Szczegóły publikacji
Opis bibliograficzny
Detection (reconstruction) of long-lived particle tracks in LHCb experiment, CERN / Piotr KULCZYCKI, Grzegorz GOŁASZEWSKI, Tomasz SZUMLAK, Szymon ŁUKASIK // W: IEEE 12th international conference on Data Science and Advanced Analytics (DSAA) [Dokument elektroniczny] : 09-12 October 2025, Birmingham, United Kingdom. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, [2025]. — ( Proceedings of the ... International Conference on Data Science and Advanced Analytics ; ISSN 2766-4112 ). — Dod. ISBN: 979-8-3315-1180-7 (print on demand). — e-ISBN: 979-8-3315-1179-1. — S. [1-10]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [10], Abstr. — Publikacja dostępna online od: 2025-11-24. — P. Kulczycki, Sz. Łukasik - dod. afiliacja: Polish Academy of Sciences, Systems Reserch Institute
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165843 |
|---|---|
| Data dodania do BaDAP | 2026-03-06 |
| Tekst źródłowy | URL |
| DOI | 10.1109/DSAA65442.2025.11247990 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Data Science and Advanced Analytics 2025 |
| Czasopismo/seria | Proceedings of the ... International Conference on Data Science and Advanced Analytics |
Abstract
The LHCb (Large Hadron Collider beauty) experiment is one of the main research areas carried out by the European Organization for Nuclear Research CERN in Geneva. The presented paper describes a novel procedure for the detection of long-lived particles provided for the needs of the above experiment. A problem that is investigated mainly relies on the determination of tracks (trajectories) of such particles based on observations obtained from measurement modules placed far apart; thus, such kind of detection is alternatively named a reconstruction. The main intention of the algorithm presented here constitutes the elimination of erroneously created tracks, the so-called ghost-tracks. The presented concept is based on elements of mathematical statistics (nonparametric estimation) as well as computational intelligence (neural networks, fuzzy approach). In order to measure the obtained effectiveness, an appropriate performance index which does not require information if the given track is real or only a ghost (as it occurs in practice) is defined. Finally, the number of ghost-tracks is reduced threefold in comparison to prior methods used. Illustrative interpretations make the presented concept easy to adopt in similar problems connected with detection/reconstruction of tracks/trajectories of elementary physical particles.
