MicroBooNE Supernova Stream Data Quality and Readout Studies

Clara Berger Nevis Laboratories, Columbia University, New York

Abstract

Abstract: The MicroBooNE detector at Fermilab has the capa- bility to detect neutrino interactions from galactic core-collapse su- pernova events. To allow for potential data collection if a supernova occurs, the recently instated MicroBooNE supernova stream continu- ously collects data from the detector that is significantly compressed to cut down on the rates at which the data is read out. An FPGA suppresses the data by applying a dynamic zero-suppression algorithm that discriminates passing charge signals from the baseline, which is determined on the fly. This note details a study of reconstructing base- line in each channel readout. It also presents data quality studies on wire occupancy and flipping bits in the supernova stream. These stud- ies will help determine how best to implement lossy data reduction and reconstruction at the Deep Underground Neutrino Experiment, where if detected and studied, supernova neutrino interactions could reveal some of the conditions within and processes that drive these stellar core-collapses.

Contents

1 Introduction 2 1.1 MicroBooNE Detector ...... 2 1.1.1 Supernova Neutrinos ...... 3 1.1.2 Supernova Stream ...... 3 1.1.3 Suppression Scheme ...... 4 1.2 DUNE ...... 4

1 2 Data Quality Studies 5 2.1 Wire Occupancy ...... 5 2.2 Flipped Bits ...... 7

3 Baseline Studies 9 3.1 Methods ...... 9 3.2 Results ...... 9 3.3 Waveform Abnormalities ...... 12 3.3.1 Identical ADC Values in the ROI ...... 12 3.3.2 Double Peaks in V Plane ...... 16

4 Conclusions and Further Research 17

5 Acknowledgements 18

1 Introduction

1.1 MicroBooNE Detector

The Micro Booster Neutrino Experiment (MicroBooNE) detector is a 170 ton liquid argon time projection chamber (LArTPC) located at Fermilab. MicroBooNE’s primary physics goals include resolving predecessor Mini- BooNE’s low energy event excess, measuring precision cross-sections, and searching for astronomical and exotic particles, such, as presented in this paper, as neutrinos from a nearby core-collapse supernova [1].

The detector is along the Booster neutrino beam (BNB), which produces mainly muon neutrinos from pion decays, as well as neutrinos from pion and kaon decays in the NuMI beam. When a charged particle traverses the detector, it leaves a stream of free electrons in the dense liquid argon. The electron trails then drift in the electric field in the detector (generated by a voltage applied on to a cathode plane) toward an anode plane. As depicted in figure 1, at the anode, 3 wire planes are arranged at 60◦, −60◦, and 0◦ from the vertical and the charges will either induce signal on the wires as the trails drift (in the first two sets of wires or induction planes) or be collected as signal (in the final set of wires or the collection plane). While the signals on the wire planes spatially track hits in the detector, the third dimension of reconstruction is the time between when the event took place and when the charge drifted to the anode. The light collection system detects the

2 Figure 1: Rendering of MicroBooNE LArTPC neutrino detection process. scintillation photons from each event that supply this initial time of event information [1].

1.1.1 Supernova Neutrinos

The majority of the binding energy released during a core-collapse super- nova is in the form of neutrinos with energies on the order of tens of MeV. The neutrinos carried away in the supernova are mixed all three flavors but liquid argon is especially sensitive to detecting electron neutrinos (νe) through the process 40 − 40 ∗ νe + Ar → e + K . The cores of supernovae house extreme conditions such as temperatures and densities. Learning about the properties of these supernova neutrinos can reveal interesting physics about the dynamics of and processes driving a collapsing stellar core in a supernova event [4].

1.1.2 Supernova Stream

The signals from the wire channels as well as from the photo-multiplier tubes in the light collection system are read out and digitized in two streams. The first avenue for read out and stored data, or the ”NU” stream, is for beam neutrino events where all information is losslessly compressed and recorded according to triggers from the BNB, NuMI and external events [1].

