Di Florio, A. (2019) Convolutional neural network for track seed filtering at the CMS HLT. Il nuovo cimento C, 42 (4). pp. 1-6. ISSN 1826-9885
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Abstract
Starting with Run III, future development projects for the Large Hadron Collider will constantly bring nominal luminosity increase, with the ultimate goal of reaching a peak luminosity of 5 · 1034 cm−2 s−1 for ATLAS and CMS experiments. This rise in luminosity will result in an increased number of simultaneous proton collisions (pileup), up to 200, that will pose new challenges for the CMS detector and, specifically, for track reconstruction in the Silicon Pixel Tracker. One of the first steps of the track finding workflow is the creation of track seeds, i.e., compatible pairs of hits, that are subsequently fed to higher-level pattern recognition steps. However, the set of compatible hit pairs is highly affected by combinatorial background. A possible way of reducing this effect is taking into account the shape of the hit pixel cluster to check the compatibility between two hits. To each doublet is attached a collection of two images built with the ADC levels of the pixels forming the hit cluster. Thus, the task of fake rejection can be seen as an image classification problem for which Convolutional Neural Networks (CNNs) have been widely proven to provide reliable results. In this work we present our studies on CNNs applications to the filtering of track pixel seeds.
| Item Type: | Article |
|---|---|
| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 21 Dec 2020 13:32 |
| Last Modified: | 21 Dec 2020 13:32 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/20600 |
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