Recent advances and trends in pattern recognition and data analysis for RICH detectors

TL;DR

This paper reviews recent progress in pattern recognition and data analysis techniques for RICH detectors, emphasizing traditional and machine learning methods, and discusses emerging trends like global identification and generative models.

Contribution

It provides a comprehensive overview of advances in RICH reconstruction algorithms, including traditional and modern machine learning approaches, and discusses future directions.

Findings

Traditional likelihood and Hough-transform methods have been improved.

Machine learning approaches are increasingly used for RICH data analysis.

Emerging trends include global particle identification and generative models for simulation.

Abstract

Ring Imaging Cherenkov (RICH) detectors are a key component of particle identification systems in many particle, nuclear and astroparticle physics experiments. Their ultimate performance depends not only on detector design and hardware implementation, but also crucially on the quality of pattern recognition and data analysis algorithms used to reconstruct Cherenkov ring images and to perform particle identification. In recent years, significant advances have been made both in traditional reconstruction approaches, such as likelihood-based methods and Hough-transform techniques, and in the application of modern machine learning tools. This contribution reviews the current state of RICH reconstruction algorithms, highlights representative use cases from operating experiments, and discusses emerging trends including global particle identification strategies and generative machine learning approaches for fast simulation and reconstruction.