3 The alternate stream allows for the detection of these unpredictable neu- trino events from supernovae, appropriately the supernova or ”SN” stream, and thus must be continuously reading data from non-beam events. Super- nova neutrinos would reach Earth before electromagnetic signals so not only must the stream be continuously reading data, it must also store several hours of data in case of an alert from another observatory, neutrino or elec- tromagnetic. In order to limit the amount of continuously read out data to near 30 GB/s, a Field Programmable Gate Array (FPGA) applies a Zero- Suppression algorithm that can discriminate between signal waveforms and the baseline, and subsequently suppress that baseline. This greatly reduces the amount of data to be processed by the DAQ, while retaining information on the regions of interest (ROIs), or packets of signal that have been selected from the baseline [2, 3].

For a supernova within the galaxy, on the order of 10 neutrino interactions producing ∼10 MeV electrons are expected in MicroBooNE [1].

1.1.3 Suppression Scheme

While the FPGA is running throughout the entire readout, only the wave- forms that surpass a certain amplitude threshold are stored. These thresholds are in respect to the baseline and vary by plane [1].

The readout stream is divided into 64 tick (1 tick = 0.5 µs) sections in which the FPGA computes the baseline mean and variance. If the values are deemed consistent (∆(mean) < 2ADC; ∆(variance) < 3ADC2) over a set of 3 of these sections, the mean of the middle block is taken as the baseline [2]. After establishing the baseline, the FPGA will subtract this baseline from all values read out and will trigger to record an ROI if a sample passes a -25 ADC threshold in the first induction plane, ±15 ADC threshold in the second induction plane, or a +30 ADC threshold in the collection plane [3]. Within the ROI, 7 presamples are taken before the first sample that passes the threshold, as well as 8 postsamples after the last sample passing the threshold to capture the baseline before and after the pulse.

1.2 DUNE

The Deep Underground Neutrino Experiment (DUNE) is an international collaboration experiment that will utilize the high intensity neutrino beam at

4 Figure 2: The Deep Underground Neutrino Experiment running between Fermilab and SURF. the Long Baseline Neutrino Facility (LBNF) at Fermilab. By 2035 DUNE is proposed to be comprised of a 1.2 MW beam and a near detector at Fermilab as well as four 10 kt LArTPCs at the Sanford Underground Research Facility (SURF) 1,300 km away in Lead, SD as depicted in in figure 2. The main sci- ence goals of DUNE include measuring CP violations, investigating neutrino mass hierarchy, and detecting neutrinos from core-collapse supernovae [5].

In the proposed DUNE detector, 3000 of such events are expected in 40 kt LAr from a supernova 10 kpc away [5, 4].

Our current work with the supernova stream in MicroBooNE will help inform how to best implement FPGA analysis in upcoming and more sensitive DUNE.

2 Data Quality Studies

All data presented uses supernova stream run 14662, the assembled frames for the Online Monitor, taken on January 17th, 2018. The binary data was then converted into LArSoft format using uboonecode version 6 47 01.

2.1 Wire Occupancy

MicroBooNE is known to have a variety of dead wires that do not return any signal in that channel. We see that beyond the dead wires, the probability of a hit occurring in a particular channel is not uniform. Figure 3 shows the gaps in a wire occupancy plot that expose dead wires but also a few particularly active channels with many more hits occurring in one channel

5 Figure 3: The number of hits recorded in each wire after 200 events. Both dead channels and noisy channels can be identified from the distribution.

Figure 4: 100 events in Run 14662 displayed on top of each other revealing the dead and noisy channels. The red lines depict the approximate shape of the occupancy distribution we would expect based on the than the surrounding channels. These are most likely noisy channels. The distribution of hits per wire in the collection plane is relatively flat because all of the wires are the same length in the detector. The induction channels vary in length so we expect a pattern resembling that of the first 4800 channels in figure 3. We observe similar patterns in figure 4 where the tracks of 100 events were stacked also giving an indication of how hits build up in each wire. However, we have found that the gap between about wire 2000 and 3000 is due to an inefficiency at high thresholds and these wires output signals when the thresholds in the channels were lowered based on the distribution of ADCs in the NU stream. More hits in a channel in the SN stream consequently requires that more data is read and saved.

