Exploring SMEFT in VH with Machine Learning

Abstract

In this paper we study the use of Machine Learning techniques to exploit kinematic information in VH, the production of a Higgs in association with a massive vector boson. We parametrise the effect of new physics in terms of the SMEFT framework. We find that the use of a shallow neural network allows us to dramatically increase the sensitivity to deviations in VH respect to previous estimates. We also discuss the relation between the usual measures of performance in Machine Learning, such as AUC or accuracy, with the more adept measure of Asimov significance. This relation is particularly relevant when parametrising systematic uncertainties. Our results show the potential of incorporating Machine Learning techniques to the SMEFT studies using the current datasets.

arXiv comment7 pages, 5 figures

References in corpus (29)

• Asymptotic formulae for likelihood-based tests of new physics
• High-Energy-Physics Event Generation with PYTHIA 6.1
• FeynRules - Feynman rules made easy
• A high-bias, low-variance introduction to Machine Learning for physicists
• The Standard Model as an Effective Field Theory
• Observation of Higgs boson decay to bottom quarks
• Observation of $H\to b\bar{b}$ decays and $VH$ production with the ATLAS detector
• Deep-learning Top Taggers or The End of QCD?
• Updated Global SMEFT Fit to Higgs, Diboson and Electroweak Data
• Complete Higgs Sector Constraints on Dimension-6 Operators
• Constraining Effective Field Theories with Machine Learning
• A Guide to Constraining Effective Field Theories with Machine Learning
• The Gauge-Higgs Legacy of the LHC Run II
• On the impact of dimension-eight SMEFT operators on Higgs measurements
• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
• Phenomenology of the Higgs Effective Lagrangian via FeynRules
• Limits on Anomalous Couplings of the Higgs to Electroweak Gauge Bosons from LEP and LHC
• Electroweak Sector Under Scrutiny: A Combined Analysis of LHC and Electroweak Precision Data
• A Fast Track towards the `Higgs' Spin and Parity
• Better Higgs-CP Tests Through Information Geometry
• Jet Charge and Machine Learning
• Electroweak Higgs boson production in the standard model effective field theory beyond leading order in QCD
• Probing Electroweak Precision Physics via boosted Higgs-strahlung at the LHC
• Higher Order QCD predictions for Associated Higgs production with anomalous couplings to gauge bosons
• Associated Production Evidence against Higgs Impostors and Anomalous Couplings
• Probing CP-violating Higgs and gauge boson couplings in the Standard Model effective field theory
• Jet substructure and probes of CP violation in Vh production
• On new physics searches with multidimensional differential shapes
• Constraints on models for the Higgs boson with exotic spin and parity in $\mathbf{VH}\to \mathbf{Vb}\bar{\mathbf{b}}$ final states

Cited by in corpus (26)

• Towards the ultimate differential SMEFT analysis
• Benchmarking simplified template cross sections in $WH$ production
• Exploring anomalous couplings in Higgs boson pair production through shape analysis
• Resolving the tensor structure of the Higgs coupling to $Z$-bosons via Higgs-strahlung
• Higgs boson tagging with the Lund jet plane
• ZH production in gluon fusion at NLO in QCD
• Precision SMEFT bounds from the VBF Higgs at high transverse momentum
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• A fully differential SMEFT analysis of the golden channel using the method of moments
• Using Machine Learning to disentangle LHC signatures of Dark Matter candidates
• Back to the Formula -- LHC Edition
• Anomaly Awareness
• Probing effective field theory operators in the associated production of top quarks with a Z boson in multilepton final states at $\sqrt{s}=$ 13 TeV
• Precision from the diphoton Zh channel at FCC-hh
• Learning Multivariate New Physics
• A New Precision Process at FCC-hh: the diphoton leptonic Wh channel
• Deep learnig analysis of the inverse seesaw in a 3-3-1 model at the LHC
• Electroweak Restoration at the LHC and Beyond: The $Vh$ Channel
• Sensitivity reach on anomalous Higgs couplings via triphoton production for the post-LHC circular high-energy hadron colliders
• Distinguishing $W^{\prime }$ Signals at Hadron Colliders Using Neural Networks
• Role of polarizations and spin-spin correlations of $W^{\prime }s$ in $e^{-}{e}^{+}\to W^{-}{W}^{+}$ at $\sqrt{s}=250$ GeV to probe anomalous $W^{-}{W}^{+}Z/\gamma$ couplings
• High energy lepton colliders as the ultimate Higgs microscopes
• Prospective constraints on anomalous Higgs boson interactions in an effective Lagrangian via diphoton production at FCC-hh
• Higgs couplings in SMEFT via Zh production at the HL-LHC
• Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences
• Signs of new physics in top quark pair production associated with a neutrino pair at the LHC