halogal 0.1.0

UV Luminosity Function and Halo Occupation Distribution modeling for high-redshift galaxies

halogal

A Python package for modeling UV luminosity functions and galaxy clustering using Halo Occupation Distribution (HOD) models for high-redshift galaxies.

Features

• Simple API: Only redshift required, scientifically validated defaults
• UV-Halo Mass Relation (UVHMR): Connect halo mass to UV luminosity through star formation
• Halo Occupation Distribution: Model central and satellite galaxy populations
• Luminosity Functions: Compute UV luminosity functions at high redshift
• Galaxy Bias: Calculate galaxy clustering bias
• Correlation Functions: Angular, real-space, and projected 2PCFs via halomod
• Efficient Parameter Updates: All observables accept inline **params for fast MCMC loops
• Dust Attenuation: Self-consistent treatment following Bouwens+2013-14
• Fitted Parameters: Defaults from Shuntov+2025
• Redshift Evolution: Built-in parameter evolution with redshift

Installation

From PyPI (when published)

pip install halogal

From source

git clone https://github.com/mshuntov/halogal.git
cd halogal
pip install -e .

Dependencies

• numpy >= 1.20
• scipy >= 1.7
• astropy >= 5.0
• colossus >= 1.3
• halomod >= 1.4

Quick Start

Minimal Example

import numpy as np
from halogal import HODModel
from halogal.model import Observables

# Create model — only redshift required!
# Uses fitted defaults from Shuntov+2025
model = HODModel(z=6.0)
obs = Observables(model)

# Compute luminosity function
MUV = np.linspace(-22, -16, 20)
phi = obs.luminosity_function(MUV)

Galaxy Bias

bias = obs.galaxy_bias(MUV)

UV-Halo Mass Relation

# UVHMR methods live directly on the model
Mh = 1e11  # M_sun
MUV = model.MUV(Mh)
sfr = model.sfr(Mh)

print(f"Halo mass {Mh:.2e} M_sun:")
print(f"  M_UV = {MUV:.2f}")
print(f"  SFR = {sfr:.1f} M_sun/yr")

# Inverse relation
Mh_recovered = model.Mhalo(MUV)

Override Specific Parameters

# At construction
model = HODModel(z=6.0, eps0=0.25, sigma_UV=0.5)
obs = Observables(model)

# Or inline when computing observables — no need to recreate anything
phi = obs.luminosity_function(MUV, eps0=0.3, sigma_UV=0.6)

Efficient MCMC Fitting

All observable methods on Observables accept **params keyword arguments to update the underlying model in-place before computing. This avoids object re-creation in tight loops:

obs = Observables(HODModel(z=6.0))
MUV = np.linspace(-22, -16, 20)

for eps0, sigma_UV in mcmc_samples:
    phi  = obs.luminosity_function(MUV, eps0=eps0, sigma_UV=sigma_UV)
    bg   = obs.galaxy_bias(MUV, eps0=eps0, sigma_UV=sigma_UV)
    mh   = obs.mean_halo_mass(-19.0, eps0=eps0, sigma_UV=sigma_UV)
    ngal = obs.number_density(-19.0, eps0=eps0, sigma_UV=sigma_UV)

For correlation functions, use the initialize/update pattern which leverages halomod's internal caching:

obs = Observables(HODModel(z=6.0))

# Initialize once (expensive)
result = obs.initialize_correlation_model(
    MUV_thresh1=-19.1, correlation_type='angular'
)

# Update efficiently in MCMC loop
for eps0, sigma_UV in mcmc_samples:
    result = obs.update_correlation_model(eps0=eps0, sigma_UV=sigma_UV)
    w_theta = result['correlation']
    theta   = result['separation']

Redshift Evolution

from halogal.models.parametrization import eps0_fz, Mc_fz
from halogal.config import DEFAULT_REDSHIFT_EVOLUTION

z_array = np.linspace(4, 8, 20)

# Get evolved parameters
eps0_z = eps0_fz(
    z_array, 
    deps_dz=DEFAULT_REDSHIFT_EVOLUTION['d_eps0_dz'],
    eps_off=DEFAULT_REDSHIFT_EVOLUTION['C_eps0']
)

Mc_z = 10**Mc_fz(
    z_array,
    dMc_dz=DEFAULT_REDSHIFT_EVOLUTION['d_logMc_dz'],
    Mc_off=DEFAULT_REDSHIFT_EVOLUTION['C_logMc']
)

# Compute with evolved parameters inline
obs = Observables(HODModel(z=z_array[0]))
for z, eps0, Mc in zip(z_array, eps0_z, Mc_z):
    phi = obs.luminosity_function(MUV, eps0=eps0, Mc=Mc)

Compare to Observations

from bouwens21_data import bouwens21, redshift_centers

data = bouwens21['z6']
z_obs = redshift_centers['z6']

obs = Observables(HODModel(z=z_obs))
MUV_model = np.linspace(-23, -15, 50)
phi_model = obs.luminosity_function(MUV_model)

plt.errorbar(data['M_AB'], data['Fi_k'], yerr=data['Fi_k_error'],
            fmt='o', label='Bouwens+2021')
plt.semilogy(MUV_model, phi_model, '-', label='Model')
plt.legend()
plt.show()

Documentation

Full documentation is available at https://uvlf-hod.readthedocs.io/en/latest/#.

