Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - An implementation of neural simulation-based inference for parameter estimation in ATLAS
AU - The ATLAS collaboration
AU - Ali, Hanadi
AU - Alsolami, Zainab
AU - Barton, A.E.
AU - Borissov, G.
AU - Bouhova-Thacker, E.V.
AU - Ferguson, Ruby
AU - Ferrando, James
AU - Fox, H.
AU - Hagan, Alina
AU - Henderson, R.C.W.
AU - Jones, R.W.L.
AU - Kartvelishvili, V.
AU - Love, P.A.
AU - Marshall, Emma J.
AU - McElhinney, Luke
AU - Meng, L.
AU - Muenstermann, D.
AU - Sampson, Elliot
AU - Smizanska, M.
AU - Wharton, A.M.
PY - 2025/5/27
Y1 - 2025/5/27
N2 - 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.
AB - 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.
U2 - 10.1088/1361-6633/add370
DO - 10.1088/1361-6633/add370
M3 - Journal article
VL - 88
JO - Reports on Progress in Physics
JF - Reports on Progress in Physics
SN - 0034-4885
IS - 6
M1 - 067801
ER -