{"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/pileup-mitigation-with-machine-learning-pumml","title":"Pileup Mitigation with Machine Learning (PUMML)","arxiv_id":"1707.08600","date":"2017-07-26","proceeding":null,"authors":["Patrick T. Komiske","Eric M. Metodiev","Benjamin Nachman","Matthew D. Schwartz"],"abstract":"Pileup involves the contamination of the energy distribution arising from the\nprimary collision of interest (leading vertex) by radiation from soft\ncollisions (pileup). We develop a new technique for removing this contamination\nusing machine learning and convolutional neural networks. The network takes as\ninput the energy distribution of charged leading vertex particles, charged\npileup particles, and all neutral particles and outputs the energy distribution\nof particles coming from leading vertex alone. The PUMML algorithm performs\nremarkably well at eliminating pileup distortion on a wide range of simple and\ncomplex jet observables. We test the robustness of the algorithm in a number of\nways and discuss how the network can be trained directly on data.","url_abs":"http://arxiv.org/abs/1707.08600v3","url_pdf":"http://arxiv.org/pdf/1707.08600v3.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":"pileup-mitigation-with-machine-learning-pumml","repo_url":"https://github.com/pkomiske/PUMML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.08600","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}