Aachen 2019 – scientific programme
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T: Fachverband Teilchenphysik
T 29: Deep Learning II
T 29.6: Talk
Tuesday, March 26, 2019, 17:15–17:30, H06
Signal-background discrimination with Deep Learning in the EXO-200 experiment — •Tobias Ziegler1, Mike Jewell2, Johannes Link1, Federico Bontempo1, Gisela Anton1, and Thilo Michel1 — 1Friedrich-Alexander-Universität Erlangen-Nürnberg, ECAP — 2Stanford University, California, USA
The EXO-200 experiment searches for the neutrinoless double beta (0νββ) decay in 136Xe with an ultra-low background single-phase time projection chamber (TPC) filled with 175 kg isotopically enriched liquid xenon (LXe). The detector has demonstrated good energy resolution and background rejection capabilities by simultaneously collecting scintillation light and ionization charge from the LXe and by a multi-parameter analysis. Advances in computational performance in recent years have made novel Deep Learning techniques applicable to the physics community. This contribution presents the concept of the detector and summarizes the work on applying Deep Learning methods for signal-background discrimination in the EXO-200 experiment.