标题： 大气模型训练的机器学习选择和分类超冷 TY矮星 显示英文标题

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

摘要： T和Y光谱类型代表了最冷和质量最低的棕矮星种群，但由于超冷矮星（UCDs）在后续类型的观测样本稀少，其普查仍然不完整。现有的检测框架通常受限于识别M、L和早期T型矮星，这是由于后续类型超冷矮星的观测样本稀少。本文提出了一种新的机器学习框架，能够检测和分类晚期T型和Y型矮星，该框架完全基于大气模型的合成测光数据进行训练。利用ATMO 2020和Sonora Bobcat模型的网格，我生成了一个比任何经验集合的>T6 UCD大两个数量级的训练数据集。拟合到模型测光的多项式颜色关系用于为这些合成模型分配光谱类型，进而训练一组分类器来识别和分类晚期UCDs的光谱类型。该模型在合成和经验数据集上的验证中表现出色，对已知UCD目录的验证对象分类指标超过99%，平均光谱类型精度在0.35 +/- 0.37子类型以内。将该模型应用于双鱼座周围1.5度区域和UKIDSS UDS场，发现了1个之前未收录的T8.2候选体，证明了这种模型训练方法从测光目录中发现暗弱、晚期类型UCDs的能力。 显示英文摘要

摘要： The T and Y spectral classes represent the coolest and lowest-mass population of brown dwarfs, yet their census remains incomplete due to limited statistics. Existing detection frameworks are often constrained to identifying M, L, and early T dwarfs, owing to the sparse observational sample of ultracool dwarfs (UCDs) at later types. This paper presents a novel machine learning framework capable of detecting and classifying late-T and Y dwarfs, trained entirely on synthetic photometry from atmospheric models. Utilizing grids from the ATMO 2020 and Sonora Bobcat models, I produce a training dataset over two orders of magnitude larger than any empirical set of >T6 UCDs. Polynomial color relations fitted to the model photometry are used to assign spectral types to these synthetic models, which in turn train an ensemble of classifiers to identify and classify the spectral type of late UCDs. The model is highly performant when validating on both synthetic and empirical datasets, verifying catalogs of known UCDs with object classification metrics >99% and an average spectral type precision within 0.35 +/- 0.37 subtypes. Application of the model to a 1.5 degree region around Pisces and the UKIDSS UDS field results in the discovery of one previously uncatalogued T8.2 candidate, demonstrating the ability of this model-trained approach in discovering faint, late-type UCDs from photometric catalogs.