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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

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Jacob Amacker

William Balunas

Lydia Beresford

Daniela Bortoletto

James Frost

Cigdem Issever

Jesse Liu

James McKee

Alessandro Micheli

Santiago Paredes Saenz

Beojan Stanislaus


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.


Amacker, J., Balunas, W., Beresford, L., Bortoletto, D., Frost, J., Issever, C., …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.

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
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Copyright 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.

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