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Title 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 computatio
Author(s) Cristiano Fanelli , Jary Pomponi
Publication Date April 2020
Document Type Journal Article
Primary Institution Massachusetts Institute of Technology, Cambridge, MA
Affiliation Exp Nuclear Physics / Experimental Halls / Hall A
Funding Source Nuclear Physics (NP), Jefferson Lab EIC Center Fellowship, FG02-94ER40 818
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-PHY-20-3179 OSTI Number: 1616671
LANL Number: 1911.11717 Other Number: DOE/OR/23177-4965
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Machine Learning: Science and Technology
Refereed Yes
Volume 1
Issue
Page(s) 015010
Attachments/Datasets/DOI Link
Document(s)
DOI Link
Dataset(s) (none)
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