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Neutron-Antineutron Oscillation in Liquid Argon Time Projection Chambers

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Neutron-Antineutron Oscillation in Liquid Argon Time Projection Chambers Neutron-Antineutron Oscillation in Liquid Argon Time Projection Chambers Jeremy Hewes, on behalf of the DUNE collaboration International Workshop on Baryon & Lepton Number Violation 2017 1 Simulated n-n̄ event in MicroBooNE Liquid Argon Time Projection Chambers • US-based international collaborations have Sense Wires adopted liquid argon time projection chamber U V Y V wire plane waveforms Liquid Argon TPC for the next generation of neutrino detectors. Charged Particles Cathode Plane Incoming Neutrino Edrift Y wire plane waveforms t ArgoNeuT MicroBooNE • Fermilab-based LArTPC experiments: DUNE • LArIAT (operating). • MicroBooNE (operating). • SBND & Icarus (beginning 2018). • DUNE (beginning 2024). Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 2 Deep Underground Neutrino Experiment • DUNE will consist of four 10kt LArTPC modules, 1.5km underground at Sanford Lab in Lead, SD. • Primary physics goals are the measurement of the neutrino mass ordering, and the CP-violating phase δCP. • One 10kt module will be commissioned per year, beginning 2024, until the full detector is operational. Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs arXiv: 1512.06148 3 Rare processes in DUNE • Large-mass detector, deep underground in low- DUNE p → K+ν̄ sensitivity background environment — ideal for rare arXiv: 1512.06148 physics searches! • DUNE has a broad variety of secondary physics goals: • Supernova neutrinos. • Dark matter searches. • BNV + LNV processes. Proton decay mode comparison (DUNE vs WC) • Search for BNV processes: arXiv: 1512.06148 • Proton decay. • Neutron-antineutron oscillation. See also: talks by J. Barrow, E. Kearns, R. Mohapatra, M. Snow Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 4 Neutron-antineutron oscillation GENIE simulation • BNV process: neutron n n̄ → π+ π- 3π0 spontaneously oscillates into antineutron. • Search for subsequent annihilation of bound neutron inside nucleus. • Spherical π topology; ~2GeV invariant mass, n-n̄ oscillation branching ratios low net momentum. adapted from arXiv: 1109.4227 • Free oscillation lifetime limit set by Super- n p Kamiokande at 2.7 x 108 s at 90% CL. 1 p n n p n p Convert from bound to • ILL search in a free neutron beam set limit n̄ free lifetime using factor 8 2 n p n at 0.86 x 10 (90% CL). p from theory TR: n p • Earlier this month, SNO set a free- n p equivalent limit of 1.23 x 108 s (90% CL). 3 1. arXiv: 1109.4227 2. Z. Phys. C. V63, 409-416 Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 5 3. arXiv: 1705.00696 n-n̄ oscillation in DUNE • Qualitative arguments for DUNE’s ability to surpass limits set by existing Cherenkov detectors: • Large 40kt fiducial mass. • mm-level spatial resolution allows vertex identification. 60m • dE/dx information provides particle ID and calorimetry. 15m DUNE 10kt module MC truth n-n̄ topology in 40Ar Preliminary Preliminary Preliminary Number of primary particles Total event visible energy Event net momentum Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 6 Convolutional neural network approach • LArTPC is an image-based technology. • Recent MicroBooNE studies show promise in using convolutional neural networks for single- particle ID and neutrino event identification. • Investigate CNN’s ability to distinguish n-n̄ from atmospheric neutrino events. • High-level difference in shape of signal and background events. Shape of interaction n-n̄ event Atmospheric ν event Single-particle CNN classification in MicroBooNE Spherically symmetric Strong directionality arXiv: 1611.05531 Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 7 Input production • Monte Carlo samples of n-n̄ oscillation and atmospheric neutrino events generated within LArSoft framework. 1 • Events simulated using GENIE event generator. 2 • Simulate particle interactions inside detector, and detector response, including noise simulations. • Wire deconvolution performed, wire vs time images produced. • Region of interest selected, image downsampled and embedded in image of uniform size. Example signal event (left) and background event (right), with three wire plane event displays overlaid using the RGB information of a single image. 