Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

Calorimeters play an important role in high-energy physics experiments. Their design includes electronic instrumentation, signal processing chain, computing infrastructure, and also a good understanding of their response to particle showers produced by the interaction of incoming particles. This is usually supported by full simulation frameworks developed for specific experiments so that their access is restricted to the collaboration members only. Such restrictions limit the general-purpose developments that aim to propose innovative approaches to signal processing, which may include machine learning and advanced stochastic signal processing models. This work presents the Lorenzetti Showers, a general-purpose framework that mainly targets supporting novel signal reconstruction and triggering strategies using segmented calorimeter information. This framework fully incorporates developments down to the signal processing chain level (signal shaping, energy estimation, and noise mitigation techniques) to allow advanced signal processing approaches in modern calorimetry and triggering systems. The developed framework is flexible enough to be extended in different directions. For instance, it can become a tool for the phenomenology community to go beyond the usual detector design and physics process generation approaches.量能器（Calorimeter）在高能物理实验中发挥着至关重要的作用。其设计涵盖电子仪器、信号处理链路、计算基础设施，同时需要充分理解其对入射粒子相互作用产生的粒子簇射的响应特性。这类响应特性的研究通常依托针对特定实验开发的全仿真框架，而此类框架的使用权限仅对实验合作组成员开放。此类权限限制了面向通用场景的研发工作——这类研发旨在提出创新性信号处理方案，其中可涵盖机器学习与先进随机信号处理模型。本研究推出Lorenzetti Showers通用框架，其主要致力于依托分段式量能器信息，支撑新型信号重建与触发策略的研发。该框架完整覆盖至信号处理链路层面的各类技术实现，包括信号整形、能量估计与噪声抑制技术，可支持现代量能测量与触发系统中的先进信号处理方案落地。所开发的框架具备良好的可扩展性，可向多个方向进行拓展。例如，该框架可成为高能物理唯象学研究团队的工具，助力其突破常规探测器设计与物理过程生成方法的局限。