Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.
In the paper \"Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning\", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and ..., , The program is tested with the following setting:

python==3.9.5
tf-nightly-gpu==2.7.0.dev20210730
keras-nightly==2.7.0.dev2021073000
tensorflow-model-optimization==0.6.0
numpy==1.19.5
scipy==1.6.2
matplotlib==3.3.4
pandas==1.3.0
pyyaml==5.4.1

Newer versions may also work.
 
When data and software files are downloaded, please unzip temp.zip and PhyECAL.zip, and put the folder temp under the root folder of PhyECAL, so that the computer code will work properly.
root directory: contain main routine scripts to train neural networks (NNs), and README.md (this file).

 ./s_toy_routine.py: Python script to train NNs on the toy experiment.
 ./s_basic_routine.py: Python script to train NNs on the ECAL experiment.
 ./README.md: This file.

`./conf/` directory: configuration files for main routine scripts.

 laser_in2048_[cluster]_[frequency]_2ch_internal.yaml: Configuration files for the toy experiment. Use optional [cluster] to select data, use opitional low-pass filter..., # Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

## Introduction

This repository holds the computer code and raw data to reproduce the results in the paper: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.

In the paper \"Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning\", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event da...,