Speaker
Attilio Andreazza
(Università degli Studi e INFN Milano (IT))
Description
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 given by individual pixel sizes is significantly improved 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, clusters have an increased probability of merging the charge
deposited by multiple particles. Recently, a neural network based algorithm which
estimates both the cluster position and whether a cluster should be split has been
developed for the ATLAS Pixel Detector. The algorithm significantly reduces
ambiguities in the assignment of pixel detector measurement to tracks within jets and
improves the position accuracy with respect to standard interpolation techniques by
taking into account the 2-dimensional charge distribution. The implementation of the
neural network, the training parameters and performance of the new clustering will be
presented. Significant improvements of the track and vertex resolution obtained using
this new method will be presented using Monte Carlo simulated data and compared to
data recorded with the ATLAS detector will be given. The resulting improvements to
both track reconstruction and the identification of jets containing b-quarks will be
discussed.
Primary author
Attilio Andreazza
(Università degli Studi e INFN Milano (IT))
