Abstract
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution can be significantly improved over that given by the individual pixel sizes by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, there is an increased probability of merging the charge deposited by multiple particles into a single cluster. A neural network based algorithm has been developed for the ATLAS Pixel Detector, in order to identify clusters due to multiple particles and to estimate their position. The algorithm significantly reduces ambiguities in the assignment of Pixel Detector measurements to tracks and improves the position accuracy and two-particle separation with respect to standard techniques by taking into account the 2-dimensional charge distribution.
Highlights
Silicon pixel detectors are used in high energy physics experiments to measure with high resolution the position of passing charged particles and reconstruct their trajectories
The ATLAS Pixel Detector [1, 2] consists of a barrel part with three layers, at radii of 50.5 mm, 88.5 mm and 122.5 mm, and of two end caps, with three disks each
A novel technique based on neural networks has been implemented to improve the position resolution and two particle separation of the detector
Summary
Silicon pixel detectors are used in high energy physics experiments to measure with high resolution the position of passing charged particles and reconstruct their trajectories. A novel technique based on neural networks has been implemented to improve the position resolution and two particle separation of the detector. It has been used for the reprocessing of 2011 data and it is the default algorithm in track reconstruction.
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