DeepRICH: learning deeply Cherenkov detectors

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.

Machine learning for imaging Cherenkov detectors

“…In fact preliminary results show high reconstruction efficiency combined to fast inference time: in particular the time reconstruction of O(ms) per batch of particles makes DeepRICH potentially faster than established reconstruction methods available at present [37].…”

“…The authors of this work claim a good precision and very fast performance from their studies. A novel deep learning algorithm for fast reconstruction has been proposed in [37] which can be applied to any imaging Cherenkov detectors. The core of this architecture is a generative model which leverages on a custom Variational Auto-encoder (VAE) [38] combined to Maximum Mean Discrepancy (MMD) [39], with a Convolutional Neural Network (CNN) [11] extracting features from the space of the latent variables for classification.…”

“…A flowchart of DeepRICH: the inputs are concatenated-n.b., the ⊕ represents the concatenation between vectors-and injected into the encoder, which generates a set of vectors of latent variables, which are then used for both the classification of the particle and for the reconstruction of the hits. Image taken from [37].…”

“…The Decoder reconstructs the input hits using the latent variables and the kinematic parameters. Standard regularization techniques (e.g., dropout, batch normalization) are considered in order to prevent overfitting and improve performance and stability of the network (more details can be found in Table 1 of [37]).…”

“…A thorough comparison with the simulation/reconstruction package FastDIRC is discussed in [37]. DeepRICH has the advantage to bypass low-level details needed to build a likelihood and allows to achieve a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms.…”