gyronet 0.1.2

Stellar age prediction via gyrochronology with Bayesian posterior reweighting

GyroNet

Stellar age prediction via gyrochronology. GyroNet takes a star's rotation period and color, optionally combined with auxiliary Gaia features, and returns a full posterior over its age using a Bayesian ensemble with adaptive out-of-distribution protection.

Installation

pip install gyronet

Or from source:

git clone https://github.com/26amitdethe/GyroNet.git
cd GyroNet
pip install -e .

Quickstart

Option 1: All auxiliary features provided

For the most accurate predictions, provide the full set of Gaia DR3 auxiliary features alongside the core inputs. With fetch=False, no network call is made. If you've already supplied the aux columns, GyroNet won't query Gaia. (Note: fetch only ever triggers when a GaiaDR3_ID is given; if none of the optional columns are needed, its value is irrelevant.)

import gyronet

posterior = gyronet.predict(
    Prot=6.312,
    BPRP_0=1.042798386,
    e_BPRP_0=0.01570765184,
    phot_bp_rp_excess_factor=1.2295822,
    astrometric_excess_noise_sig=0.0,
    G_0=13.87433119,
    parallax=2.471274462,
    fetch=False,
)

print(f"Peak age : {posterior.peak():.1f} Myr")
print(f"Median age : {posterior.median():.1f} Myr")
print(f"68% CI : {posterior.credible_interval(0.68)}")

Option 2: Required inputs + Gaia DR3 ID, auto-fetch enabled

If you know the Gaia DR3 source ID, GyroNet can fetch the auxiliary features from the Gaia archive automatically. This requires an internet connection.

import gyronet

posterior = gyronet.predict(
    Prot=6.312,
    BPRP_0=1.042798386,
    e_BPRP_0=0.01570765184,
    GaiaDR3_ID=5290651323511794816,
    fetch=True,
)

print(f"Peak age : {posterior.peak():.1f} Myr")
print(f"Median age : {posterior.median():.1f} Myr")
print(f"68% CI : {posterior.credible_interval(0.68)}")

Option 3: Required inputs only (baseline model)

If you only have the rotation period and color, GyroNet will still produce a prediction using the baseline model alone. This skips the auxiliary-feature reweighting that sharpens and calibrates Option 1/2 predictions, so the resulting posterior will generally be less precise.

import gyronet

posterior = gyronet.predict(
    Prot=6.312,
    BPRP_0=1.042798386,
    e_BPRP_0=0.01570765184,
    fetch=False,
)

print(f"Peak age : {posterior.peak():.1f} Myr")
print(f"Median age : {posterior.median():.1f} Myr")
print(f"68% CI : {posterior.credible_interval(0.68)}")

Documentation

• Quickstart
• Input format
• API reference

Method

The shipped ensemble averages three posteriors derived from two trained Neural Spline Flow models and two Bayesian reweighting strategies over Gaia DR3 auxiliary features:

1. Precision-Weighted Flow + Cluster-Kernel Likelihood, Reliability-Masked — a flow trained with a precision-weighted loss, reweighted by a likelihood built from a Gaussian kernel smoother over per-cluster feature statistics, and gated by an adaptive reliability mask.
2. Baseline Flow + Learned Likelihood, Reliability-Masked — the baseline flow, reweighted by learned MLP likelihoods of the noise features, with the same reliability mask.
3. Baseline Flow + Tempered Learned Likelihood (T=0.7) — the baseline flow reweighted by the same learned likelihoods with a constant gentler temperature.

The Reliability Mask protects against out-of-distribution stars (for example, very nearby clusters whose proximity-driven photometric artifacts can mimic youth).

Citation

If you use GyroNet in your research, please cite: (placeholder — paper in prep).

The training catalogue bundled in training/eda_output_clean.csv is a derived version of the rotation-period catalogue from Van-Lane et al. (2025), ChronoFlow: A Neural Spline Flow Framework for Gyrochronology. The original catalogue is MIT-licensed; this repository redistributes a version enriched with additional Gaia DR3 auxiliary columns. See training/README.md for full data attribution.

License

MIT