Speaker
Description
The Super Tau Charm Facility (STCF) is a future electron-positron collider proposed with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity of 0.5$\times10^{35}$ ${\rm cm}^{-2}{\rm s}^{-1}$. In STCF, the identification of high-momentum hadrons is critical for various physics studies, therefore two Cherenkov detectors (RICH and DTOF) are designed to boost the PID performance.
In this work, targeting the pion/kaon identification at STCF, we developed a PID algorithm based on the convolutional neural network (CNN) for the DTOF detector, which combines the hit channel and arrival time of Cherenkov photons at multi-anode microchannel plate photomultipliers. The current performance meets the physics requirements of STCF, with a pion identification efficiency exceeding 97% along with a kaon misidentification rate of less than 2% at p = 2Gev/c. In addition, based on classical CNN, we conducted a proof-of-concept study on quantum convolutional neural networks (QCNN) to explore potential quantum advantages and feasibility. Preliminary results indicate that QCNN has a promising potential to outperform classical CNN on a same dataset.
