Machine Learning-Based Tagging of SiPM Crosstalk in LEGEND-200.
POSTER
Abstract
LEGEND is a Ge-76 based search aiming to discover whether neutrinos are Majorana in nature. This would provide a potential explanation for the observed matter-antimatter asymmetry. To reject backgrounds caused by external gamma sources, LEGEND employs a liquid argon light readout system instrumented with silicon photomultipliers (SiPMs). Currently, certain forms of crosstalk between the SiPMs and the germanium detectors are being missed by traditional data tagging methods, leading to low 0vββ signal efficiency if liquid argon trigger thresholds are lowered. To address this, a machine learning based tag was developed through the use of unsupervised affinity propagation (AP) and a supervised support vector machine (SVM). The resulting model has demonstrated the ability to tag crosstalk not previously detected by traditional methods.
*This work is supported by the U.S. DOE, and the NSF, the LANL, ORNL and LBNL LDRD programs; the European ERC and Horizon programs; the German DFG, BMBF, and MPG; the Italian INFN; the Polish NCN and MNiSW; the Czech MEYS; the Slovak RDA; the Swiss SNF; the UK STFC; the Canadian NSERC and CFI; the LNGS and SURF facilities. This material is based upon work supported by the U.S. DOE-SC, Office of Nuclear Physics under Award Number DE-SC, Office of Nuclear Physics under Award Number DE-SC0022339.
Presenters
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Mara Mayhew
- University of North Carolina at Chapel Hill