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The hit signals read out from pixels on planar semi-conductor sensors are grouped into clusters, to reconstruct the location where a charged particle passed through. The spatial resolution of the pixel detector can be improved significantly using the information from the cluster of adjacent pixels. Such analogue cluster creation techniques have been used by the ATLAS experiment for many years giving an excellent performance. However, in dense environments, such as those inside high-energy jets, it is likely that the charge deposited by two or more close-by tracks merges into one single cluster. A clusterization algorithm based on neural network methods has been developed for the ATLAS Pixel Detector. This can identify the shared clusters, split them if necessary, and estimate the positions of all particles traversing the cluster. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurements to tracks within jets, and improves the positional accuracy with respect to standard interpolation techniques, by the use of the 2-dimensional charge distribution information. The reconstruction using the neural network reduces strongly the number of hits shared by more than one track and improves the resolution of the impact parameter by about 15%.

Neural network based cluster reconstruction in the ATLAS silicon Pixel Detector