{"paper":{"title":"Probing Heavy Neutrinos at the LHC from Fat-jet using Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Hao Sun, Jing Li, Wei Liu, Zixiang Chen","submitted_at":"2023-03-28T12:21:18Z","abstract_excerpt":"We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \\rightarrow W^{\\pm *}\\rightarrow \\mu^{\\pm} N \\rightarrow \\mu^{\\pm} \\mu^{\\mp} W^{\\pm} \\rightarrow \\mu^{\\pm} \\mu^{\\mp} J$ at hadron colliders. We use either the Gradient Boosted Decision Tree or Multi-Layer Perceptron methods to analyse the observables incorporating the jet substructure information, which is performed at hadron colliders with $\\sqrt{s}=$ 13, 27, 100 TeV. It is found that, among the observables, the invariant ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15920","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.15920/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}