LaCE

LaCE contains a set of emulators for the one-dimensional flux power spectrum of the Lyman-alpha forest. It has been used in the papers:

• https://arxiv.org/abs/2011.15127
• https://arxiv.org/abs/2209.09895
• https://arxiv.org/abs/2305.19064 (latest version)

Please cite at least https://arxiv.org/abs/2305.19064 if you use this emulator in your research.

There is a documentation website with installation instructions and relevant descriptions at [here](https://igmhub.github.io/LaCE/

Installation

(Last updated: Nov 19 2024)

LaCE contains a submodule to estimate compressed parameters from the power spectrum that uses cosmopower. The LaCE installation is slightly different depending on whether you want to use cosmopower or not.

LaCE without cosmopower

• 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. If you want to use LaCE with cosmopower, as of November 2024 you need to install python 3.10. Please look at the cosmopower installation before proceeding with the LaCE installation.

conda create -n lace -c conda-forge python=3.11 pip 
conda activate lace
pip install --upgrade pip

• Clone the repo into your machine and perform an editable installation:

git clone https://github.com/igmhub/LaCE.git
cd LacE
pip install -e .

• If you find problems, please install LaCE as follows:

pip install -e ".[explicit]"

LaCE with cosmopower

• Create a new conda environment.

conda create -n lace python=3.11 pip 
conda activate lace
pip install --upgrade pip

• Install cosmopower:

  pip install cosmopower pyDOE

• Clone the repo into your machine and perform an editable installation:

  git clone https://github.com/igmhub/LaCE.git
  cd LacE

• Install LaCE using the installation with explicit dependencies:

  pip install -e ".[explicit]"

Tests

Please run the following script to check that the package is working properly.

python test_lace.py

Nyx users:

• You may need to add the Nyx path as an environment variable in your notebook kernel. The first is done by writing in the kernel.json file:

 "env": {
  "NYX_PATH":"path_to_Nyx"
 }

You also need to add the Nyx path as an environment variable. The Nyx data is located at NERSC in

NYX_PATH="/global/cfs/cdirs/desi/science/lya/y1-p1d/likelihood_files/nyx_files/"

• Before running LaCE, please precompute all cosmological information needed using CAMB and save IGM histories. This is done by running the following scripts. You do not need to do it if you are in NERSC.

python scripts/save_nyx_emu_cosmo.py
python scripts/save_nyx_IGM.py

Emulator parameters:

These are the parameters that describe each individual P1D(k) 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

alpha_p is the running of the linear power spectrum at k_p

f_p is the (scale-independent) logarithmic growth rate

The current version of the emulator, relased in this repo, does not emulate alpha_p and f_p. However, these parameters are stored in the P1D archive.

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

Notebooks / tutorials

• All notebooks in the repository are in .py format. To generate the .ipynb version, run:

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 lace --display-name lace

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

• Archive tutorial: notebooks/Tutorial_bookkeeping.py
• Emulator tutorial: notebooks/Tutorial_emulator.py
• Emulating compressed parameters tutorial: notebooks/Tutorial_compressedParams.ipynb