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Higgs self-coupling measurements using deep learning in the bb¯¯bb¯¯ final state

Amacker, Jacob and Balunas, William and Beresford, Lydia and Bortoletto, Daniela and Frost, James and Issever, Cigdem and Liu, Jesse and McKee, James and Micheli, Alessandro and Saenz, Santiago Paredes and Spannowsky, Michael and Stanislaus, Beojan (2020) 'Higgs self-coupling measurements using deep learning in the bb¯¯bb¯¯ final state.', Journal of high energy physics., 2020 (12). p. 115.

Abstract

Measuring the Higgs trilinear self-coupling λhhh 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 λhhh scenarios. We compare the λhhh 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 yt can modify λhhh constraints by ∼ 20%. For SM yt, we find prospects of −0.8 < λhhh/λSMhhh < 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.

Item Type:Article
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Available under License - Creative Commons Attribution 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/JHEP12(2020)115
Publisher statement:This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
Date accepted:03 November 2020
Date deposited:13 April 2021
Date of first online publication:18 December 2020
Date first made open access:13 April 2021

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