Bulletin of the American Physical Society
Fall 2022 Meeting of the APS Division of Nuclear Physics
Volume 67, Number 17
Thursday–Sunday, October 27–30, 2022; Time Zone: Central Daylight Time, USA; New Orleans, Louisiana
Session LD: Mini-Symposium: The Physics of Double Beta Decay - Machine Learning Tools; Neutrinos I; Neutrino Mass I
2:00 PM–3:24 PM,
Saturday, October 29, 2022
Hyatt Regency Hotel
Room: Celestin B
Chair: Pranava Teja Surukuchi, Yale University
Abstract: LD.00003 : Machine Learning Denoising of nEXO's Time Project Chamber using an Unsupervised Variational Autoencoder*
2:24 PM–2:36 PM
Presenter:
Jason P Brodsky
(Lawrence Livermore Natl Lab)
Author:
Jason P Brodsky
(Lawrence Livermore Natl Lab)
Collaboration:
nEXO Collaboration
This talk presents the preliminary results of a method for denoising nEXO’s charge sensors using unsupervised machine learning. Because nEXO’s charge signal depends only on the position and shape of a single charge deposit, the charge waveforms can be described using a physically-motivated small number of dimensions. Using a variational autoencoder, these signals can be encoded into this low-dimensional latent space and then decoded, naturally suppressing the random noise which cannot be described in so few dimensions.
This approach can operate fully unsupervised on experimental output without requiring simulations to train the autoencoder.
*This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-836638
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