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. Program Title: Lorenzetti Showers CPC Library link to program files:https://doi.org/10.17632/sy64367452.1 Developer's repository link:https://github.com/lorenzetti-hep/lorenzetti Licensing provisions: GPLv3 Programming language: Python, C++. Nature of problem: In experimental high-energy physics, simulation is essential for experiment preparation, design and interpretations of ongoing acquisitions. Especially for calorimeters, an accurate simulation that can describe detector geometry, behavior to different physics processes and signal generation close to the readout electronics and data acquisition levels is required to properly develop signal processing and computational methods. Such detectors may face very challenging demands arising from the new designs, such as pileup mitigation and noise reduction tasks under unprecedented levels. In this sense, simulation requirements continuously increase in complexity and performance, because new physics searches require large datasets and accurate modeling to experimental effects. Solution method: The Lorenzetti Showers is an integrated software framework that provides complete calorimeter informati