Higgs self-coupling measurements using deep learning in the $$ b\overline{b}b\overline{b} $$ final state

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 <$$ {\lambda}_{hhh}/{\lambda}_{hhh}^{\mathrm{SM}} $$ λ hhh / λ hhh 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.

Precision SMEFT bounds from the VBF Higgs at high transverse momentum

“…In recent years, the particle physics community has been increasingly adapting to the use of deep neural networks (DNN) in challenging signal characterisation problems [77][78][79][80][81][82][83][84][85][86][87]. The Keras library [88] offers a python-based, flexible framework using feed-forward networks [89][90][91] to create mashed layers with connected neurons (nodes).…”

Higgs self-coupling measurements in the HL-LHC era: new approaches for the $hh \to b\bar{b}b\bar{b}$ final state.

“…Extending ℎℎ → ¯ ¯ searches into the HL-LHC era generated with varied coupling of the Higgs to the top quark . See [1] for more details on the simulated samples.…”

“…Extending ℎℎ → ¯ ¯ searches into the HL-LHC era as missing transverse energy (see [1] for details). A signal region is defined for each channel in the ℎℎ variable, which are the final selection for the baseline analysis.…”

A universal neural network for learning phases

“…Techniques from machine learning (ML), which originally belong to the fields of information and computer sciences, have found applications in many-body systems during the last a few years. Such examples contain studies associated with condensed matter physics including the critical phenomena of certain models , the high energy particle physics and astrophysics covering the analysis of the data relevant to jets and gravitational wave [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57], and the first principles material calculations like finding the density functionals . In some cases, the performance of these new approaches for exploring many-body physical systems is comparable with that of the traditional methods.…”