Publications
Publication Information
| Title | DeepRICH: learning deeply Cherenkov detectors |
| Authors | Cristiano Fanelli, Jary Pomponi |
| JLAB number | JLAB-PHY-20-3179 |
| LANL number | 1911.11717 |
| Other number | DOE/OR/23177-4965 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
| Other Funding: | Jefferson Lab EIC Center Fellowship FG02-94ER40 818 |
|
Journal Compiled for Machine Learning: Science and Technology Volume 1 Page(s) 015010 Refereed | |
| Publication 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. |
| Experiment Numbers: | other |
| Group: | Hall A |
| Document: | |
| DOI: | https://doi.org/10.1088/2632-2153/ab845a |
| Accepted Manuscript: | Fanelli_2020_Mach._Learn.__Sci._Technol._1_015010.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |