First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors. (12th March 2021)

• Record Type: Journal Article
• Authors: Siviero, F.; Arcidiacono, R.; Cartiglia, N.; Costa, M.; Ferrero, M.; Legger, F.; Mandurrino, M.; Sola, V.; Staiano, A.; Tornago, M.
• Is Part Of: Journal of instrumentation. Volume 16:Number 3(2021)
• Publication Date: 2021-03-12
• Subjects: Particle tracking detectors (Solid-state detectors) -- Data processing methods -- Timing detectors; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/16/03/P03019
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: RSDs (Resistive AC-Coupled Silicon Detectors) are n-in-p silicon sensors based on the LGAD (Low-Gain Avalanche Diode) technology, featuring a continuous gain layer over the whole sensor area. The truly innovative feature of these sensors is that the signal induced by an ionising particle is seen on several pixels, allowing the use of reconstruction techniques that combine the information from many read-out channels. In this contribution, the first application of a machine learning technique to RSD devices is presented. The spatial resolution of this technique is compared to that obtained with the standard RSD reconstruction methods that use analytical descriptions of the signal sharing mechanism. A Multi-Output regressor algorithm, trained with a combination of simulated and real data, leads to a spatial resolution of less than 2 μm for a sensor with a 100 μm pixel. The prospects of future improvements are also discussed.