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GyroNet: Well-calibrated Rotation-based Stellar Ages from a Machine Learning and Bayesian Framework with Gaia DR3 Features

  • Authors: Amit Dethe, Melinda Soares-Furtado

Amit Dethe and Melinda Soares-Furtado 2026 The Astrophysical Journal Supplement Series 286 .

  • Provider: AAS Journals

Caption: Figure 1.

Distribution of stars in the training sample by fiducial cluster age after quality cuts (7615 stars across 30 clusters). The majority of calibration data is concentrated below 500 Myr, with a secondary peak near 700–1000 Myr driven by Praesepe, NGC 6811, and the Hyades. Beyond 1.5 Gyr, only three clusters (NGC 6819, Ruprecht 147, and M67) provide calibration, totaling 456 stars. This sparsity at old ages is a fundamental limitation for all gyrochronology models trained on open cluster data.

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