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

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

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

在 arXiv stat.ML 阅读 →

• Higgs trilinear self-coupling
• Large Hadron Collider
• neural simulation-based inference (NSBI)
• Standard Model effective field theory (SMEFT)
• Ulrich Haisch

• 论文
• 其他

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