Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties

Abstract

We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approach incorporates known analytic dependencies on parameters of interest, including signal strengths and nuisance parameters. When these dependencies are unknown, as is frequently the case for systematic uncertainties, dedicated neural network parametrizations provide an approximation that is trained on simulated data. The resulting machine-learned surrogate captures the complete parameter dependence of the likelihood ratio, providing a near-optimal test statistic. As a case study, we perform a first-principles inclusive cross-section measurement of $\text{H}\to \tau \tau$ in the single-lepton channel, utilizing simulated data from the FAIR Universe Higgs Uncertainty Challenge. Results in Asimov data, from large-scale toy studies, and using the Fisher information demonstrate significant improvements over traditional binned methods. Our computer code ``Guaranteed Optimal Log-Likelihood-based Unbinned Method'' (GOLLUM) for machine-learning and inference is publicly available.

23 pages, 16 figures, 2 tables. Contribution to the FAIR Universe Higgs Uncertainty Challenge, winning first place ex aequo; v2: accepted for publication

References in corpus (29)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• An Introduction to PYTHIA 8.2
• Asymptotic formulae for likelihood-based tests of new physics
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Evidence for the 125 GeV Higgs boson decaying to a pair of tau leptons
• Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector
• Observation of the Higgs boson decay to a pair of tau leptons with the CMS detector
• Search for neutral Higgs bosons decaying to tau pairs in pp collisions at sqrt(s) = 7 TeV
• Mining gold from implicit models to improve likelihood-free inference
• Constraining Effective Field Theories with Machine Learning
• A Guide to Constraining Effective Field Theories with Machine Learning
• Cross-section measurements of the Higgs boson decaying into a pair of tau-leptons in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• The CMS statistical analysis and combination tool: COMBINE
• Search for the Standard Model Higgs boson in the H to tau+ tau- decay mode in sqrt(s) = 7 TeV pp collisions with ATLAS
• Machine learning the Higgs boson-top quark CP phase
• Measurements of Higgs boson production cross-sections in the $H\to {\tau }^{+}{\tau }^{-}$ decay channel in $pp$ collisions at $\sqrt{s}=13\text{TeV}$ with the ATLAS detector
• Benchmarking simplified template cross sections in $WH$ production
• Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
• Measurements of Higgs boson production in the decay channel with a pair of $\tau$ leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV
• Unbinned multivariate observables for global SMEFT analyses from machine learning
• Measurement of off-shell Higgs boson production in the $H^{\ast }\to ZZ\to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV $pp$ collisions with the ATLAS detector
• Measurement of the inclusive and differential Higgs boson production cross sections in the decay mode to a pair of $\tau$ leptons in pp collisions at $\sqrt{s}$ = 13 TeV
• An implementation of neural simulation-based inference for parameter estimation in ATLAS
• Simulation-based inference in the search for CP violation in leptonic WH production
• Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
• Differential cross-section measurements of Higgs boson production in the $H\to {\tau }^{+}{\tau }^{-}$ decay channel in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Refinable modeling for unbinned SMEFT analyses
• Measurement of the production cross section of a Higgs boson with large transverse momentum in its decays to a pair of $\tau$ leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Cited by in corpus (2)

• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• Data-Driven High-Dimensional Statistical Inference with Generative Models