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A neural network clustering algorithm for the ATLAS silicon pixel detector
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Stockholms universitet, Naturvetenskapliga fakulteten, Oskar Klein-centrum för kosmopartikelfysik (OKC).
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Stockholms universitet, Naturvetenskapliga fakulteten, Oskar Klein-centrum för kosmopartikelfysik (OKC).
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Stockholms universitet, Naturvetenskapliga fakulteten, Oskar Klein-centrum för kosmopartikelfysik (OKC).
Stockholms universitet, Naturvetenskapliga fakulteten, Fysikum. Stockholms universitet, Naturvetenskapliga fakulteten, Oskar Klein-centrum för kosmopartikelfysik (OKC).
Vise andre og tillknytning
2014 (engelsk)Inngår i: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 9, s. P09009-Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

sted, utgiver, år, opplag, sider
2014. Vol. 9, s. P09009-
Emneord [en]
Particle tracking detectors, Particle tracking detectors (Solid-state detectors)
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-109286DOI: 10.1088/1748-0221/9/09/P09009ISI: 000343281300046OAI: oai:DiVA.org:su-109286DiVA, id: diva2:764473
Merknad

AuthorCount:2876;

Tilgjengelig fra: 2014-11-19 Laget: 2014-11-17 Sist oppdatert: 2017-12-05bibliografisk kontrollert

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Abulaiti, YimingÅkerstedt, HenrikÅsman, BarbroBendtz, KatarinaBertoli, GabrieleBessidskaia, OlgaBohm, ChristianClément, ChristopheCribbs, Wayne A.Eriksson, DanielGellerstedt, KarlHellman, StenJohansson, K. ErikJon-And, KerstinKhandanyan, HovhannesKim, HyeonKlimek, PawelLundberg, OlofMilstead, David A.Moa, TorbjörnMolander, SimonOhm, Christian C.Petridis, AndreasPlucinski, PawelRossetti, ValerioSilverstein, Samuel B.Sjölin, JörgenStrandberg, SaraTylmad, Maja
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