Machine Learning Applied To Background Events Identification In LUX Dark Matter Experiment

In most LUX data analyses, the collaboration have mostly relied on the LUX Data Processing Framework’s output known as the "reduced quantities" (e.g. event energy, position etc.). However some info embedded in the unprocessed multichannel photomultiplier tube (PMT) time traces were lost in the processing. To extract these info and improve current existing analysis, a technique uses convolutional neural networks (CNN) in the discrimination between a single S2 vertex vs. a double S2 vertex was developed. After training the CNN with all 122 channels of PMT waveforms, it can correctly identify double S2s that are partially merged together and look like a single vertex but with substructure. Such merged vertex signal would arise from two simultaneous particle interactions that occur within a few mm. Implementing this new identification technique not only reduces the hand scanning effort required, but also improves our analyses in (1) eliminating backgrounds from conventional source, and (2) Identifying rare event signals. The preliminary results of applying this technique to different physics searches such as xenon isotopes double decay half life estimate will be presented.