MINATO: Massive bINaries Analysis TOols

Python tools for the comprehensive analysis of massive binary stars, focusing on precise radial velocity measurement, spectral line profile fitting, and robust time series analysis.

Version 0.1.0

• Spectral analysis (span):
  • Simultaneous spectral fitting of binary stars using synthetic models.
  • Computes effective temperatures, log surface gravities, rotational velocities, He/H ratios, and the light ratio of the binary.

Version 0.2.0

Added Features:

• Radial Velocity Determination and Time Series Analysis (ravel)

  • Spectral Line Profile Fitting (SLfit)

    • Supports single-lined (SB1) and double-lined (SB2) spectroscopic binaries.
    • Automated Gaussian/Lorentzian line profile fitting with customizable priors.
    • For SB2s:
      • Probabilistic modeling using Bayesian inference with Numpyro.
      • Direct radial velocity computation.

  • Radial Velocity Analysis for SB1s (GetRVs)

    • Automated computation of radial velocities (RVs) from fitted spectral lines.
    • Weighted mean RV calculations with built-in outlier rejection based on median absolute deviation (MAD).
    • Comprehensive statistical summaries and error propagation for reliable velocity measurements.

  • Time Series and Period Analysis

    • Implementation of Lomb-Scargle periodograms with false alarm probability (FAP) estimation.
    • Automatic peak detection with adjustable significance thresholds.
    • Probabilistic sinusoidal model fitting to phased radial velocity curves for orbital characterisation.

Planned Future Features

• Simulations of binary populations
• Spectral Energy Distribution (SED) fitting
• Automated spectral classification of massive stars

Installation

Clone the repository:

git clone https://github.com/jvillasr/MINATO/

Use your favourite dependency manager:

cd MINATO
mamba env create -f minato_env.yml

Usage

Detailed examples are provided in the minato/tutorials directory.

Dependencies

• tested under Python 3.10
• astropy
• jax
• lmfit
• matplotlib
• numpy
• numpyro
• pandas
• scipy
• tqdm

Contributing and Issues

Contributions and bug reports are welcome! Please submit an issue on GitHub or open a pull request.

Citation

If you use MINATO in your research, please cite:

• For span:

Villaseñor et al., 2023, MNRAS, 525, 5121, 10.1093/mnras/stad2533

• For ravel:

Villaseñor et al., 2025, A&A accepted, 10.48550/arXiv.2503.21936

License

This project is licensed under the MIT License. See LICENSE for more information.