{"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/learning-particle-physics-by-example-location","title":"Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis","arxiv_id":"1701.05927","date":"2017-01-20","proceeding":null,"authors":["Luke de Oliveira","Michela Paganini","Benjamin Nachman"],"abstract":"We provide a bridge between generative modeling in the Machine Learning\ncommunity and simulated physical processes in High Energy Particle Physics by\napplying a novel Generative Adversarial Network (GAN) architecture to the\nproduction of jet images -- 2D representations of energy depositions from\nparticles interacting with a calorimeter. We propose a simple architecture, the\nLocation-Aware Generative Adversarial Network, that learns to produce realistic\nradiation patterns from simulated high energy particle collisions. The pixel\nintensities of GAN-generated images faithfully span over many orders of\nmagnitude and exhibit the desired low-dimensional physical properties (i.e.,\njet mass, n-subjettiness, etc.). We shed light on limitations, and provide a\nnovel empirical validation of image quality and validity of GAN-produced\nsimulations of the natural world. This work provides a base for further\nexplorations of GANs for use in faster simulation in High Energy Particle\nPhysics.","url_abs":"http://arxiv.org/abs/1701.05927v2","url_pdf":"http://arxiv.org/pdf/1701.05927v2.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":"learning-particle-physics-by-example-location","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":"learning-particle-physics-by-example-location","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":"learning-particle-physics-by-example-location","repo_url":"https://github.com/hep-lbdl/adversarial-jets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.05927","atlas_url":"https://app.syntology.ai/?focus=1701.05927","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}