Package Structure

halogal/
├── __init__.py          # Public API
├── config.py            # Configuration and defaults
├── cosmology.py         # Halo mass function and bias
├── model.py             # Unified UVHMR, HOD, and Observables
├── luminosity.py        # UV luminosity and dust
└── models/
    └── parametrization.py  # Redshift parametrizations

Key Classes

• HODModel: Galaxy population model combining UVHMR + HOD (parameters and occupation functions)
• Observables: Compute observables (UVLF, bias, correlation functions) from an HODModel; supports efficient inline parameter updates via **params
• UVHMRModel: Base class for UV-halo mass relations only

Model Architecture

UVHMRModel (base class)
├── Handles UV-halo mass relations
├── Methods: sfr(), MUV(), Mhalo(), star_formation_efficiency()
└── Parameters: z (required), eps0, Mc, a, b (optional)

HODModel (extends UVHMRModel)
├── Inherits all UVHMR methods
├── Adds occupation distributions: Ncen(), Nsat(), Ngal()
└── Additional parameters: sigma_UV, Mcut, Msat, asat (optional)

Observables (takes an HODModel)
├── luminosity_function(MUV, **params)
├── galaxy_bias(MUV, **params)
├── mean_halo_mass(MUV_thresh, **params)
├── mean_bias(MUV_thresh, **params)
├── number_density(MUV_thresh, **params)
├── initialize_correlation_model() / update_correlation_model()
└── compute_correlation_function()

All Observables methods accept **params (eps0, Mc, a, b, sigma_UV, Mcut, Msat, asat) to update the model inline before computing.

Default Parameters

All defaults from Shuntov+2025 (2025A&A...699A.231S) at z~5.4:

UVHMR Parameters

Parameter	Default	Description
eps0	0.19	Star formation efficiency
Mc	10^11.64 M_☉	Characteristic halo mass
a	0.69	Low-mass slope
b	0.65	High-mass slope

HOD Parameters

Parameter	Default	Description
sigma_UV	0.69 mag	UV magnitude scatter
Mcut	10^9.57 M_☉	Satellite cutoff mass
Msat	10^12.65 M_☉	Satellite normalization
asat	0.85	Satellite power-law slope

Redshift Evolution

All parameters evolve as: param(z) = d_param/dz × z + C_param

Evolution parameters available in DEFAULT_REDSHIFT_EVOLUTION.

Examples

See the examples/ directory for detailed examples:

• basic_usage.ipynb: Interactive Jupyter notebook with complete workflow
• bouwens21_data.py: Observational data compilation

Testing

Run tests with pytest:

pytest tests/

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

Citation

If you use this package in your research, please cite:

@ARTICLE{2025A&A...699A.231S,
       author = {{Shuntov}, Marko and {Oesch}, Pascal A. and {Toft}, Sune and {Meyer}, Romain A. and {Covelo-Paz}, Alba and {Paquereau}, Louise and {Bouwens}, Rychard and {Brammer}, Gabriel and {Gelli}, Viola and {Giovinazzo}, Emma and {Herard-Demanche}, Thomas and {Illingworth}, Garth D. and {Mason}, Charlotte and {Naidu}, Rohan P. and {Weibel}, Andrea and {Xiao}, Mengyuan},
        title = "{Constraints on the early Universe star formation efficiency from galaxy clustering and halo modeling of H{\ensuremath{\alpha}} and [O III] emitters}",
      journal = {\aap},
     keywords = {galaxies: evolution, galaxies: high-redshift, galaxies: luminosity function, mass function, galaxies: statistics, Astrophysics of Galaxies},
         year = 2025,
        month = jul,
       volume = {699},
          eid = {A231},
        pages = {A231},
          doi = {10.1051/0004-6361/202554618},
archivePrefix = {arXiv},
       eprint = {2503.14280},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025A&A...699A.231S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Based on methodology from:

• Shuntov et al. 2025, A&A 699 A321
• Sabti et al. 2022
• Muñoz et al. 2023
• Bouwens et al. 2013, 2014
• And others...

Contact

For questions or issues, please open an issue on GitHub or contact [e-mail].