<ul data-eligibleForWebStory="true"><li>A new machine learning framework has been developed for detecting and classifying late-T and Y dwarfs, which represent the coolest and lowest-mass population of brown dwarfs.</li><li>The framework was trained using synthetic photometry from atmospheric models, creating a dataset larger than any empirical set of >T6 UCDs, enabling classification of spectral types for these ultracool dwarfs.</li><li>Validation results showed high performance on both synthetic and empirical datasets, with object classification metrics exceeding 99% accuracy and an average spectral type precision within 0.35 +/- 0.37 subtypes.</li><li>Application of the model around Pisces and the UKIDSS UDS field led to the discovery of a previously uncatalogued T8.2 candidate, showcasing the effectiveness of this model-trained approach in finding faint, late-type UCDs from photometric catalogs.</li></ul>

Atmospheric model-trained machine learning selection and classification of ultracool TY dwarfs

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