Speaker
Description
The Compressed Baryonic Matter experiment (CBM) at FAIR is designed to explore the QCD phase diagram at high baryon densities with interaction rates up to 10 MHz using triggerless free-streaming data acquisition. The CBM Ring Imaging Cherenkov detector (RICH) contributes to the overall PID by identification of electrons from the lowest momenta up to 6-8 GeV/c, with a pion suppression factor of more than 100. The RICH reconstruction combines a local Cherenkov ring-finding with a ring-track matching of extrapolated tracks from the Silicon Tracking System (STS) by closest distance.
The existing conventional algorithm for standalone ring-finding based on the Hough transform was revised and optimized. A method based on a Convolutional Neural Network (CNN) architecture was developed for noise suppression while taking into account the latency and data format (space and time, i.e. 3+1) constraints of the triggerless free-streaming readout. The method was tested and validated on simulations taking into account the time data stream and on data from the prototype mini-RICH (mRICH) in the mini-CBM (mCBM) experiment, which shares the same free-streaming readout concept as the future CBM experiment.
An alternative standalone ring-finder based on a Graph Neural Network (GNN) is investigated for its viability for the CBM RICH. It is designed as an end-to-end pipeline for ring-finding, optionally including noise classification as an auxiliary downstream task.
