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A Deep-Learning Based Waveform Region-of-interest Finder for the Liquid Argon Time Projection Chamber

R. Acciarri, I. Lepetic, A. M. Szelc, C. James, B. T. Fleming, Xiaobo Luo, V. Basque, B. Baller,M.H.L.S. Wang,T. Yang, J. Spitz, P. Green, F. Cavanna, G. Scanavini,W. Wu,Lorenzo Uboldi, M. Soderberg, O. Palamara, R. S. Fitzpatrick

openalex(2021)

Cited 2|Views2
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Abstract
mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of $$\sim$$0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.
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