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forestflow

This code is a neural network emulator for the 3D flux power spectrum of the Lyman-alpha forest

https://github.com/igmhub/forestflow

Science Score: 44.0%

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Repository

This code is a neural network emulator for the 3D flux power spectrum of the Lyman-alpha forest

Basic Info
  • Host: GitHub
  • Owner: igmhub
  • Language: Python
  • Default Branch: main
  • Size: 126 MB
Statistics
  • Stars: 1
  • Watchers: 7
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Created over 2 years ago · Last pushed 7 months ago
Metadata Files
Readme Citation

README.md

ForestFlow

Lyman-alpha Cosmology Emulator. This code is a normalising flow emulator for the 3D flux power spectrum of the Lyman-alpha forest.

Emulator parameters:

These are the parameters that describe each individual P3D(k, mu) power spectrum. We have detached these from redshift and traditional cosmology parameters.

Cosmological parameters:

Delta2_p is the amplitude of the (dimensionless) linear spectrum at k_p = 0.7 1/Mpc

n_p is the slope of the linear power spectrum at k_p

IGM parameters:

mF is the mean transmitted flux fraction in the box (mean flux)

sigT_Mpc is the thermal broadening scale in comoving units, computed from T_0 in the temperature-density relation

gamma is the slope of the temperature-density relation

kF_Mpc is the filtering length (or pressure smoothing scale) in inverse comoving units

Tutorials:

In the Notebooks folder, there are several tutorials one can run to learn how to use the emulators and archives.

  • Archive tutorial: notebooks/Tutorial_archive.ipynb
  • Emulator tutorial: notebooks/Tutorial_emulator.ipynb

Installation

(Last update Jan 19 2024)

  • Create a new conda environment. It is usually better to follow python version one or two behind. In January 2024, the latest is 3.12, so we recommend 3.11.

conda create -n forestflow -c conda-forge python=3.11 camb fdasrsf pip=24.0 conda activate forestflow - Install LaCE:

Follow the instructions from https://github.com/igmhub/LaCE

  • Clone the ForestFlow repo and perform an editable installation:

git clone https://github.com/igmhub/ForestFlow.git cd ForestFlow pip install -e . [jupyter] # try with or without space between the . and jupyter if you need it

  • Generate notebooks:

pip install jupytext jupytext --to ipynb notebooks/*/*.py

  • If you want to use notebooks via JupyterHub, you'll also need to download ipykernel:

pip install ipykernel python -m ipykernel install --user --name forestflow --display-name forestflow

  • If you want to use Px routines, you need to install hankl:

pip install -e .[px]

Owner

  • Name: igmhub
  • Login: igmhub
  • Kind: organization

IGM analysis tools

Citation (citation.md) 

# Citation

If you use **ForestFlow** in your research, please cite the following paper:

> J. Chaves-Montero et al. *"ForestFlow: predicting the Lyman-$\alpha$ forest clustering from linear to nonlinear scales"*, 2024, arXiv:2409.05682.  
> [https://ui.adsabs.harvard.edu/abs/2024arXiv240905682C/abstract](https://ui.adsabs.harvard.edu/abs/2024arXiv240905682C/abstract)

### BibTeX

```bibtex
@ARTICLE{2024arXiv240905682C,
       author = {{Chaves-Montero}, J. and {Cabayol-Garcia}, L. and {Lokken}, M. and {Font-Ribera}, A. and {Aguilar}, J. and {Ahlen}, S. and {Bianchi}, D. and {Brooks}, D. and {Claybaugh}, T. and {Cole}, S. and {de la Macorra}, A. and {Ferraro}, S. and {Forero-Romero}, J.~E. and {Gazta{\~n}aga}, E. and {Gontcho}, S. Gontcho A and {Gutierrez}, G. and {Honscheid}, K. and {Kehoe}, R. and {Kirkby}, D. and {Kremin}, A. and {Lambert}, A. and {Landriau}, M. and {Manera}, M. and {Martini}, P. and {Miquel}, R. and {Mu{\~n}oz-Guti{\'e}rrez}, A. and {Niz}, G. and {P{\'e}rez-R{\`a}fols}, I. and {Rossi}, G. and {Sanchez}, E. and {Schubnell}, M. and {Sprayberry}, D. and {Tarl{\'e}}, G. and {Weaver}, B.~A.},
        title = "{ForestFlow: predicting the Lyman-$\alpha$ forest clustering from linear to nonlinear scales}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2024,
        month = sep,
          eid = {arXiv:2409.05682},
        pages = {arXiv:2409.05682},
          doi = {10.48550/arXiv.2409.05682},
archivePrefix = {arXiv},
       eprint = {2409.05682},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240905682C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

```

GitHub Events

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Dependencies

pyproject.toml pypi
  • FrEIA *
  • corner *
  • emcee *
  • numpy *
  • pydoe2 *