Contents
This section provides a general summary for the non-physicist. For an introduction to the UCNτ neutron lifetime experiment, we suggest the thesis by Gonzalez (2021), Precision Measurement of the Neutron Lifetime with the UCNτ Experiment. The goal of this project is to examine applications of computer vision ML models towards counting neutron events in detectors, with the idea of replacing existing moving-window algorithms. The existing moving-window algorithms require more complicated instrumentation (such as additional detectors) and are computationally expensive.
One method of ultracold neutron detection involves capturing a neutron on a material, emitting some photons (i.e. light), and then detecting the photons with a photomultiplier tube (PMT). The probability distribution function (PDF) of photons for a single neutron event can be depicted as in Fig. 1. Typically, there are 30 - 40 photons per neutron event.
A single neutron event is not difficult to count. A naive method would be to bin photon counts and to set a threshold. However, what happens if there are two (or more) neutron events that occur in quick succession? We call this a pileup event, and an example instance PDF is displayed in Fig. 2.
The sparsity of photon counts per neutron event in combination with the long PDF decay "tails" means that it may be easy to miscontstrue multiple events as only one. Miscounts are extremely detrimental to the accuracy of UCNτ, as the aggregation of a handful of miscounts per run easily skew experimental accuracy over the course of a multi-year measurement cycle. Pileup events are further complicated with the influence of background photon counts. For instance, the neutron absorption material glows for a very long time after being activated, and over the course of a neutron counting period the background photon count steadily increases. Other background events are coupled to temperature, ambient light leaks, cosmic rays, or radioactive decays from the experimental area.
This section will be updated with models as they are implemented and evaluated
Multi Scale 1D ResNet by Fei Wang et al.
https://github.com/geekfeiw/Multi-Scale-1D-ResNet/tree/master
@article{wang2018csi,
title={CSI-Net: Unified Body Characterization and Action Recognition},
author={Wang, Fei and Han, Jinsong and Zhang, Shiyuan and He, Xu and Huang, Dong},
journal={arXiv preprint arXiv:1810.03064},
year={2018}
}
1D ResNet by Shenda Hong et al.
https://github.com/hsd1503/resnet1d.git
@inproceedings{hong2020holmes,
title={HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units},
author={Hong, Shenda and Xu, Yanbo and Khare, Alind and Priambada, Satria and Maher, Kevin and Aljiffry, Alaa and Sun, Jimeng and Tumanov, Alexey},
booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1614--1624},
year={2020}
}
Training, validation, and test data is stored as a numpy memmap to avoid loading the entire dataset into memory, and is implemented with torch.utils.data.Dataset. One label (number of neutron events) is associated with a 1D input tensor representing a binned timeseries of photon events (1 ns bins). Labels are stored in a corresponding csv file.
Tested on a Windows 10 PC with a 3070TI, Git bash and Conda
Install the Git Bash utility for Windows from Git SCM
(Useful note on git-credential store methods for Windows here)
The coincidences file (coinc_4200_5441.dat) is large and requires the use of the Git large file storage (LFS). A commit + push that involves this file counts against the 1 Gb/mo bandwidth in my account. Instructions for installing Git LFS are here.
On Ubuntu, the apt-get package manager can install Git LFS. On Windows, the Git Bash install may bundle Git LFS for you. Installation of Git LFS may be verified by running the below command in the pulseTrain root directory
git lfs envgit clone https://github.com/dougUCN/pulseTrain.gitInstall Conda following the instructions here
To get Conda to run on Git Bash, to the ~/.bashrc configuration file add the line
source /c/Users/$USERNAME/anaconda3/etc/profile.d/conda.shwith $USERNAME replaced appropriately, or the path modified if you chose a different installation directory for conda. Then, restart your Git Bash instance.
As per Nvidia's instructions, installation of the CUDA Toolkit with Conda simply requires
conda install cuda -c nvidiaVerify the install with nvcc -V. The output for my device is
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Jun_13_19:42:34_Pacific_Daylight_Time_2023
Cuda compilation tools, release 12.2, V12.2.91
Build cuda_12.2.r12.2/compiler.32965470_0
Uninstall with conda remove cuda
Create a conda environment from the provided environment.yml file
conda env create -f environment.yml
conda activate pulseTrainOther useful conda commands
conda install PACKAGE_NAME # Install a package
conda deactivate # Exit the environment
conda env list # List all conda environments
conda create --name ENV_NAME # Create a new conda environment carrying over dependencies from base
conda env export > environment.yml # Export active environment to a configuration file
conda remove --name ENV_NAME --all # deletes environment ENV_NAME and uninstalls associated packages$ python generate_training_data.py --help
usage: generate_training_data.py [-h] [-l LENGTH] [-sb STARTBUFFER] [-eb ENDBUFFER] [-ucn UCN UCN] [-p PHOTONS PHOTONS]
Generate training, validation, and test data sets via Monte Carlo
options:
-h, --help show this help message and exit
-l LENGTH, --length LENGTH
Number of bins in the pileup window. [1 bin = 1 ns] (default: 2000)
-sb STARTBUFFER, --startBuffer STARTBUFFER
Allows UCN events to occur before the pileup window by some number of bins [1 bin = 1 ns]
(default: 50)
-eb ENDBUFFER, --endBuffer ENDBUFFER
Does NOT allow UCN events to occur before the end of the pileup window by some number of bins [1
bin = 1 ns] (default: 25)
-ucn UCN UCN, --ucn UCN UCN
[min, max] number of allowed UCN events per dataset (default: [0, 50])
-p PHOTONS PHOTONS, --photons PHOTONS PHOTONS
[av, std_dev] Gaussian dist. of photons (integer) to generate per UCN event (default: [35, 2.5])
$ python train_model.py --help
usage: train_model.py [-h] [-f FILENAME] [--showModelOnly]
Train model
options:
-h, --help show this help message and exit
-f FILENAME, --filename FILENAME
metadata filename (default: in/metadata.csv)
--showModelOnly Exit immediately after displaying model params (default: False)
For additional documentation and contribution guidelines see CONTRIBUTING.md