Pattern Recognition and Event Classification in the Search for Neutrinoless Double-Beta Decay with SuperNEMO

超NEMO是一项目前正在进行的实验，目的是寻找无中微子的双β衰变，从而解决与中微子的性质和宇宙物质-反物质不对称的起源有关的基本问题。该项目由伦敦大学学院联合领导，通过结合跟踪和量热信息来寻找半衰期远远长于宇宙年龄的极其罕见的核衰变模式，在该领域是独一无二的。与任何其他相关实验相比，SuperNEMO提供了更丰富的每个事件的信息。跟踪器的命中位置和漂移时间，以及量热计的计时和能量测量，结合在一起，给出了每个事件的图片。虽然从这个意义上说，探测原理类似于对撞机实验，但在实践中，由于低能电子和γ射线的广泛散射，以及各种空间分布鲜为人知的背景同位素(探测器内部和外部)，挑战大不相同。SuperNEMO目前在基于CUT的事件分类之前，使用一系列算法--集群、轨迹拟合和量热计关联--将信号与背景分离。由于每个阶段都是按顺序执行的，后一个阶段可能不会对前几个阶段产生影响--例如，如果粒子轨迹被错误地分割，即使其他事件特征强烈支持这一假设，以后也没有机会重新组合轨迹。更复杂的拓扑经常被当前方法完全错误地重建和错误分类。一种更全面的事件分类方法，允许机器学习算法自己找到特征，将允许我们将这些步骤中的一些或全部结合起来，并产生明显更好的分析结果。

SuperNEMO is an experiment, currently being commissioned, to search for neutrinoless double-beta decay and thereby address fundamental questions regarding the nature of the neutrino and the origin of the cosmic matter-antimatter asymmetry. The project, jointly led by UCL, is unique in the field through its combination of tracking and calorimetric information in the search for extremely rare nuclear decay modes with half-lives much longer than the age of the universe. SuperNEMO provides much richer per-event information than any other related experiment. Tracker hit positions and drift times, together with calorimeter timing and energy measurements, are combined to give a picture of each event. Although in this sense the detection principles are similar to those of collider experiments, in practice the challenges are quite different due to the extensive scattering of low-energy electrons and Y-rays and the wide variety of background isotopes with poorly known spatial distributions (both internal and external to the detector). SuperNEMO currently separates signal from background using a sequence of algorithms - clustering, track fitting and calorimeter association, prior to cut-based event classification. As each stage is performed sequentially, the latter stages can have no influence on the earlier ones - for example, if a particle track has been wrongly split there is no opportunity to recombine tracks later, even if other event characteristics strongly favour this hypothesis. More complex topologies are often completely mis-reconstructed and mis-categorised by the current approach. A more holistic event classification approach where a machine learning algorithm was allowed to find features on its own would allow us to combine some or all of these steps and generate distinctly better analysis outcomes.