Temat: neural simulation - Prolib Integro

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Tytuł:: Measurement of off-shell Higgs boson production in the $H^{*}\to ZZ\to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector The ATLAS Collaboration
Autorzy:: Gil, Damian
Volkotrub, Yuriy
Przygoda, Witold
Richter-Wąs, Elżbieta
Współwytwórcy:: Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2873
Opis:: A measurement of off-shell Higgs boson production in the $H^{*}\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb$^{−1}$ of proton–proton collisions at $\sqrt{s}$ = 13 TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$ decay channel at 68% CL is $0.87^{+0.75}_{-0.54} (1.00^{+1.04}_{-0.95})$. The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$ decay channel has an observed (expected) significance of 2.5σ (1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell2\nu$ decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ (2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is $4.3^{+2.7}_{-1.9}(4.1^{+3.5}_{-3.4})$ MeV.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Tytuł:: An implementation of neural simulation-based inference for parameter estimation in ATLAS
Autorzy:: Przygoda, Witold
Richter-Wąs, Elżbieta
Volkotrub, Yuriy
Gil, Damian
Współwytwórcy:: Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2873
Opis:: Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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