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T: Fachverband Teilchenphysik

T 29: Deep Learning II

T 29.6: Vortrag

Dienstag, 26. März 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.