vlne

Package to train Deep Learning neutrino energy estimators operating on particle information.

This package is a continuation of the lstm_ee work, but aimed at supporting version 2 of TensorFlow framework.

Overview

This package is designed to simplify training of the energy estimators for various neutrino experiments. It contains an extensive library of helper functions and a number of scripts to train different kinds of energy estimators and evaluate them. Please refer to Documentation for the details.

This package is inspired by the rnnNeutrinoEnergyEstimator which laid the groundwork for the deep learning energy estimator development.

Installation

vlne package is intended for developers. Therefore, it is recommended to install the live version of the package, for example:

1. Git clone this repository:

git clone https://github.com/usert5432/vlne

2. Setup vlne package, by running

python setup.py develop --user

in the vlne directory.

If you are running vlne for the first time it might be useful to run its test suite to make sure that the package is not broken:

python -m unittest

Requirements

vlne package is written in python v3 and won't work with python v2. vlne depends on the following packages:

• tensorflow -- for training of neural networks. TensorFlow v2.9 and above are tested. One may try running vlne with the lower TF versions, but you may need to modify some code parts. Unfortunately, TF does not guarantee backward compatibility even between minor releases.
• pandas, numpy -- basis of python data handling.
• scipy -- for fitting curves.
• vlndata -- for working with tabular and variable length array data formats. Needs manual installation.

Make sure that these packages are available on your system.

There are multiple ways these packages can be installed. The contrib/containers subdirectory provides definitions of various containers that pack the above mentioned packages. Alternatively, these packages can be installed with pip by running

pip install --user -r requirements.txt

Also, vlne has a number of optional dependencies:

• speval -- for parallelizing training across multiple machines.
• cafplot -- for creating and plotting histograms

Documentation

vlne package comes with a basic documentation in the sphinx format. You can compile the sphinx documentation by running the following command in the doc subdirectory (requires sphinx installed):

make html

It will build all available documentation, which can be viewed with a web browser by pointing it to the build/html/index.html file.

In addition to the sphinx documentation the vlne code is covered by a numpy like docstrings. Please refer to the docstrings and the source code for the details about inner vlne workings.

Example Usage

This subsection provides a summary of a usage example of the vlne package to train and evaluate a Deep Learning energy estimator. To start training a neutrino energy estimator one needs to obtain a training dataset.

The instructions on how to obtain a training dataset are experiment specific. For example, NOvA's training dataset can be obtained according to following guide, and this link gives instructions specific to the DUNE experiment.

After the training dataset has been obtained, one can start the training itself. vlne packs multitude of training scripts for various experiments and training variations (under scripts/train). All the scripts are essentially declarative files that define a training configuration and call train(config).

The training configuration is hierarchical and has intuitive structure. It can be easily modified for a particular need. One can use this scripts/train/nova/numu/mprod5/final/train_fd_fhc.py training script as a starting point for developing a training configuration for a new dataset.

Finally, once the training is complete, one can begin evaluation of the performance of the Deep Learning energy estimator. vlne has several scripts to do that under the scripts/eval subdirectory. For example, scripts/eval/eval_model.py can be used to evaluate energy resolution of the energy estimators.