March 4, 2022, 2:12 a.m. | Aobo Li, Zhenghao Fu, Lindley A. Winslow, Christopher P. Grant, Hasung Song, Hideyoshi Ozaki, Itaru Shimizu, Atsuto Takeuchi

cs.LG updates on arXiv.org arxiv.org

Rare event searches allow us to search for new physics at energy scales
inaccessible with other means by leveraging specialized large-mass detectors.
Machine learning provides a new tool to maximize the information provided by
these detectors. The information is sparse, which forces these algorithms to
start from the lowest level data and exploit all symmetries in the detector to
produce results. In this work we present KamNet which harnesses breakthroughs
in geometric deep learning and spatiotemporal data analysis to maximize …

arxiv deep neural network event network neural network physics search

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