Higgs self-coupling measurements using deep learning in the b b ˉ b b ˉ documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} egin{document}$$ boverline{b}boverline{b} $$end{document} final state

摘要

A bstract Measuring the Higgs trilinear self-coupling λ _( hhh )is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in the four b -quark channel ( hh → 4 b ), extending current methods in several directions. We perform deep learning to suppress the formidable multijet background with dedicated optimisation for BSM λ _( hhh )scenarios. We compare the λ _( hhh )constraining power of events using different multiplicities of large radius jets with a two-prong structure that reconstruct boosted h → bb decays. We show that current uncertainties in the SM top Yukawa coupling y _( t )can modify λ _( hhh )constraints by ～ 20%. For SM y _( t ), we find prospects of ? 0 . 8 < λ hhh / λ hhh SM documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} egin{document}$$ {lambda}_{hhh}/{lambda}_{hhh}^{mathrm{SM}} $$end{document} < 6 . 6 at 68% CL under simplified assumptions for 3000 fb~( ? 1)of HL-LHC data. Our results provide a careful assessment of di-Higgs identification and machine learning techniques for all-hadronic measurements of the Higgs self-coupling and sharpens the requirements for future improvement.