Speaker
Description
Focusing Aerogel RICH (FARICH) Detector employs a non-uniform aerogel radiator to measure velocity of charged particles with high precision and identify them in the momentum range of a few GeV/c. PID system based on FARICH with SiPM readout is proposed for the SCTF detector which should provide pi/K separation in the entire momentum region of the experiment and mu/pi separation up to 1.5 GeV/c momentum.
SiPMs are known to have very high dark count rate of 100 kHz/mm^2 at room temperature that could spoil the performance of the FARICH detector.
Artificial Neural Networks (ANN) can be used in reconstruction algorithms in High Energy Physics where complex calculations are needed to measure a signal in presence of background.
We employ a fully connected neural network to reconstruct particle's velocity in a FARICH detector. For training and testing Geant4 simulated data are used. Velocity resolution of ANN was found to be comparable with a geometrical-based reconstruction algorithm in case of no background. ANN provides almost the same resolution for high momentum particles even in presence of substantial background from SiPM dark counts up to 1 MHz/mm^2.
