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Generative models for fast simulation of Cherenkov detectors at the electron–ion collider
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Generative models for fast simulation of Cherenkov detectors at the electron–ion collider
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Journal Article
Generative models for fast simulation of Cherenkov detectors at the electron–ion collider
2025
Overview
The integration of deep learning (DL) into experimental nuclear and particle physics has driven significant progress in simulation and reconstruction workflows. However, traditional simulation frameworks such as Geant4 remain computationally intensive, especially for Cherenkov detectors, where simulating optical photon transport through complex geometries and reflective surfaces introduces a major bottleneck. To address this, we present an open, standalone fast simulation tool for detection of internally reflected Cherenkov light (DIRC) detectors, with a focus on the high-performance DIRC at the future electron–ion collider. Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks by offering a scalable, graphical processing unit-accelerated alternative to full Geant4 -based simulations. Designed with accessibility in mind, our simulation package enables both DL researchers and physicists to efficiently generate high-fidelity large-scale datasets on demand, without relying on complex traditional simulation stacks. This flexibility supports the development and benchmarking of novel DL-driven PID methods.
Publisher
IOP Publishing
