| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09001 | |
| Number of page(s) | 7 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509001 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429509001
A method for inferring signal strength modifiers by conditional invertible neural networks
RWTH Aachen University, III. Physikalisches Institut A, Otto-Blumenthal-Straße, 52074 Aachen, Germany
* e-mail: mate.zoltan.farkas@cern.ch
** e-mail: svenja.diekmann@cern.ch
*** e-mail: niclas.steve.eich@cern.ch
**** e-mail: martin.erdmann@cern.ch
Published online: 6 May 2024
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 of simulated samples of events, which include a simulation of the CMS detector.
© The Authors, published by EDP Sciences, 2024
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