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Abstract The search for a dark photon holds considerable interest in the physics community. Such a force carrier would begin to illuminate the dark sector. Many experiments have searched for such a particle, but so far it has proven elusive. In recent years the concept of a low mass dark photon has gained popularity in the physics community. Of particular recent interest is the 8 Be and 4 He anomaly, which could be explained by a new fifth force carrier with a mass of 17 MeV/ c 2 . The proposed Darklight experiment would search for this potential low mass force carrier at ARIEL in the 10-20 MeV/ c 2 e + e − invariant mass range. This proceeding will focus on the experimental design and physics case of the Darklight experiment. 

Abstract A new search for two-neutrino double-beta (2νββ) decay of¹³⁶Xe to theexcited state of¹³⁶Ba is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90% confidence level half-life sensitivity of 2νββdecay to thestate of¹³⁶Ba isyr using a total¹³⁶Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββdecay to thestate is observed, leading to a lower limit ofyr at 90% confidence level, improved by 70% relative to the current world's best constraint. 

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.