10.1088-1748-0221-9-09-P09009.pdf (1.4 MB)
A neural network clustering algorithm for the ATLAS silicon pixel detector
journal contribution
posted on 2023-06-09, 06:13 authored by Benedict AllbrookeBenedict Allbrooke, Lily AsquithLily Asquith, Alessandro CerriAlessandro Cerri, C A Chavez Barajas, Antonella De SantoAntonella De Santo, Fabrizio SalvatoreFabrizio Salvatore, I Santoyo Castillo, K Suruliz, Mark SuttonMark Sutton, Iacopo Vivarelli, The ATLAS CollaborationA 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.
Funding
ATLAS; G0275; STFC-SCIENCE AND TECHNOLOGY FACILITIES COUNCIL; ST/I006048/1
History
Publication status
- Published
File Version
- Published version
Journal
Journal of InstrumentationISSN
1748-0221Publisher
Institute of PhysicsExternal DOI
Volume
9Page range
P09009Department affiliated with
- Physics and Astronomy Publications
Research groups affiliated with
- Experimental Particle Physics Research Group Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-05-09First Open Access (FOA) Date
2017-05-09First Compliant Deposit (FCD) Date
2017-05-09Usage metrics
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