Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider

• URL: http://arxiv.org/abs/2504.19042v1
• Date: Sat, 26 Apr 2025 22:33:08 GMT
• Title: Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider
• Authors: James Giroux, Michael Martinez, Cristiano Fanelli,
• Abstract summary: We present an open, standalone fast simulation tool for Detection of Internally Reflected Cherenkov Light (DIRC) detectors.<n>Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks.<n>This flexibility supports the development and benchmarking of novel DL-driven PID methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (hpDIRC) at the future Electron-Ion Collider (EIC). Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks by offering a scalable, GPU-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. Moreover, this fast simulation pipeline represents a critical step toward enabling EIC-wide PID strategies that depend on virtually unlimited simulated samples, spanning the full acceptance of the hpDIRC.

Related papers

• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation [0.0]
  This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics.
  arXiv  Detail & Related papers  (2024-03-05T23:12:47Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research [0.0]
  Subatomic particle track reconstruction is a vital task in High-Energy Physics experiments. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo.
  arXiv  Detail & Related papers  (2023-08-30T12:50:45Z)
• Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss [0.0]
  We discuss Lamarr, a framework to speed-up the simulation production parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parameterize the high-level response of the single components of the LHCb detector.
  arXiv  Detail & Related papers  (2023-03-20T20:18:04Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Deep Transformer Networks for Time Series Classification: The NPP Safety Case [59.20947681019466]
  An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data. The Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy on the testing dataset.
  arXiv  Detail & Related papers  (2021-04-09T14:26:25Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)
• Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks [0.0]
  In this work, we demonstrate a novel approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adrial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. To evaluate the quality of the proposed model, we integrate it into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator.
  arXiv  Detail & Related papers  (2020-12-08T17:57:37Z)
• Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning [4.666011151359189]
  We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. We propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples.
  arXiv  Detail & Related papers  (2020-10-05T07:48:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.