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（Calorimeters）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系统（electronic instrumentation）、信号处理链路（signal processing chain）与计算基础设施（computing infrastructure），同时需要充分理解其对入射粒子（incoming particles）相互作用所产生粒子簇射（particle showers）的响应特性。此类响应特性通常依托为特定实验开发的全模拟框架（full simulation frameworks）进行验证，但这类框架的访问权限仅对实验合作组成员开放。此类访问限制制约了通用研发工作的开展——此类研发旨在提出面向信号处理的创新方案，其中可涵盖机器学习（machine learning）与高级随机信号处理模型（stochastic signal processing models）。本工作提出洛伦泽蒂簇射（Lorenzetti Showers）这一通用框架，其核心目标是借助分段式量能器（segmented calorimeter）信息，支撑新型信号重建（signal reconstruction）与触发策略（triggering strategies）的研究。该框架完整覆盖至信号处理链路层面的相关技术开发，包括信号整形（signal shaping）、能量估计（energy estimation）与噪声抑制（noise mitigation）技术，从而可在现代量能测量（calorimetry）与触发系统中应用先进的信号处理方案。所开发的框架具备良好的可扩展性，可沿多个方向进行拓展。例如，该框架可作为工具，助力唯象学研究共同体（phenomenology community）突破常规探测器设计（detector design）与物理过程生成（physics process generation）方法的局限。