2020/04/30 by Jacob Amacker, William Balunas, Lydia Beresford +9
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Current (fluid) #Deep learning #Higgs boson #Identification (biology) #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #Standard Model (mathematical formulation) #State (computer science) #Yukawa potential #hep-ex #hep-ph
paper · pdf · doi:10.1007/jhep12(2020)115
published as JHEP 12 (2020) 115 · 36 pages, 15 figures + bibliography and appendices
openalex created_date 2020/08/03 · arxiv created 2020/10/12 · openalex publication_date 2020/12/01 · arxiv updated 2020/12/24 · openalex updated_date 2026/08/05
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 < λhhh/λhhhSM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>λ</mml:mi><mml:mi>hhh</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msubsup><mml:mi>λ</mml:mi><mml:mi>hhh</mml:mi><mml:mi>SM</mml:mi></mml:msubsup></mml:math> < 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.