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 neutrinoless double beta decay ($0\nu\beta\beta$). Using a simplified background model for KamLAND we show that KamNet outperforms a conventional CNN on benchmarking MC simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ 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.
翻译:稀有事件搜索使我们得以在无法通过其他手段获得的能量尺度上搜索新的物理学。 机器学习提供了一种新工具, 以尽量扩大这些探测器提供的信息。 信息稀少, 迫使这些算法从最低水平的数据开始, 并利用探测器中的所有对称来产生结果。 在这项工作中, 我们展示了 KamNet, 它利用了几何深深度学习和空间时空数据分析方面的突破, 以最大限度地提高KamLaland- Zen物理学的敏感度, 一个千吨比例的球状液体焚化器探测器, 寻找无中子双乙型衰变( 0\ nu\ beta\ beta$ ) 。 使用一个简化的背景模型, 我们用一个简化的背景模型来显示 KamNet 超越了常规的CNN, 以越来越强的强度来设定MC 模拟的基准。 使用模拟数据, 我们然后展示 KamNet 能够提高Kamland- Zen 的敏感度至 $0\ nu\ beta\ beta$, 和 $0\\ nuta\ beta\ beta $ to ext state state state.