| Autor: |
Farkas Máté Zoltán, Diekmann Svenja, Eich Niclas, Erdmann Martin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 295, p 09001 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202429509001 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
