https://github.com/rikab/GaussianAnsatz

https://github.com/rikab/GaussianAnsatz

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/rikab/GaussianAnsatz
• Owner: rikab
• Created: 2022-01-05T03:53:13.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-04-17T20:06:47.000Z (over 1 year ago)
• Last Synced: 2024-07-14T00:24:37.930Z (4 months ago)
• Language: Python
• Size: 117 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Gaussian Ansatz (v0.0.1)

[![GitHub Project](https://img.shields.io/badge/GitHub--blue?style=social&logo=GitHub)](https://github.com/rikab/GaussianAnsatz)

The `Gaussian Ansatz` is a machine learning framework for performing frequentist inference, complete with local uncertainty estimation, as described in ["Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics" (arxiv:2205.03413)](https://arxiv.org/abs/2205.03413). These models can be used to quickly estimate local resolutions of an inference $z$ given a measurement $x$, even if $x$ is very high-dimensional.

## Installation

### From PyPI

In your Python environment run

```

python -m pip install GaussianAnsatz

```

### From this repository locally

In your Python environment from the top level of this repository run

```

python -m pip install .

```

### From GitHub

In your Python environment run

```

python -m pip install "GaussianAnsatz @ git+https://github.com/rikab/GaussianAnsatz.git"

```

## Example Usage

For an example of how to use the Gaussian Ansatz, see the notebook `examples/minimal_working_example.ipynb`. This notebook contains example code for loading data, using pre-built DNN Gaussian Ansatz, pre-training and training, and extracting inferences with uncertainty estimates.

Additional, more complicated examples can be found in the `JEC` subfolder. The files here correspond exactly to the jet energy studies, as described in ["Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics" (arxiv:2205.03413)](https://arxiv.org/abs/2205.03413)

## Dependencies

To use the `Gaussian Ansatz`, the following packages must be installed as prerequisites:

- [Tensorflow](https://github.com/tensorflow/tensorflow): A standard tensor operation library.

- [Energyflow](https://energyflow.network/): A suite of particle physics tools, including Energy Flow Networks and Particle FLow Networks

- Standard python packages: [numpy](https://numpy.org/), [scipy](https://scipy.org/), [matplotlib](https://matplotlib.org/)

## Citation

If you use the `Gaussian Ansatz`, please cite both the corresponding paper, "Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics"?:

    @article{Gambhir:2022gua,

    author = "Gambhir, Rikab and Nachman, Benjamin and Thaler, Jesse",

    title = "{Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics}",

    eprint = "2205.03413",

    archivePrefix = "arXiv",

    primaryClass = "hep-ph",

    reportNumber = "MIT-CTP 5431",

    doi = "10.1103/PhysRevLett.129.082001",

    journal = "Phys. Rev. Lett.",

    volume = "129",

    number = "8",

    pages = "082001",

    year = "2022"

}

## Changelog

- v0.0.1: 17 April 2023. Now pip-installable!

- v0.0.0: 6 May 2022. Public release.

Based on the work in ["Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics" (arxiv:2205.03413)](https://arxiv.org/abs/2205.03413)

Bugs, Fixes, Ideas, or Questions? Contact me at [email protected]