Speaker
Description
Lorenzetti Showers was developed in 2022 by integrating particle generation and detector interaction simulation, providing an easy-to-use interface. The aim was to allow the HEP community at large to simulate calorimeters for collider experiments, including a full electronic signal processing chain, and access to instrumental pulses, enabling studies of electronic circuits, signal processing, machine learning, and triggering.
Since then, several updates have been implemented, including integration with HEPMC and DD4hep, and features such as: capacitive and inductive crosstalk between adjacent calorimeter cells that degrade reconstruction of energy and time; distortions that simulate hardware malfunctioning; emulation of online calorimeter cells suppression for information reduction when readings are below specific thresholds; and a overlay strategy to accelerate simulations with high pileup.
Extensions beyond the original calorimeter design are under development, including integration of a tracking system and prototype detectors for FCC, providing a versatile tool for developing instrumentation and signal processing chains for future detectors. Lorenzetti Showers is also used to provide training data for generative neural networks for ultra-fast simulations that generate calorimeter-based reconstructed level features of particle showers. Such tools would become important for improving recasting frameworks that will preserve LHC's legacy results in the coming years.
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