6 Figure 5: Waveform in channel 3401 showing a flipped bit at tick 4804.

2.2 Flipped Bits

After the electronics write the binary data, once the data is read out, some binary words show a flipped bit where one (or more) bit(s) in the word flips. These flipped bits are almost always from 0 to 1, which adds a 2n ADC, depending on which bit is effected, to the original value. Figure 5 depicts an example of a waveform with one flipped bit.

Before deconvolution [6] however 1, a flipping bit filter is applied that sets the value of a flipped bit to the interpolation of the two neighboring samples. This interpolation correction is applied if the flipped bit value is 32 or more ADCs from the interpolation. In figure 6, the integrals of waveforms with and without flipped bits are compared before and after deconvolution + the flipped bit filter. The structures at 2n, which are characteristic of flipping bits, are not present after deconvolution. Thus the interpolation filter and deconvolution together appear to have mitigated effects of flipping bits with a jump greater than 32 ADC counts (the metric used in isolating flipped bits). The number of times a channel experienced an ROI with at least one flipped bit is shown in figure 7. While flipped bits occur in all channels, the pattern in this distribution does not closely resemble the wire occupancy histogram (figure 3) meaning flipping bits occur at different rates depending on the channel/plane.

1as part of uboonecode CalData/CalWireZS module.cc

7 Figure 6: Integrated ROIs with and without flipped bits before (left) and after (right) deconvolution. 65 events.

Figure 7: Number of ROIs that include one or more flipped bit (over 32ADC) in each channel. 200 events.

8 3 Baseline Studies

The FPGA does not report the baseline value used for zero suppression, but subtracting the baseline is the first step of analysis before deconvolution and hit finding so we must be able to reconstruct the baseline for each ROI with the limited amount of presamples and postsamples given by the FPGA.

3.1 Methods

We implemented three methods of baseline estimation. The first two are simply using either the first presample or the last postsample as the baseline value. The third method for estimating the baseline is an algorithm that takes the best pre and postsamples (such that there are no flipping bits) and interpolates a linear baseline. We may then solve for the baseline at a given time and subtract it from the ROI sample at the same time.

To compare all three methods of estimating the baseline, we selected either the first or last sample passing the threshold and subtracted the three differ- ent baselines. We plotted the distributions of the threshold passing sample minus the baseline for all 3 methods of baseline estimation, in all 3 planes, and for both the first sample passing the threshold and the last sample pass- ing the threshold. If the baseline was well estimated, we would see a narrow normal distribution of values peaking at the threshold for each plane. The thinner the peak and the more centered the peak is around -25, ±15, or +30 ADC counts the better that method is at estimating the baseline. We also fit each of the peaks to extract a mean and standard deviation approximating the distributions as Gaussian.

3.2 Results

We deemed the linear interpolation algorithm the best method for estimat- ing the baseline in the first induction plane. The algorithm gave the narrowest distributions and was thus the most consistent baseline at reconstructing the threshold (figure 8). However, the distributions are not centered exactly at -25 ADC counts because of an accepted bias in each plane. Between the time the FPGA calculates the baseline and the time the pulse passes there is a time delay. If the readout has an underlying low frequency oscillation, the baseline may have slightly changed during that time delay and have given

9 Figure 8: Three methods of baseline subtraction and the statistics of the Gaussian fits in the first induction plane using both the first (top) and last (bottom) sample passing threshold for 100 events. Red vertical line show the threshold in each plane. our reconstruction a bias as we are only able to calculate the baseline at the time of the pulse.

In the second induction plane, we observed two peaks at the two thresholds +15 and -15 ADC for reasons explained in Section 3.3.2 (figure 9). Other- wise, we again found the algorithm to be the best method for estimating the baseline. When both peaks are fit with Gaussian distributions, the algorithm returns the narrowest peaks and doesn’t have the shoulder in the negative as seen in the distributions that uses the first presample as the baseline.

Finally, in the collection plane, using the first presample as the baseline returned the narrowest fits, but also produced an extra bump in the distri- bution around -5 (figure 10). We do not know what causes this bump at the moment, the same bump does not appear when we employ the algorithm baseline, which itself shows similarly narrow peaks. Therefore the algorithm

10 Figure 9: Three methods of baseline subtraction and the statistics of the Gaussian fits in the second induction plane using both the first (top) and last (bottom) sample passing threshold for 100 events.

