{"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/accelerating-science-with-generative","title":"Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters","arxiv_id":"1705.02355","date":"2017-05-05","proceeding":null,"authors":["Michela Paganini","Luke de Oliveira","Benjamin Nachman"],"abstract":"Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of\nparticle collisions to build expectations of what experimental data may look\nlike under different theory modeling assumptions. Petabytes of simulated data\nare needed to develop analysis techniques, though they are expensive to\ngenerate using existing algorithms and computing resources. The modeling of\ndetectors and the precise description of particle cascades as they interact\nwith the material in the calorimeter are the most computationally demanding\nsteps in the simulation pipeline. We therefore introduce a deep neural\nnetwork-based generative model to enable high-fidelity, fast, electromagnetic\ncalorimeter simulation. There are still challenges for achieving precision\nacross the entire phase space, but our current solution can reproduce a variety\nof particle shower properties while achieving speed-up factors of up to\n100,000$\\times$. This opens the door to a new era of fast simulation that could\nsave significant computing time and disk space, while extending the reach of\nphysics searches and precision measurements at the LHC and beyond.","url_abs":"http://arxiv.org/abs/1705.02355v2","url_pdf":"http://arxiv.org/pdf/1705.02355v2.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":"accelerating-science-with-generative","repo_url":"https://github.com/SchattenGenie/CaloGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"accelerating-science-with-generative","repo_url":"https://github.com/ezeeEric/DiVAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"accelerating-science-with-generative","repo_url":"https://github.com/hep-lbdl/CaloGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"accelerating-science-with-generative","repo_url":"https://github.com/qalosim/caloqvae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02355","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}