Deep generative models for detector signature simulation: A taxonomic review-Reference-Cited by-同舟云学术

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Deep generative models for detector signature simulation: A taxonomic review

Published:2024-12 Issue: Volume:12 Page:100092
ISSN:2405-4283
Container-title:Reviews in Physics
language:en
Short-container-title:Reviews in Physics

Author:

Hashemi Baran^ORCID,Krause Claudius^ORCID

Publisher

Elsevier BV

Reference371 articles.

1. Machine learning and LHC event generation;Butter;SciPost Phys.,2023

2. Event generators for high-energy physics experiments;Campbell,2024

3. Geant4—a simulation toolkit;Agostinelli;Nucl. Instrum. Methods Phys. Res. A,2003

4. Geant4 developments and applications;Allison;IEEE Trans. Nucl. Sci.,2006

5. Recent developments in Geant4 - ScienceDirect, URL https://www.sciencedirect.com/science/article/pii/S0168900216306957.

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Foundations of automatic feature extraction at LHC–point clouds and graphs;The European Physical Journal Special Topics;2024-09-11

2. Systematic evaluation of generative machine learning capability to simulate distributions of observables at the large hadron collider;The European Physical Journal C;2024-09-09

3. Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows;Journal of Instrumentation;2024-09-01

4. Unsupervised and lightly supervised learning in particle physics;The European Physical Journal Special Topics;2024-07-08

5. How deep learning is complementing deep thinking in ATLAS;The European Physical Journal Special Topics;2024-07-05

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