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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 2.

Learned likelihood functions ﹩P({\rm{feature}}| {\rm{age}})﹩ for the two retained Gaia DR3 features, predicted by the conditional density MLPs. Both features decrease with age, reflecting the transition from crowded, dusty young cluster environments to quieter older fields. The shaded region shows the learned ±1σ spread.

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