Neural network based cluster creation in the ATLAS pixel detector

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.