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Abstract Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted. 

Abstract We present results of several measurements of CsI[Na] scintillation response to 3–60 keV energy nuclear recoils performed by the COHERENT collaboration using tagged neutron elastic scattering experiments and an endpoint technique. Earlier results, used to estimate the coherent elastic neutrino-nucleus scattering (CEvNS) event rate for the first observation of this process achieved by COHERENT at the Spallation Neutron Source (SNS), have been reassessed. We discuss corrections for the identified systematic effects and update the respective uncertainty values. The impact of updated results on future precision tests of CEvNS is estimated. We scrutinize potential systematic effects that could affect each measurement. In particular we confirm the response of the H11934-200 Hamamatsu photomultiplier tube (PMT) used for the measurements presented in this study to be linear in the relevant signal scale region. 

Aeolis Mons (informally, Mount Sharp) exhibits a number of canyons, including Gediz and Sakarya Valles. Poorly sorted debris deposits are evident on both canyon floors and connect with debris extending down the walls for canyon segments that cut through sulphate‐bearing strata. On the floor of Gediz Vallis, debris overfills a central channel and merges with a massive debris ridge located at the canyon terminus. One wall‐based debris ridge is evident. In comparison, the floor of Sakarya Vallis exhibits a complex array of debris deposits. Debris deposits on wall segments within Sakarya Vallis are mainly contained within chutes that extend downhill from scarps. Lateral debris ridges are also evident on chute margins. We interpret the debris deposits in the two canyons to be a consequence of one or more late‐stage hydrogeomorphic events that increased the probability of landslides, assembled and channelized debris on the canyon floors, and moved materials down‐canyon. The highly soluble nature of the sulphate‐bearing rocks likely contributed to enhanced debris generation by concurrent aqueous weathering to produce blocky regolith for transport downslope by fluvial activity and landslides, including some landslides that became debris flows. Subsequent wind erosion in Gediz Vallis removed most of the debris deposits within that canyon and partially eroded the deposits within Sakarya Vallis. The enhanced wind erosion within Gediz Vallis was a consequence of the canyon's alignment with prevailing slope winds.