Neural-network-driven proton decay sensitivity in the $p\rightarrow \bar{\nu} K^{+}$ channel using large liquid argon time projection chambers
Christoph Alt, Balint Radics, Andr\'e Rubbia

TL;DR
This paper demonstrates that large liquid argon time projection chambers can significantly improve sensitivity to proton decay in the $p ightarrow ar{ u} K^+$ channel, achieving a lifetime sensitivity over 7 x 10^{34} years with advanced simulation and neural network techniques.
Contribution
The study introduces an improved neural network-based kaon identification method and comprehensive simulation to enhance proton decay sensitivity estimates in large LAr TPCs.
Findings
Achieves a proton lifetime sensitivity > 7 x 10^{34} years at 90% CL.
Confirms the superiority of LAr TPCs over other detector technologies.
Demonstrates the effectiveness of neural networks in particle identification.
Abstract
We report on an updated sensitivity for proton decay via at large, dual phase liquid argon time projection chambers (LAr TPCs). Our work builds on a previous study in which several nucleon decay channels have been simulated and analyzed [arXiv:hep-ph/0701101]. At the time several assumptions were needed to be made on the detector and the backgrounds. Since then, the community has made progress in defining these, and the computing power available enables us to fully simulate and reconstruct large samples in order to perform a better estimate of the sensitivity to proton decay. In this work, we examine the benchmark channel , which was previously found to be one of the cleanest channels. Using an improved neutrino event generator and a fully simulated LAr TPC detector response combined with a dedicated neural network for kaon…
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