Work

Unsupervised selection and characterisation of little red dots in JWST surveys with manifold learning

Year: 2026

Type: article

Abstract: Little red dots (LRDs) are compact, red sources discovered at high redshifts by JWST, and their physical nature and selection function remain a topic of debate. Here, we investigate whether an unsupervised machine-learning approach applied to multi-band photometry has the capacity to identify LRD-like objects, along with other populations, without relying on predefined colour-cuts. Using UMAP, a manifold-learning (dimensionality-reduction) method, we placed ∼242,000 isolated, well-measured sources from the ASTRODEEP-JWST catalogue on a 2D map, where objects with similar broadband colours, morphology, and photometric redshift lie close together. We then used spectroscopically confirmed LRDs to identify where LRD-like objects lie within this map. We compared the resulting areas with published colour cuts and validated our data-driven selection against archival NIRSpec spectra from the DJA. We find that the spectroscopically selected LRDs are concentrated in two well-defined regions with no colour-cut imposed, tracing populations that differ mainly in redshift; a difference that is imprinted on their broadband colours. The main region reaches a purity of ≃0.78 at ≃0.82 completeness on the spectroscopically classified subset. This result is competitive with (or cleaner than) literature colour-cuts, and yields ∼100 additional candidates. We also tested the method as a general tool for population discovery: the manifold recovers the locations of brown dwarfs and broad-line active galactic nuclei 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.

Source: Astronomy and Astrophysics

DOI: 10.1051/0004-6361/202662204