11 Figure 10: Three methods of baseline subtraction and the statistics of the Gaussian fits in the collection plane using both the first (top) and last (bot- tom) sample passing threshold for 100 events. baseline will again be optimal for estimating the baseline.

3.3 Waveform Abnormalities

3.3.1 Identical ADC Values in the ROI

A few notable abnormalities presented themselves during analysis. The first of these is a spike at 0 ADC when the last post sample was subtracted from the first sample passing threshold. We have pointed out examples of this spike in figure 11 where a zoomed in view of the baseline subtracted collection plane distribution reveals that there is an abnormal peak at 0 ADC. To investigate this problem we analyzed the waveforms where the 8th sample (theoretically the first sample passing threshold) minus the last postsample was zero. We observed 3 common reasons in all 3 planes for why

12 Figure 11: Zoomed in distributions of the 8th sample with baseline subtrac- tion using the 3 difference methods in the collection plane. the 8th sample and the last sample may be the same.

The first reason appeared to be noise. These noisy ROIs were usually 16 or 17 ticks long, which is the minimum for a full ROI with 7 presamples, one or two samples ’passing the threshold’, and 8 postsamples. These noisy readouts also do not pass the thresholds in any of the 3 planes nor show signs of a distinct waveform (figure 12). Nearly all of these noise pulses began at either 1600 or 4800 ticks. Each frame is 3200 ticks long but during analysis the binary to LArSoft converter (or swizzler) stitches together 1600 ticks from the preceding frame and 1600 ticks from the following frame such that the entire time span is 6400 ticks long. This means there are transitions between the frame boundaries at 0, 1600, 4800, and 6400 ticks. Thus, this noise between frames is most likely a consequence of how the frames are stiched together and will be addressed in future versions of the swizzler.

The second reason arises from artificial cropping of ROIs that occur be- tween two frames. If an ROI occurs between any of the frames, i.e. occur over one of the tick numbers 0, 1600, 4800, or 6400, it will be cropped into two half-ROIs. LArSoft recob::Wire should merge these cropped ROIs, but either a problem in the swizzler of the wire object prevents it. When these half-ROIs are only 8 ticks long, the 8th sample (that should theoretically pass the threshold) is the last sample as well (figure 13).

The third is a potential FPGA problem in which the value of the sample passing the threshold is copied over again as the last post sample. We do not currently know why or how this may be occurring but can observe that in otherwise ideal looking waveforms, the 8th sample seems to be directly

13 Figure 12: Event 1999 channel 3018 waveform that begins at 4800 ticks, is only 17 samples long, spans a mere 3 ADC, and does not have the shape of a pulse and is thus mostly a swizzler noise effect.

Figure 13: Event 2 channel 1998 waveform that is only 8 samples long and ends at 4799 ticks, cut off right before the following frame.

14 Figure 14: Event 2 channel 6917 waveform where the 8th sample (and thresh- old passing sample) is copied as the last postsample.

Figure 15: Distribution of the last postsample - (second to last sample) in waveforms where the 8th sample is copied over as the last postsample. Red vertical line show the threshold in each plane. copied into the last post sample as seen in figure 14.

To verify this last postsample is not a flipped bit, we subtracted the pre- ceding sample, as an approximation of the local baseline, and found this difference reconstructed the thresholds in each plane. This shows that the last sample has the value of the threshold passing sample, instead of the characteristic 2n values of flipped bits (figure 15).

15 Figure 16: (Top) Channel 3402 V plane waveform that was triggered on the +15 ADC threshold such that the entire pulse is kept. (Bottom) Channel 3412 V plane waveform that was triggered on the -15 ADC threshold such that only the second half of the pulse is kept.

