KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen

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KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen^*

A. Li, Z. Fu, C. Grant, H. Ozaki, I. Shimizu, H. Song, A. Takeuchi, and L. A. Winslow

Phys. Rev. C 107, 014323 – Published 30 January 2023

Abstract

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 the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for $0 ν β β$ . Using a simplified background model for KamLAND, we show that KamNet outperforms a conventional convolutional neural network (CNN) on benchmarking Monte Carlo simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to $0 ν β β$ and $2 ν β β$ decay to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.

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Received 6 March 2022
Accepted 14 September 2022

DOI:https://doi.org/10.1103/PhysRevC.107.014323

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal "citation, and DOI.

^*Code Repository: https://github.com/aobol/KamNet.git

Physics Subject Headings (PhySH)

Neutrinoless double beta decay

Artificial neural networks Machine learning Scintillators

Nuclear Physics

Authors & Affiliations

A. Li ^1,†, Z. Fu ², C. Grant³, H. Ozaki ⁴, I. Shimizu ⁴, H. Song³, A. Takeuchi⁴, and L. A. Winslow ²

¹Department of Physics and Astronomy, University of North Carolina, Chapel Hill, North Carolina 27514, USA and Triangle Universities Nuclear Laboratory, Durham, North Carolina 27708, USA
²Laboratory of Nuclear Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
³Department of Physics, Boston University, Boston, Massachusetts 02215, USA
⁴Research Center for Neutrino Science, Tohoku University, Sendai 980-8578, Japan

^†Corresponding author: liaobo77@ad.unc.edu

Search for the Majorana Nature of Neutrinos in the Inverted Mass Ordering Region with KamLAND-Zen

S. Abe et al. (KamLAND-Zen Collaboration)

Phys. Rev. Lett. 130, 051801 (2023)

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Vol. 107, Iss. 1 — January 2023

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Physical Review C

covering nuclear physics