Speaker
Description
The Time Of Propagation (TOP) detector at the Belle II experiment is a ring-imaging Cherenkov detector designed to identify charged hadrons in electron-positron collisions at the SuperKEKB accelerator. It consists of 16 quartz radiator modules arranged around the barrel region of the Belle II detector. When a charged particle crosses a module, Cherenkov photons are emitted. A fraction of these photons is trapped by total internal reflection and propagates through the quartz to an array of micro-channel-plate photomultiplier tubes (MCP-PMTs), which measure the arrival time and hit position of the photons. Since the Cherenkov angle depends on the particle species for a given momentum and direction, different particles produce distinct position-time photon patterns. Particle identification (PID) in the TOP detector therefore reduces to a pattern recognition problem.
Currently, PID in the TOP relies on comparing event-by-event photon patterns with analytical probability density functions (PDFs) computed for different particle hypotheses and track parameters. So far, TOP PID performance has been consistent with its design expectations. However, further improvements are limited by the need for highly accurate modeling of the detector geometry and optical properties.
Machine Learning (ML) techniques offer a data-driven approach for PID in the TOP by learning the relationship between photon patterns and particle identity directly from data. In particular, Convolutional Neural Networks (CNNs), which are specifically designed for pattern recognition tasks, are well suited to analyze the two-dimensional position-time photon patterns recorded by the TOP detector. This approach has the potential to improve PID performance while reducing sensitivity to residual detector mismodeling.
A feasibility study of a CNN-based approach has been conducted using simulated samples of charged pions and kaons divided into phase-space bins according to the track momentum and direction. The results, validated on both simulated and experimental data, show an improvement in the kaon/pion separation performance of the TOP detector with respect to the standard PDF-based likelihood method. However, the performance gain is achieved at the cost of a considerably more complex PID approach that requires significantly greater computational resources.