3.3.2 Double Peaks in V Plane

Another peculiarity was the appearance of two peaks for a distribution of threshold passing samples minus the baseline. We would expect that since bipolar pulses in the induction planes first rise above and then fall below the baseline (figure 16 top), that the first sample passing threshold - the baseline will always be positive and the last sample passing threshold - the baseline will always be negative. In figure 9 there is secondary peak around the -15 ADC threshold meaning that in some cases, the first sample crossing the threshold is negative. We again looked at waveforms in the V plane where this was the case and observed that the pulse did not always reach the +15 ADC threshold during its rise but would then trigger on the -15 ADC threshold. By triggering during the second half of the pulse, the 7 presamples do not go far enough back to recover the baseline before the pulse (figure 16 bottom).

16 Figure 17: Cumulative length of all ROIs in a certain channel per frame divided by the length of the frame (6400 ticks) to demonstrate how much the data was suppressed. 100 events.

4 Conclusions and Further Research

Since the first detection of core-collapse supernova neutrinos from SN 1987a, there have been considerable developments in neutrino mixing and understanding astrophysical particles but fundamental questions about the process of stellar death remain open [7]. By maintaining a stream open to detecting low energy supernova neutrino events, MicroBooNE can monitor how to best suit electronics to finding these elusive tens of MeV supernova neutrino interactions.

We have presented a study on how to best reconstruct the FPGA-identified baseline and found that a tailored linear baseline algorithm can most closely determine the FPGA baseline. Correctly modeling this baseline will ensure accurate baseline subtraction followed by deconvolution and hit-finding in the energy reconstruction process. Further studies on Michel spectrum re- construction will also for comparisons between the NU and SN streams and the effectiveness of the SN FPGA.

Other considerations moving forward include addressing the repeating sam- ple problem and possibly re-configuring the FPGA code as well as further studies addressing the issue of flipping bits. While we have shown the effects flipping bits larger than 32 ADC may be attenuated through an interpola- tion filter and deconvolution, there is still the question of how to identify and

17 remove flipping bits with values of 2, 4, and 8 ADC counts.

Finally, as shown in figure 17, some wires still show relatively ineffective zero-suppression. Identifying and addressing trouble with noisy wires will ensure that the important data is being read out with efficiency. This ef- ficiency is key for implementing suppression and reconstruction in DUNE, carrying us to the next step in neutrino physics.

5 Acknowledgements

I would like to thank Jos´eCrespo-Anad´on,Georgia Karagiorgi, Mike Shae- vitz, and the rest of the Neutrinos group for guidance and support as well as interest in my progress throughout the summer. Further thanks to John Parsons and Amy Garwood for organizing, Bill Seligman for ROOT advis- ing, and, along with the rest of the staff at Nevis Laboratories, for further supporting me during this program. I would also like to acknowledge the National Science Foundation for funding this REU and making the research I and my fellow participants carried out possible.

References and Notes

[1] R. Acciarri et al. MicroBooNE Collaboration, Design and Construc- tion of the MicroBooNE Detector, JINST 12, no. 02, P02017 (2017) doi:10.1088/1748-0221/12/02/P02017 [arXiv:1612.05824 [physics.ins-det]].

[2] C. Callahan, MicroBooNE Collaboration, Description and Simulation of a Proposed Data-Compression Scheme for the BooNE TPC, MicroBooNE- doc-4473-v2

[3] A. Fadeeva, MicroBooNE Collaboration, Supernova Continuous Readout Stream: Compression Algorithm Studies, MicroBooNE-doc-12533-v1

[4] A. Ankowski et al., Supernova Physics at DUNE, arXiv:1608.07853 [hep- ex].

[5] R. Acciarri et al. [DUNE Collaboration], Long-Baseline Neutrino Fa- cility (LBNF) and Deep Underground Neutrino Experiment (DUNE) : Conceptual Design Report, Volume 1: The LBNF and DUNE Projects, arXiv:1601.05471 [physics.ins-det].

18 [6] MicroBooNE Collaboration, A Method to Extract the Charge Distribu- tion Arriving at the TPC Wire Planes in MicroBooNE, MICROBOONE- NOTE-1017-PUB v1.0

[7] I. Tamborra, Supernova Neutrinos: Theory, arXiv:1604.07332 [hep-ph].

All ROOT/C++ codes can be found in the repository: https://github.com/claraberger0/microboone

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