STI Publications - View Publication Form #16293
Back to Search Results |
Publication Information
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 |
|
||||
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) |
Fanelli_2020_Mach._Learn.__Sci._Technol._1_015010.pdf
(STI Document)
Fanelli_2020_Mach._Learn.__Sci._Technol._1_015010.pdf
(Accepted Manuscript)
|
DOI Link | |
Dataset(s) | (none) |
Back to Search Results |