{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/calogan-simulating-3d-high-energy-particle","title":"CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks","arxiv_id":"1712.10321","date":"2017-12-21","proceeding":null,"authors":["Michela Paganini","Luke de Oliveira","Benjamin Nachman"],"abstract":"The precise modeling of subatomic particle interactions and propagation\nthrough matter is paramount for the advancement of nuclear and particle physics\nsearches and precision measurements. The most computationally expensive step in\nthe simulation pipeline of a typical experiment at the Large Hadron Collider\n(LHC) is the detailed modeling of the full complexity of physics processes that\ngovern the motion and evolution of particle showers inside calorimeters. We\nintroduce \\textsc{CaloGAN}, a new fast simulation technique based on generative\nadversarial networks (GANs). We apply these neural networks to the modeling of\nelectromagnetic showers in a longitudinally segmented calorimeter, and achieve\nspeedup factors comparable to or better than existing full simulation\ntechniques on CPU ($100\\times$-$1000\\times$) and even faster on GPU (up to\n$\\sim10^5\\times$). There are still challenges for achieving precision across\nthe entire phase space, but our solution can reproduce a variety of geometric\nshower shape properties of photons, positrons and charged pions. This\nrepresents a significant stepping stone toward a full neural network-based\ndetector simulation that could save significant computing time and enable many\nanalyses now and in the future.","url_abs":"http://arxiv.org/abs/1712.10321v1","url_pdf":"http://arxiv.org/pdf/1712.10321v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"calogan-simulating-3d-high-energy-particle","repo_url":"https://github.com/hep-lbdl/CaloGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"calogan-simulating-3d-high-energy-particle","repo_url":"https://github.com/ian-pang/ad_with_cf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"calogan-simulating-3d-high-energy-particle","repo_url":"https://github.com/ian-pang/regression_with_cf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"calogan-simulating-3d-high-energy-particle","repo_url":"https://gitlab.com/claudius-krause/caloflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[{"slug":"electromagnetic-calorimeter-shower-images","name":"Electromagnetic Calorimeter Shower Images","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.10321","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}