing so here allows us to override package # paths and load personalized packages from our loginuser.cl. clpackage

# List any packages you want loaded at login time, ONE PER LINE. images # general image operators plot # graphics tasks dataio # data conversions, import export lists # list processing

# The if(deftask...) is needed for V2.9 compatibility. if (deftask ("proto")) proto # prototype or ad hoc tasks tv # image display utilities # miscellaneous utilities 58 noao # optical astronomy packages vo # Virtual Observatory tools prcache directory cache directory page type help

# Print the message of the day. if (access (".hushiraf")) menus = no else { type hlib$motd }

# Uncomment to initialize the SAMP interface on startup. if (deftask ("samp") == yes) { printf ("Initializing SAMP .... ") if (sampHubAccess() == yes) { # Enable SAMP messaaging. Set default handlers that don't require # VO capabilities. samp quiet samp ("on", >& "dev$null") # samp ("handler", "table.load.votable", "tinfo $url", >& "dev$null") # samp ("handler", "image.load.fits", "imstat $url", >& "dev$null") samp noquiet print ("on") } else print ("No Hub Available\n") }

# Delete any old MTIO lock (magtape position) files. if (deftask ("mtclean")) mtclean else delete uparm$mt?.lok,uparm$*.wcs verify- keep Had trouble with it not finalizing some reset imtype="fts" reset imext="fxf:fts,fits" changes so added these lines to ensure changes are applied.

Once the login.cl file is edited to match this, you should be ready to begin using

IRAF. Launch the xgterm window and make sure you’re in the home folder where the login.cl file is located by typing 59 cd name/of/home/folder/ then type cl

This should sign you in and produce a screen like this.

From this point, there are a number of valuable resources for reference on how to use IRAF. Some of the ones I found very useful were, “A beginners Guide to

Using IRAF” by Jeannete Barnes and “A User’s Guide to Stellar CCD Photometry

With IRAF” by Phillip Massey (Barnes, 1993) (Massey & Davis, 1992).

Additionally, I have posted a copy of many of my parameters below for reference.

Parameters:

PHOT:

Datapars inside PHOT:

Centerpars inside PHOT:

Fitskypars inside PHOT:

Photpars inside PHOT:

Setinstrument:

The following command parameter settings are for creating master calibration frames. Here are my personal settings.

Zerocombine:

Darkcombine:

Flatcombine:

CCDProc:

Appendix B

The repository for the pipeline produced from this research can be found at https://github.com/jfausett/GBO_KIC_Pipeline.git.

Crontab:

Crontab is a useful tool for Linux and mac machines. It allows you to schedule terminal commands and it was it used to schedule this pipeline to run. Below is a sample of what appears when we type crontab -e.

SHELL=/bin/bash PATH=/Users/jfausett/anaconda2/envs/astro/bin:/Users/jfausett/anaconda2/bin:/usr/local/bin:/usr/bin:/bin:/us r/sbin:/sbin:/Library/TeX/texbin:/opt/X11/bin 0 7 * * * cd /Users/jfausett/PycharmProjects/gbo_tabby_pipeline && ./Tabby_API.bash >> ./log.txt

This defines the shell environment as bash then defines the path, so all available modules and tools are accessible. The scheduling and commands start on the line that begins with 0 7 * * *…. The 0 refers the minute and the 7 is the hour followed by * * * for the day of the month, week, and year respectively. So, this command tells it to execute the following command at 7:00 every day of every week of every year. The command

/Users/jfausett/PycharmProjects/gbo_tabby_pipeline && ./Tabby_API.bash >> ./log.txt

This tells the terminal to change directories to the pipeline location then (&&) run the Tabby_API.bash script. The >> is telling it to save all terminal output to a file named log.txt located in the same directory.

Tabby_API_bash:

This is a bash script that activates the environment where IRAF and the rest of the required modules are installed. After activating the environment, it runs the python script Tabby_API.py which is discussed in the next section. After running the python script, it deactivates the environment before ending. Here is a copy of the bash script

Tabby_API.py:

This script is a non-interactive front end that does most of the automating. It imports Tabby_pipe for all of the routines it uses process images. It first looks for new data from the GBO and moves them to a separate directory if found (line

10). Next it compares dates for images in that directory and compares them against dates that have already been processed (lines 14-15). If new dates exist, it processes them based on the description given in section 3.4 (lines 18-42).

Here is the script in its entirety. 67

Tabby_pipe.py:

This script contains all of the back-end code for processing the data. The script is quite long and therefore has been excluded from this copy; however, the

GitHub link above can be used to access the source code in its entirety. Below are snippets from Tabby_pipe.py that start with comments (begins with #) 68 describing the routines purpose and followed by defining the routine/function.

# Get appropriate paths (From Thachers Tabby_reduction) def get_paths():

# Backup data and pipout to backup path using os.system and rsync def backup(servers=['Dropbox', 'Box\ Sync', 'Google\ Drive']):

# Find new data and parse into data path def move_new_files():

# Return donedates, obsdates, and caldates def get_dates(band, prefix=['IRAF_night', 'KIC']):

# Determine closest calibrationc file dates based on time from observation def get_cal_dates(date, band):

# Calibration frames (From Thachers Tabby_reduction) def get_cal_frames(biasdate, darkdate, flatdate, setting=None, readnoise=False, band='r', flatten=False):

# Get cal_frames and process reduction on all images for a night def do_phot(date, filters=['g', 'r', 'i', 'z', 'V'], setting=None, source='iraf'):

# Read iraf .mag files and extract photometry data def night_phot(date, write=True, source='iraf'):

# Add dates to obs_summary and IRAF_all_photometry def add_new_dates():

# Compiles useful data from overall dataframe for easy acces when plotting def make_summary(): 69

# Make plots def make_plot(date='all', filters=['g', 'r', 'i', 'V'], sigma=False, write=False, sub=False, night=False, zoom=False, reference=False):