CERN Accelerating science

ATLAS Slides
Report number	ATL-PHYS-SLIDE-2016-356
Title	Robustness of the ATLAS pixel clustering neural network algorithm
Author(s)	Sidebo, Per Edvin (Royal Institute of Technology (KTH))
Corporate author(s)	The ATLAS collaboration
Collaboration	ATLAS Collaboration
Submitted to	4th Conference on Large Hadron Collider Physics 2016 (LHCP 2016), Lund, Sweden, 13 - 18 Jun 2016
Submitted by	edvin.sidebo@cern.ch on 21 Jun 2016
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	TRACKING
Abstract	Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The method recovers otherwise lost tracks in dense environments where particles are separated by distances comparable to the size of the detector read-out elements. Such environments are highly relevant for LHC run 2, e.g. in searches for heavy resonances. Within the scope of run 2 track reconstruction performance and upgrades, the robustness of the neural network algorithm will be presented. The robustness has been studied by evaluating the stability of the algorithm’s performance under a range of variations in the pixel detector conditions.