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001-es BibID:	BIBFORM116766
035-os BibID:	(cikkazonosító)052002 (Scopus)85175427508 (WoS)001091059400002
Első szerző:	CMS Collaboration
Cím:	Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector / CMS Collaboration
Dátum:	2023
ISSN:	2470-0010 2470-0029
Megjegyzések:	A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle Formula Presented into two photons, Formula Presented, is chosen as a benchmark decay. Lorentz boosts Formula Presented are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using Formula Presented decays in LHC collision data.
Tárgyszavak:	Természettudományok Fizikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Physical Review D. - 108 : 5 (2023), p. 1-34. -
További szerzők:	Tumasyan, Armen Adam, Wolfgang Andrejkovic, J.W. Raics Péter (1944-) (fizikus) Trócsányi Zoltán (1961-) (fizikus) Ujvári Balázs (1977-) (fizikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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