  Imaging Cherenkov detectors are crucial for particle identification (PID) in
nuclear and particle physics experiments. Fast reconstruction algorithms are
essential for near real-time alignment, calibration, data quality control, and
efficient analysis. At the future Electron-Ion Collider (EIC), the ePIC
detector will feature a dual Ring Imaging Cherenkov (dual-RICH) detector in the
hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the
barrel, and a proximity focus RICH in the electron direction. This paper
focuses on the DIRC detector, which presents complex hit patterns and is also
used for PID of pions and kaons in the GlueX experiment at JLab. We present
Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and
faster PID compared to traditional methods and, for the first time, fast and
accurate simulation. This advancement addresses a major bottleneck in Cherenkov
detector simulations involving photon tracking through complex optical
elements. Our results leverage advancements in Vision Transformers,
specifically hierarchical Swin Transformer and normalizing flows. These methods
enable direct learning from real data and the reconstruction of complex
topologies. We conclude by discussing the implications and future extensions of
this work, which can offer capabilities for PID for multiple cutting-edge
experiments like the future EIC.