1. arXiv: 1311.6774 Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 8 2. arXiv: 0905.2517 Network training • Use version of Caffe CNN framework1 modified to interface with LArTPC data files. • VGG16 2 network trained with 50,000 signal and background events. • Network performs convolutions on input images to pick out complex features, and learns to associate these features with the event type. VGG16B network architecture Conv-64 Conv-128 Conv-256 Conv-512 Conv-512 Fully ReLU ReLU ReLU ReLU ReLU connected Conv-64 Conv-128 Conv-256 Conv-512 Conv-512 Input layers Score ReLU ReLU ReLU ReLU ReLU Soft-max Max pool Max pool Max pool Max pool Max pool 1. arXiv: 1408.5093 Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 9 2. arXiv: 1409.1556 Training metrics • Network learns by minimising a loss function, derived from network weights, which abstracts how many classification mistakes the network made. • Network also monitors accuracy — simply, the proportion of images classified correctly. • Reducing learning rate allows network to be fine-tuned after initial training. Preliminary Preliminary Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 10 Network classification • Network performance was tested by classifying a separate sample of 200,000 signal and background events. • The network scores each image between 0 (background-like) and 1 (signal-like). • Defining a cut on this score provides a signal selection efficiency and background rejection rate. Benchmarking CNN performance on 200k event samples Preliminary Preliminary Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 11 Signal classification — event displays 150 200 250 300 350 400 450 260 280 300 320 340 360 380 400 420 440 460 480 4200 4300 4000 3800 4200 3600 4100 3400 4000 3200 3900 3000 2800 3800 2600 3700 2400 0 200 400 600 800 1000 200 400 600 800 1000 4200 4300 4000 4200 3800 4100 3600 3400 4000 3200 3900 3000 3800 2800 3700 2600 3600 4300 4000 4200 3800 3600 4100 3400 4000 3200 3900 3000 3800 2800 3700 2600 3600 0 100 200 300 400 500 600 700 800 900 700 720 740 760 780 1 1 LArSoft 0.8 LArSoft 0.8 0.6 0.6 Run: 1/26 Run: 1/97 q [ADC] Well-classified signal event q [ADC] Poorly classified signal event Event: 1261 0.4 Event: 4842 0.4 0.2 0.2 UTC Fri Jan 2, 1981 UTC Fri Jan 2, 1981 0 0 00:13:47.974791616 0 500 1000CNN1500 score:2000 2500 1 3000 3500 4000 00:20:11.348169824 0 500CNN1000 score:1500 2000 0.001652500 3000 3500 4000 Well-contained, ideal n-n̄ topology t [ticks] Atmospheric-like n-n̄ topology t [ticks] Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 12 Background classification — event displays 250 300 350 400 450 390 400 410 420 430 440 450 460 470 480 4200 3300 4000 3250 3800 3200 3600 3150 3400 3100 3200 3000 3050 2800 3000 2600 2950 2400 300 400 500 600 700 800 900 1000 1100 0 100 200 300 400 500 600 700 800 4200 3300 4000 3250 3800 3200 3600 3150 3400 3100 3200 3050 3000 3000 2800 2950 2600 3300 4200 4000 3250 3800 3200 3600 3150 3400 3100 3200 3050 3000 3000 2800 2950 2600 2900 0 200 400 600 800 1000 455 460 465 470 475 480 485 490 495 500 1 1 LArSoft 0.8 LArSoft 0.8 0.6 0.6 Run: 1/363 Well-classified background event Run: 1/367Poorly classified background event Event: 18101 q [ADC] 0.4 Event: 18321 q [ADC] 0.4 0.2 0.2 UTC Sat Jan 3, 1981 UTC Sat Jan 3, 1981 0 CNN score: 0 0 CNN score: 0.9999573 14:42:18.180170768 0 500 1000 1500 2000 2500 3000 3500 4000 14:55:16.575819136 0 500 1000 1500 2000 2500 3000 3500 4000 Highly directional atmospheric ν topologyt [ticks] Poorly contained atmospheric ν topologyt [ticks] Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 13 n-n̄ sensitivity in DUNE • Optimised cut on CNN score of Preliminary 0.999966 provides a signal selection efficiency of 18.1% and a background mis-ID rate of ~10-5. • At this efficiency and background rate, DUNE’s sensitivity is 1.7 x 109 s (90% CL) after 10 years running. • Factor ~5 improvement over current best limit, 2.7 x 108 s (90% CL) from Super-Kamiokande. • A far more conservative cut of 0.99 equates to a sensitivity of Projected DUNE sensitivity 4.2 x 108 s (90% CL). Optimised CNN score cut, 10 years exposure Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 14 Conclusions • DUNE will be a very large, deep underground detector with excellent prospects for rare searches. • There are good qualitative arguments for its ability to improve on current lifetime limits for neutron-antineutron oscillation: • LArTPC provides excellent spatial resolution, tracking information, calorimetry. • Convolutional neural networks are able to very efficiently separate signal from background in Monte Carlo studies. • At optimum CNN performance, DUNE sensitivity for 10 years of running with 40kt 9 is 1.7 x 10 s (90% CL). • Great scope for further network improvements: longer training time, network fine- tuning, feature correlation across wire planes. • Convolutional neural networks have the potential to be extremely powerful tools in LArTPC rare decay searches. Jeremy Hewes - Neutron-antineutron oscillation in LArTPCs 15.
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