论文标题
$ p \ rightarrow \barνk^{+} $通道使用大型液体氩时间投影室中的神经网络驱动的质子衰减灵敏度
Neural-network-driven proton decay sensitivity in the $p\rightarrow \barν K^{+}$ channel using large liquid argon time projection chambers
论文作者
论文摘要
我们在大的双相液体氩时间投影室（Lar tpcs）通过$ p \ rightarrow \barνk^+ $报告对质子衰变的更新灵敏度。我们的工作建立在先前的研究基础上，该研究已经模拟和分析了几个核子衰减通道[ARXIV：HEP-PH/0701101]。当时，需要在检测器和背景上做出几个假设。从那以后，社区在定义这些方面取得了进展，可用的计算能力使我们能够完全模拟和重建大型样本，以便更好地估计对质子衰变的敏感性。在这项工作中，我们检查了基准通道$ p \ rightarrow \barνk^{+} $，以前被认为是最干净的通道之一。使用改进的中微子事件发生器和完全模拟的LAR TPC检测器响应与专用神经网络进行KAON识别，我们证明了$τ/ \ text {br} \ left的寿命敏感性\％$的置信度可以在$ 1 \，\ text {megaton} \ cdot \ text {year} $的情况下达到$ 1 \，\％的置信度，在quasi-background-frogn-frogn-Forcround条件下，可以确保LAR TPC优于其他技术的优越性，以解决挑战性的Proton衰减模式。
We report on an updated sensitivity for proton decay via $p \rightarrow \barν K^+ $ 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 $p\rightarrow \barν K^{+}$, 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 identification, we demonstrate that a lifetime sensitivity of $ τ/ \text{Br} \left( p \rightarrow \barν K^+ \right) > 7 \times 10^{34} \, \text{years}$ at $90 \, \%$ confidence level can be reached at an exposure of $1 \, \text{megaton} \cdot \text{year}$ in quasi-background-free conditions, confirming the superiority of the LAr TPC over other technologies to address the challenging proton decay modes.
