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2018

Deep Neural Networks for Energy and Position Reconstruction in EXO-200

Delaquis, S., Jewell, M. J., Ostrovskiy, I. et al.

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We apply deep neural networks (DNN) to data from the EXO-200 experiment.

In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions.
For the first time, the developed algorithms are evaluated on real detector calibration data.
The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment.

Deep Neural Networks for Energy and Position Reconstruction in EXO-200 · Around

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