Nuclear, Particle, Astroparticle and Cosmology (NUPAC) Seminars
Deep learning for improving directional recoil detection in 3D recoil-imaging gaseous time projection chambers (TPCs)
Presented by Dr. Jeff Schueler (University of Hawaii)
Recoil-imaging gas TPCs with MPGD charge readout are promising detector candidates for directional dark matter searches beneath the neutrino fog. Directional performance in such a TPC can be described using angular resolution and head/tail recognition efficiency, with the latter expected to be the most important performance metric for using directionality to reject solar neutrino backgrounds and establish the galactic origin of dark matter. In this talk, we detail ongoing angular resolution and head/tail studies using a 40cm3 micro-TPC with double GEM amplification and pixel ASIC readout, filled with a 70:30 mixture of He:CO2 at atmospheric pressure. We begin by introducing the use of convolutional neural networks (CNNs) to improve the angular resolution of simulated He recoils, placing emphasis on the choice of loss function to optimize the CNN's performance. We then compare head/tail assignment performance on He recoils -- both in simulation and in measurement -- using traditional Bragg-curve-based approaches, as well as CNNs. In our preliminary studies at a double GEM gain of O(1000), we found deep learning to substantially improve head/tail recognition for He recoils down to O(10 keV). We will also discuss our ongoing work and experimental challenges encountered in extending this work to much higher detector gain. In that regime, the detector has high efficiency to single primary electrons, which should eventually result in maximal directional sensitivity for keV-scale nuclear recoils.
2:00 pm, Tuesday, April 4, 2023
PAIS-3205, PAIS
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