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

Amacker, Jacob; Balunas, William; Beresford, Lydia; Bortoletto, Daniela; Frost, James; Issever, Cigdem; Liu, Jesse; McKee, James; Micheli, Alessandro; Saenz, Santiago Paredes; Spannowsky, Michael; Stanislaus, Beojan

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.

Citation

Amacker, J., Balunas, W., Beresford, L., Bortoletto, D., Frost, J., Issever, C., Liu, J., McKee, J., Micheli, A., Saenz, S. P., Spannowsky, M., & Stanislaus, B. (2020). Higgs self-coupling measurements using deep learning in the bb¯¯bb¯¯ final state. Journal of High Energy Physics, 2020(12), Article 115. https://doi.org/10.1007/jhep12%282020%29115

• Journal Article Type: Article
• Acceptance Date: Nov 3, 2020
• Online Publication Date: Dec 18, 2020
• Publication Date: 2020-12
• Deposit Date: Apr 13, 2021
• Publicly Available Date: Apr 13, 2021
• Journal: Journal of High Energy Physics
• Print ISSN: 1126-6708
• Publisher: Scuola Internazionale Superiore di Studi Avanzati (SISSA)
• Peer Reviewed: Peer Reviewed
• Volume: 2020
• Issue: 12
• Article Number: 115
• DOI: https://doi.org/10.1007/jhep12%282020%29115
• Public URL: https://durham-repository.worktribe.com/output/1277593
• Related Public URLs: https://arxiv.org/abs/2004.04240