Higgs self-coupling measurements using deep learning in the b b ¯ b b ¯ $$ b\overline{b}b\overline{b} $$ final state

• Main Authors: Jacob Amacker, William Balunas, Lydia Beresford, Daniela Bortoletto, James Frost, Cigdem Issever, Jesse Liu, James McKee, Alessandro Micheli, Santiago Paredes Saenz, Michael Spannowsky, Beojan Stanislaus
• Format: Article
• Language: English
• Published: SpringerOpen 2020-12-01
• Series: Journal of High Energy Physics
• Subjects: Higgs Physics; Beyond Standard Model
• Online Access: https://doi.org/10.1007/JHEP12(2020)115
• ISSN: 1029-8479

Abstract 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 → 4b), 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 $$ {\lambda}_{hhh}/{\lambda}_{hhh}^{\mathrm{SM}} $$ < 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.