Szczegóły publikacji
Opis bibliograficzny
Toward a generative modeling analysis of CLAS exclusive $2\pi$ photoproduction / T. Alghamdi, Y. Alanazi, M. Battaglieri, Ł. BIBRZYCKI, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Montaña, A. Pilloni, N. Sato, A. P. Szczepaniak, T. Vittorini // Physical Review. D ; ISSN 2470-0010. — Tytuł poprz.: Physical Review D. Particles, fields, gravitation, and cosmology ; ISSN: 1550-7998. — 2023 — vol. 108 iss. 9 art. no. 094030, s. 094030-1–094030-14. — Bibliogr. s. 094030-13–094030-14, Abstr. — Publikacja dostępna online od: 2023-11-21
Autorzy (16)
- Alghamdi Tareq
- Alanazi Yasir
- Battaglieri Marco
- AGHBibrzycki Łukasz
- Golda Andrey V.
- Hiller Blin A. N.
- Isupov Evgeny L.
- Li Yaohang
- Marsicano Luca
- Melnitchouk Wally
- Mokeev Victor I.
- Montaña Glòria
- Pilloni Alessandro
- Sato Nobuo
- Szczepaniak Adam P.
- Vittorini Tommaso
Dane bibliometryczne
| ID BaDAP | 150362 |
|---|---|
| Data dodania do BaDAP | 2023-12-20 |
| Tekst źródłowy | URL |
| DOI | 10.1103/PhysRevD.108.094030 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Physical Review, D |
Abstract
AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. This work employs a generative modeling approach to unfold detector effects specifically tailored for exclusive reactions that involve multiparticle final states. Our study demonstrates the preservation of correlations between kinematic variables in a multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector’s nontrivial effects represents an ideal test case for AI-supported analysis. Uncertainty quantification performed via bootstrap provides an estimate of the systematic uncertainty associated with the procedure. The test demonstrates that GANs can reproduce highly correlated multidifferential cross sections even in the presence of detector-induced distortions in the training datasets, and provides a solid basis for applying the framework to real experimental data.
