SiMPLE-Gen

Simulated Mock Population of Lyman-Alpha Emitters Generator

A modular, end-to-end pipeline to generate simulated Lyman-α emitters from cosmological simulation outputs.

Note: This pipeline builds upon the methodology outlined in Weinberger et al. (2019), which models intrinsic and transmitted Lyman-α emission based on halo properties and radiative transfer approximations.

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Setup

1. Clone the repo:

   git clone https://github.com/yourorg/SiMPLE-Gen.git
   cd SiMPLE-Gen

2. Install dependencies:

   pip install -r requirements.txt

3. Prepare your raw data:

   • Ensure the necessary directories exist by running:

     mkdir -p data/raw data/processed

   • Place simulation files (density, temperature, ionization, velocity, halo positions & masses) in data/raw/.

   • Edit SiMPLE-Gen/config.py ▶︎ INPUT_DIR to point at that folder.

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Repository Structure

SiMPLE-Gen/              # root folder (repo name)
├── run.py               # master pipeline script
├── requirements.txt     # Python dependencies
├── README.md            # this file
├── .gitignore
└── SiMPLE-Gen/          # Python package
    ├── config.py        # all RAW & PATHS definitions
    ├── gen.py           # Step 1: sightline generation
    ├── spec.py          # Step 2: τ(z) calculation
    ├── abundance.py     # Step 3: abundance matching
    ├── assign.py        # Step 4: LLya & REW sampling
    └── damping.py       # Step 5: damping transmission

Simulation inputs live outside this repo under data/raw/, and outputs are written to data/processed/ by default.

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Running the Pipeline

Execute the master script:

python run.py

This calls each step in sequence: gen.py, spec.py, abundance.py, assign.py, damping.py.

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Module Descriptions

• gen.py: Extracts 1D neutral-hydrogen, temperature, and velocity sightlines around halos.
• spec.py: Computes Lyman-α optical depth τ(z) along each sightline using your Voigt-profile routine.
• abundance.py: Builds a Sheth–Tormen halo mass function, Schechter UV LF, applies duty cycle, then matches halo masses ↔ UV magnitudes.
• assign.py: Defines probability distributions for rest-frame equivalent width (REW) and Lyman-α luminosity, and Monte Carlo-samples each halo’s values.
• damping.py: Convolves τ(v) with a halo-velocity Gaussian to compute the equivalent-width decrease ratio.

All file paths are centralized in config.py—no hidden hardcoded paths.

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