Abstract
A novel technique using a set of artificial neural networks to identify and split merged measurements created by multiple charged particles in the ATLAS pixel detector is presented. Such merged measurements are a common feature of boosted physics objects such as tau leptons or strongly energetic jets where particles are highly collimated. The neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. The performance of the splitting technique is quantified using LHC data collected by the ATLAS detector and Monte Carlo simulation. The number of shared hits per track is significantly reduced, particularly in boosted systems, which increases the reconstruction efficiency and quality. The improved position and error estimates of the measurements lead to a sizable improvement of the track and vertex resolution.
Highlights
The ATLAS Pixel Detector The silicon pixel detector [2] is the innermost layer of the tracking detector and provides precision measurements of the positions of charged particles
It is crucial for the reconstruction of primary vertices which give an estimate of the number of proton-proton interactions per bunch crossing, and for the identification of long-lived particles via the reconstruction of secondary vertices
The standard clustering provides excellent resolution for most clusters, it is inadequate for dense environments with multiple charged particles where charge is deposited in neighbouring pixels and clusters are shared between particles, as is illustrated in figure 1 (b)
Summary
2. The ATLAS Pixel Detector The silicon pixel detector [2] is the innermost layer of the tracking detector and provides precision measurements of the positions of charged particles. 3. Pixel Clustering Particles traversing the detector typically deposit charge in more than one pixel. The position of the particle at the point where it crosses the detector layer is computed from the signal heights inside the cluster of pixels: 1 x cs = x centre + ∆ x · Ω x − 2
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
