DeepRICH: learning deeply Cherenkov detectors

Cristiano Fanelli,Jary Pomponi

doi:10.1088/2632-2153/ab845a

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.

Highlights

Imaging Cherenkov detectors [1] measure the velocity of charged particles and combined to independent measurements of their momentum are largely used for PID in modern particle physics experiments
For completeness we report a comparison with the reconstruction time of other methods: for look-up-tablebased algorithms, not fully optimized estimates provide order few ms per track on a single standard CPU [40]; for FastDIRC it is about 300 ms per track on a Macbook
The DeepRICH architecure developed in this paper shows very promising results

Summary

Introduction

Imaging Cherenkov detectors [1] measure the velocity of charged particles and combined to independent measurements of their momentum are largely used for PID in modern particle physics experiments. The pattern recognition of the rings is typically likelihood-based and requires computationally expensive simulations, different strategies (among which pre-computed look-up tables) have been developed to find a trade-off between time and reconstruction performance. The first DIRC detector was developed by the BaBar experiment at SLAC [5], and inspired other experiments (see, e.g., [6,7,8]) to utilize similar detectors, in view of future experiments like the Electron Ion Collider [9]. In the following we will consider as an example the case of the GlueX experiment [2, 10] at the Jefferson Laboratory, where the DIRC has been recently installed utilizing components of the decommissioned BaBar DIRC to enhance the PID capabilities of the experiment

Methods

Results

Conclusion