DeepRICH: Learning Deeply Cherenkov Detectors

TL;DR

DeepRICH introduces a deep learning-based reconstruction algorithm for imaging Cherenkov detectors, significantly improving speed while maintaining performance, by combining generative models and CNNs for particle identification.

Contribution

The paper presents a novel deep learning architecture that bypasses traditional likelihood-based methods, enabling faster reconstruction in Cherenkov detectors with comparable accuracy.

Findings

DeepRICH achieves faster reconstruction times than traditional algorithms.

The model maintains competitive particle identification accuracy.

It demonstrates versatility across different Cherenkov detector types.

Abstract

Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data. In this paper we present DeepRICH, a novel deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors. The core of our architecture is a generative model which leverages on a custom Variational Auto-encoder (VAE) combined to Maximum Mean Discrepancy (MMD), with a Convolutional Neural Network (CNN) extracting features from the space of the latent variables for classification. A thorough comparison with the simulation/reconstruction package FastDIRC is discussed in the text. DeepRICH has the advantage to bypass low-level details needed to build a likelihood, allowing for a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms. In the conclusions, we address the implications and potentialities of this work, discussing possible future extensions and generalization.