{"paper":{"title":"Higgs Physics with the XFEL Compton $\\boldsymbol{\\gamma\\gamma}$ Collider Concept at $\\boldsymbol{\\sqrt{s}=125}$ GeV","license":"http://creativecommons.org/licenses/by/4.0/","headline":"An XFEL-based γγ collider at 125 GeV can measure Higgs properties with high precision by applying set transformer machine learning to particle data.","cross_cats":["hep-ex"],"primary_cat":"hep-ph","authors_text":"Ariel Schwartzman, Tim Barklow, Umar Sohail Qureshi","submitted_at":"2026-05-16T02:11:31Z","abstract_excerpt":"We investigate single Higgs production in $\\sqrt{s}=125$ GeV $\\gamma\\gamma$ collisions at the X-Ray Free Electron (XFEL) Compton Collider (XCC) concept and present an analysis targeting the major hadronic, semi-leptonic, and fully leptonic final states of the Higgs boson, including $H\\to s\\overline{s}$. In addition to studying Higgs production at a novel collider concept, our approach couples a novel set transformer-based deep learning framework that acts on particle-flow object point clouds with a genetic algorithm optimizer for signal-background discrimination, yielding significantly higher "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that an XFEL γγ collider can probe the Higgs sector with extremely high precision and enable new physics opportunities, complementary to proposed e+e- machines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The set transformer-based deep learning framework that acts on particle-flow object point clouds, when coupled with a genetic algorithm optimizer, yields significantly higher sensitivity than traditional methods for signal-background discrimination in the Higgs final states.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An XFEL Compton gamma-gamma collider at 125 GeV with a set transformer deep learning classifier on particle-flow point clouds can achieve high-precision Higgs measurements across hadronic, semi-leptonic, and leptonic final states including H to strange quarks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An XFEL-based γγ collider at 125 GeV can measure Higgs properties with high precision by applying set transformer machine learning to particle data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3e4d11f87f33d2eda67ca3754f169a01dc978bd1504b77e3541ffddcceb484b5"},"source":{"id":"2605.16756","kind":"arxiv","version":1},"verdict":{"id":"2accf2dc-5afd-4ee6-927c-c5d6f54a773c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:28:50.710036Z","strongest_claim":"Our results demonstrate that an XFEL γγ collider can probe the Higgs sector with extremely high precision and enable new physics opportunities, complementary to proposed e+e- machines.","one_line_summary":"An XFEL Compton gamma-gamma collider at 125 GeV with a set transformer deep learning classifier on particle-flow point clouds can achieve high-precision Higgs measurements across hadronic, semi-leptonic, and leptonic final states including H to strange quarks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The set transformer-based deep learning framework that acts on particle-flow object point clouds, when coupled with a genetic algorithm optimizer, yields significantly higher sensitivity than traditional methods for signal-background discrimination in the Higgs final states.","pith_extraction_headline":"An XFEL-based γγ collider at 125 GeV can measure Higgs properties with high precision by applying set transformer machine learning to particle data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16756/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.791165Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:40:54.647674Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.322654Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.454068Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"04c9edde4be2427653ba2ebcedfe9d071e6305a1035054864d48ca76b5ec1483"},"references":{"count":65,"sample":[{"doi":"","year":1964,"title":"F. Englert and R. Brout,Broken symmetry and the mass of gauge vector mesons,Phys. Rev. Lett.13(1964) 321","work_id":"2d51c46d-46ab-442e-a2f3-d94b458aeb7d","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1964,"title":"G.S. Guralnik, C.R. Hagen and T.W.B. Kibble,Global conservation laws and massless particles,Phys. Rev. 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