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Robustness of the ATLAS pixel clustering neural network algorithm
KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
2016 (English)In: Proceedings of Science, Proceedings of Science (PoS) , 2016Conference paper (Refereed)
Abstract [en]

Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In particle-dense environments multiple charged particles can contribute to the same cluster of neighbouring read-out elements in the ATLAS pixel detector. The ATLAS track reconstruction algorithm utilises an artificial neural network to identify and split such clusters. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.

Place, publisher, year, edition, pages
Proceedings of Science (PoS) , 2016.
National Category
Astronomy, Astrophysics and Cosmology
Identifiers
URN: urn:nbn:se:kth:diva-213340Scopus ID: 2-s2.0-85025816438OAI: oai:DiVA.org:kth-213340DiVA, id: diva2:1137026
Conference
4th Annual Large Hadron Collider Physics, LHCP 2016, Lund, Sweden, 13 June 2016 through 18 June 2016
Note

QC 20170830

Available from: 2017-08-30 Created: 2017-08-30 Last updated: 2017-08-30Bibliographically approved

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