AI 中文总结
研究利用流形学习方法，对JWST巡天中的小红点进行无监督选择和特征描述，不依赖预定义颜色切割，通过UMAP将源置于二维地图，经光谱验证，该方法能有效识别类似LRD物体，还可用于群体发现，提供了高效框架。
AI 中文摘要
小红点（LRDs）是詹姆斯·韦布空间望远镜在高红移处发现的紧凑红色源，其物理性质和选择函数仍存在争议。我们研究了一种应用于多波段测光的无监督机器学习方法能否在不依赖预定义颜色切割的情况下识别类似LRD的物体和其他天体群体。使用UMAP（一种流形学习（降维）方法），我们将来自ASTRODEEP-JWST目录的约242,000个孤立且测量良好的源放置在二维地图上，具有相似宽带颜色、形态和测光红移的物体靠在一起。然后我们使用光谱确认的LRD来确定类似LRD的物体在该地图中的位置，将所得区域与已发表的颜色切割进行比较，并根据来自DJA的存档近红外光谱验证我们的数据驱动选择。我们发现，在没有施加颜色切割的情况下，光谱选择的LRD集中在两个定义明确的区域，追踪主要在红移上不同的群体，这种差异体现在它们的宽带颜色中。在光谱分类子集中，主要区域在约0.82的完整性下达到约0.78的纯度，与文献中的颜色切割相当或更纯净，并产生约100个额外的候选者。我们还将该方法作为群体发现的通用工具进行测试：流形在没有明确标准的情况下恢复了褐矮星和宽线AGN的位置，并分离出罕见的病态异常值。总体而言,由稀疏的高置信度光谱标签锚定的无监督流形为表征群体、在共同基础上比较选择方法以及在大型测光数据集中发现罕见物体提供了一个高效、假设少的框架。
英文摘要
Little Red Dots (LRDs) are compact, red sources discovered at high redshift by JWST whose physical nature and selection function remain debated. We investigate whether an unsupervised machine-learning approach applied to multi-band photometry can identify LRD-like objects, and other populations, without relying on predefined colour cuts. Using UMAP, a manifold-learning (dimensionality-reduction) method, we place ~242,000 isolated, well-measured sources from the ASTRODEEP-JWST catalogue on a two-dimensional map, where objects with similar broadband colours, morphology, and photometric redshift lie close together. We then use spectroscopically confirmed LRDs to identify where LRD-like objects lie within this map, compare the resulting areas with published colour cuts, and validate our data-driven selection against archival NIRSpec spectra from the DJA. We find that the spectroscopically selected LRDs concentrate in two well-defined regions with no colour cut imposed, tracing populations that differ mainly in redshift, a difference imprinted in their broadband colours. The main region reaches a purity of ~0.78 at ~0.82 completeness on the spectroscopically classified subset, competitive with, or cleaner than, literature colour cuts, and yields ~100 additional candidates. We also test the method as a general tool for population discovery: the manifold recovers the locations of brown dwarfs and broad-line AGN with no explicit criterion, and isolates rare pathological outliers. Overall, unsupervised manifolds, anchored by sparse high-confidence spectroscopic labels, provide an efficient, assumption-light framework for characterising populations, comparing selection methods on a common basis, and discovering rare objects in large photometric datasets.
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