Journal Article

PUBDB-2021-00149

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Higgs self-coupling measurements using deep learning in the $ b\overline{b}b\overline{b} $ final state

Amacker, J. ; Balunas, W. ; Beresford, L. (Corresponding author) ; Bortoletto, D. ; Frost, J. ; Issever, C.DESY* ; Liu, J. ; McKee, J. ; Micheli, A. ; Paredes Saenz, S. ; Spannowsky, M. ; Stanislaus, B.

2020
SISSA [Trieste]

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Please use a persistent id in citations: doi:10.1007/JHEP12(2020)115  doi:10.3204/PUBDB-2021-00149

Report No.: IPPP/20/11; arXiv:2004.04240

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 <$ {\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.

Keyword(s): CERN LHC Coll: upgrade ; potential: Higgs ; multiplicity: difference ; jet: multiple production ; coupling: Yukawa ; structure ; background ; Higgs Physics ; Beyond Standard Model

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Contributing Institute(s):

1. Experimente (ZEU-EXP)

Research Program(s):

1. 631 - Accelerator R & D (POF3-631) (POF3-631)

Experiment(s):

1. LHC: ATLAS

Appears in the scientific report 2020

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Database coverage:
; ; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz ; SCOAP3 sponsored Journal ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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