{"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/deep-neural-networks-for-physics-analysis-on","title":"Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC","arxiv_id":"1711.03573","date":"2017-11-09","proceeding":null,"authors":["Wahid Bhimji","Steven Andrew Farrell","Thorsten Kurth","Michela Paganini","Prabhat","Evan Racah"],"abstract":"There has been considerable recent activity applying deep convolutional\nneural nets (CNNs) to data from particle physics experiments. Current\napproaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,\nand for identifying objects or particular particle types. We explore approaches\nthat use the entire calorimeter, combined with track information, for directly\nconducting physics analyses: i.e. classifying events as known-physics\nbackground or new-physics signals.\n  We use an existing RPV-Supersymmetry analysis as a case study and explore\nCNNs on multi-channel, high-resolution sparse images: applied on GPU and\nmulti-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)\non the Cori supercomputer at NERSC.","url_abs":"http://arxiv.org/abs/1711.03573v2","url_pdf":"http://arxiv.org/pdf/1711.03573v2.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":"deep-neural-networks-for-physics-analysis-on","repo_url":"https://github.com/NERSC/pytorch-examples","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-neural-networks-for-physics-analysis-on","repo_url":"https://github.com/sparticlesteve/cori-intml-examples","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-neural-networks-for-physics-analysis-on","repo_url":"https://github.com/sparticlesteve/pytorch-examples","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-neural-networks-for-physics-analysis-on","repo_url":"https://github.com/vmos1/atlas_cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-neural-networks-for-physics-analysis-on","repo_url":"https://github.com/vpayyar/layered_CNNs_for_ATLAS_data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}