https://github.com/dpiras/vaexede

A repository to host the trained models from https://arxiv.org/abs/2502.09810
https://github.com/dpiras/vaexede

Last synced: 3 months ago
JSON representation

A repository to host the trained models from https://arxiv.org/abs/2502.09810

• Host: GitHub
• URL: https://github.com/dpiras/vaexede
• Owner: dpiras
• Created: 2025-03-19T14:25:07.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-19T14:26:12.000Z (3 months ago)
• Last Synced: 2025-03-19T15:32:12.128Z (3 months ago)
• Homepage:
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# VAExEDE

![](https://img.shields.io/badge/Python-181717?style=plastic&logo=python)

![](https://img.shields.io/badge/Author-Davide%20Piras%20-181717?style=plastic)

![](https://img.shields.io/badge/Installation-pip%20install%20vaexede-181717?style=plastic)

[![arXiv](https://img.shields.io/badge/arXiv-2502.09810-b31b1b.svg)](https://arxiv.org/abs/2502.09810)

A repository to host the trained models from [https://arxiv.org/abs/2502.09810](https://arxiv.org/abs/2502.09810v1), where we trained a variational autoencoder (VAE) on CMB temperature power spectra for early dark energy (EDE) models, to discover novel, data-driven parametrizations.  

## Installation

To use the trained models, follow these steps:

1. (optional) `conda create -n vaexede python=3.11 jupyter` (create a custom `conda` environment with python 3.11) 

2. (optional) `conda activate vaexede` (activate it)

3. Install the package:

        pip install vaexede

        python -c 'from vaexede.load_models import load_model'

   or alternatively, clone the repository and install it:

        git clone https://github.com/dpiras/VAExEDE.git

        cd VAExEDE

        pip install . 

        python -c 'from vaexede.load_models import load_model'

The latter option will also give you access to a [Jupyter notebook with a quick walkthrough](https://github.com/dpiras/VAExEDE/blob/main/notebooks/quickstart.ipynb) on how to use the models.

## Usage

A simple way to load and use the trained models looks like this:

    model = 'lcdm' # either 'lcdm' or 'ede'

    lcdm_model = load_model(model) # load the trained network

    # add your unitless D_ell temperature spectrum here, in the ell range [30, 2500]

    # should also support batches of data, has not been tested though

    # D_ell = C_ell * ell * (ell+1) / 2pi

    input_spectrum = # your spectrum here

    # and preprocess it as described in the paper

    input_spectrum_preprocess = preprocess(input_spectrum, model)

    # here the preprocessed spectrum gets encoded, samples from the latent space are obtained

    # then the spectrum is decoded and unpreprocessed

    mean, logvar = lcdm_model.encode(input_spectrum_preprocess.reshape(1, -1))

    z = lcdm_model.reparameterize(mean, logvar) # here we sample from the latent distribution

    decoded_spectrum = lcdm_model.decode(z)

    output_spectrum_lcdm = unpreprocess(decoded_spectrum[0, :, 0], model)

## Disclaimer

The repository contains only some of the material needed to reproduce the paper. If you need more or would like to add a feature, feel free to [fork](https://github.com/dpiras/VAExEDE/fork) this repository to work on it; otherwise, please [raise an issue](https://github.com/dpiras/VAExEDE/issues) or contact [Davide Piras](mailto:[email protected]).

## Contributors

[Laura Herold](https://github.com/LauraHerold) and [Luisa Lucie-Smith](https://github.com/lluciesmith) contributed to this code.

## Citation

If you use this code, please cite the corresponding paper:

     @article{Piras:2025eip,

     author = "Piras, Davide and Herold, Laura and Lucie-Smith, Luisa and Komatsu, Eiichiro",

     title = "{$\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum}",

     eprint = "2502.09810",

     archivePrefix = "arXiv",

     primaryClass = "astro-ph.CO",

     month = "2",

     year = "2025"

     }

## License

This code is released under the GPL-3 license - see [LICENSE](https://github.com/dpiras/VAExEDE/blob/main/LICENSE.txt)-, subject to the non-commercial use condition - see [LICENSE_EXT](https://github.com/dpiras/VAExEDE/blob/main/LICENSE_EXT.txt).

     VAExEDE

     Copyright (C) 2025 Davide Piras & contributors

     This program is released under the GPL-3 license (see LICENSE.txt), 

     subject to a non-commercial use condition (see LICENSE_EXT.txt).

     This program is distributed in the hope that it will be useful,

     but WITHOUT ANY WARRANTY; without even the implied warranty of

     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.