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New neural inference method targets Higgs self-coupling at LHC

Researchers have developed a novel neural simulation-based inference (NSBI) approach to determine the Higgs trilinear self-coupling. This method combines the efficiency of matrix-element-enhanced techniques with the practical benefits of classification-based methods for background estimation. The NSBI approach demonstrates sensitivity close to the theoretical optimum and is expected to provide constraints for the Large Hadron Collider's high-luminosity upgrade, also considering other Standard Model effective field theory operators. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel computational method that could enhance scientific discovery in high-energy physics.

RANK_REASON Academic paper detailing a new computational method for particle physics research. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

New neural inference method targets Higgs self-coupling at LHC

COVERAGE [1]

  1. arXiv stat.ML TIER_1 · Aishik Ghosh, Maximilian Griese, Ulrich Haisch, Tae Hyoun Park ·

    Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production

    arXiv:2507.02032v2 Announce Type: replace-cross Abstract: One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in prot…