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May 8 – 12, 2023
Norfolk Waterside Marriott
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A method for inferring signal strength modifiers by conditional invertible neural networks

May 9, 2023, 5:45 PM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Farkas, Máté Zoltán (CMS)


The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the mapping from signal strength modifiers to observables and its inverse. The resulting network infers the posterior distribution of the signal strength modifiers rapidly and for low computational cost. We present performance indicators of such a setup including the treatment of systematic uncertainties. Additionally, we highlight the features of cINNs estimating the signal strength for a vector boson associated Higgs production analysis carried out at the CMS experiment on Monte Carlo samples.

Consider for long presentation No

Primary authors

Farkas, Máté Zoltán (CMS) Diekmann, Svenja (RWTH Aachen University) Eich, Niclas (RWTH Aachen University) Prof. Erdmann, Martin (RWTH Aachen University)

Presentation materials