Bayesian Deep Learning for Shower Parameter Reconstruction in Water Cherenkov Detectors

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  • <p>ICRC</p>

    ICRC

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  • uploaded June 25, 2021

Discussion timeslot (ZOOM-Meeting): 13. July 2021 - 12:00
ZOOM-Meeting URL: https://desy.zoom.us/j/98542982538
ZOOM-Meeting ID: 98542982538
ZOOM-Meeting Passcode: ICRC2021
Corresponding Session: https://icrc2021-venue.desy.de/channel/52-Analysis-Methods-Catalogues-Community-Tools-Machine-Learning-GAD-GAI/64
Live-Stream URL: https://icrc2021-venue.desy.de/livestream/Discussion-04/5

Abstract:
'Deep Learning methods are among the state-of-art of several computer vision tasks, intelligent control systems, fast and reliable signal processing and inference in big data regimes. It is also a promising tool for scientific analysis such as gamma/hadron discrimination.rnWe present an approach based on Deep Learning for the regression of shower parameters, namely its core position and energy at the ground, using water Cherenkov detectors. We design our method using simulations. In this contribution, we explore the recovery of the shower’s center coordinates. We evaluate the limits of such estimation near the borders of the arrays, including the when the center is outside the detector’s range. We also address the feasibility of recovering other parameters, such as ground energy. We used Bayesian Neural Networks and derived and quantified systematic errors arising from Deep Learning models and optimized the network design. The method could be easily adapted to estimate other parameters.'

Authors: Clecio R. Bom
Co-Authors: Luciana O. Dias | Ruben Conceição | Bernardo Tomé | Ulisses Barres de Almeida | Arthur Moraes | Mário Pimenta | Ronald Shellard | Márcio P. Albuquerque
Indico-ID: 932
Proceeding URL: https://pos.sissa.it/395/739

Tags:
Presenter: Clecio R. Bom