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T: Fachverband Teilchenphysik

T 20: Neutrinophysik I

T 20.3: Vortrag

Montag, 19. März 2018, 16:35–16:50, Z6 - SR 2.012

Charge-only energy reconstruction with Convolutional Neural Networks for the EXO-200 experiment — •Tobias Ziegler¹, Michael Jewell², Sebastian Schmidt¹, Jürgen Hößl¹, 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 of ¹³⁶Xe with a single-phase liquid xenon (LXe) time projection chamber (TPC) filled with enriched LXe. The TPC provides the position (X,Y,Z) of events and the deposited energy in LXe by simultaneously detecting the xenon scintillation light and the amount of secondary electrons. For charge collection, electrons drift in the electric field towards the anode, where they induce currents in a first plane of wires and are collected by a second plane of wires. In this study, we investigate the energy reconstruction of events with single or multiple charge deposits using all available collection wires. We apply Deep Learning methods, esp. Convolutional Neural Networks, to reconstruct the charge-only energy deposition in the EXO-200 experiment and compare its performance to the conventional approach.