Higgs self-coupling measurements using deep learning in the $$ b\overline{b}b\overline{b} $$ final state
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
Abstract
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 < $$ {\lambda}_{hhh}/{\lambda}_{hhh}^{\mathrm{SM}} $$ <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.