Szczegóły publikacji
Opis bibliograficzny
Message passing Graph Neural Network for seeds classification / Piotr MOSZKOWICZ, Piotr A. KOWALSKI, Tomasz BOŁD // W: Computational intelligence and mathematics for tackling complex problems 6 / eds. László T. Kóczy, Jesús Medina, Piotr A. Kowalski, Eloísa Ramírez-Poussa. — Cham : Springer Nature Switzerland, cop. 2026. — ( Studies in Computational Intelligence ; ISSN 1860-949X ; SCI vol. 1222 ). — Publikacja zawiera materiały z konferencji: 15th European Symposium on Computational Intelligence and Mathematics : 12–15 May 2024, Krakow. — ISBN: 978-3-031-97878-4; e-ISBN: 978-3-031-97879-1. — S. 69–77. — Bibliogr., Abstr. — P. A. Kowalski - dod. afiliacja: Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166188 |
|---|---|
| Data dodania do BaDAP | 2026-03-12 |
| DOI | 10.1007/978-3-031-97879-1_8 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Studies in Computational Intelligence |
Abstract
Within the field of High Energy Physics various machine learning techniques are being constantly researched. Due to upcoming HL-LHC (High Luminosity Large Hadron Collider) upgrade, which aims to increase probability of occurrence of rare physical phenomenas by significant increase of number of collisions occurring each second, currently used algorithms in the area of charged particle reconstructions are not going to scale well enough. The data from tracking detectors if the LHC experiments can be represented in the form of a graph, where detected signals are represented as nodes and hypothetical connections between them are represented by edges. In particular the initial stage of track finding, the formation of seeds can be represented in that way. Within this paper we propose solution based on Graph Neural Network used to dramatically decrease number of seeds and therefore significantly reduce amount of calculations needed by downstream algorithms in order to reconstruct tracks. The numerical experiments were carried out with use a data set obtained from Monte Carlo simulation of the Open Data Detector. The initial results showed, that proposed solution based on Graph Neural Network has application potential.
