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Making Euclid VIS Imaging AI-ready: A Scalable Pipeline for Morphology, Anomaly Detection, and Similarity Search

  • Authors: Jinhui Xie, Yunfei Xu, Zhen Zhang, Lang Chen, Chenzhou Cui

Jinhui Xie et al 2026 The Astrophysical Journal Supplement Series 287 .

  • Provider: AAS Journals

Caption: Figure 6.

Historical LOF–UMAP candidate selection. (a) Logarithmic object density in the same saved two-dimensional UMAP projection described in Section 3.2, with the same 1681 candidates from the original intersection rule overplotted. The projection uses nneighbors = 15, ﹩{\rm{\min }}\_{\rm{dist}}=0.1﹩, the Euclidean metric, and random_state = 42. (b) Positive LOF-score distributions for all objects and selected candidates on a logarithmic count axis. LOF used the raw 384-dimensional embeddings with k = 100 and contamination = 0.1; the secondary filter retained objects beyond the 98th percentile of centroid distance in that saved projection. The visualization documents a configuration-dependent quality-control candidate list, not a complete astrophysical anomaly census.

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