Anomaly Detection using Autoencoders on Fundamental LZ Signals

Searching for anomalous data is especially important in rare event searches like that of the LUX-ZEPLIN (LZ) experiment’s hunt for dark matter. While LZ’s data processing provides analyzer-friendly features for all data, searching for anomalous data after minimal reconstruction allows one to find anomalies not captured by reconstructed features, agnostic to any reconstruction errors. Autoencoders can be used to probe for anomalous light-detecting photomultiplier tube (PMT) waveforms resulting from ionization signals (S2) and have found unresolved S2s resulting from multiple scatter interactions. These techniques can be extended by applying them to prompt scintillation light (S1) PMT waveforms and by analyzing the latent-spaces of variational autoencoders (VAEs).

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701191
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Anomaly Detection using Autoencoders on Fundamental LZ Signals

Tyler Anderson, M. Arthurs, M. E. Monzani
EPJ Web of Conferences
Anomaly Detection Techniques and Applications
article

Anomaly Detection using Autoencoders on Fundamental LZ Signals

Tyler Anderson, M. Arthurs, M. E. Monzani
article en

Abstract

Searching for anomalous data is especially important in rare event searches like that of the LUX-ZEPLIN (LZ) experiment’s hunt for dark matter. While LZ’s data processing provides analyzer-friendly features for all data, searching for anomalous data after minimal reconstruction allows one to find anomalies not captured by reconstructed features, agnostic to any reconstruction errors. Autoencoders can be used to probe for anomalous light-detecting photomultiplier tube (PMT) waveforms resulting from ionization signals (S2) and have found unresolved S2s resulting from multiple scatter interactions. These techniques can be extended by applying them to prompt scintillation light (S1) PMT waveforms and by analyzing the latent-spaces of variational autoencoders (VAEs).

EPJ Web of ConferencesVol. 337
SLAC National Accelerator Laboratory (US), Kavli Institute for Particle Astrophysics and Cosmology (US), Stanford University (US)
Openalex Percentile: Top 85%
Anomaly Detection Techniques and Applications
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Anomaly Detection using Autoencoders on Fundamental LZ Signals — Tyler Anderson, M. Arthurs, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS