Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models

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.

Solving Witness-type Triangle Puzzles Faster with an Automatically Learned Human-Explainable Predicate

Automatically solving puzzle instances in the game The Witness can guide players toward solutions and help puzzle designers generate better puzzles. In the latter case such an Artificial Intelligence puzzle solver can inform a human puzzle designer and procedural puzzle generator to produce better instances. The puzzles, however, are combinatorially difficult and search-based solvers can require large amounts of time and memory. We accelerate such search by automatically learning a human-explainable predicate that predicts whether a partial path to a Witness-type puzzle is not completable to a solution path. We prove a key property of the learned predicate which allows us to use it for pruning successor states in search thereby accelerating search by an average of six times while maintaining completeness of the underlying search. Conversely given a fixed search time budget per puzzle our predicate-accelerated search can solve more puzzle instances of larger sizes than the baseline search.