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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 3.

Held-out ridge-regression probes of four catalog quantities from the fixed DINOv2 embedding. Hexagonal bins show test-object density on a logarithmic scale; the dashed line is the one-to-one relation. To prevent a small number of extreme catalog values from compressing the visible density, each panel displays the joint 0.1–99.9 percentile range of observed and predicted values; all reported metrics retain the complete held-out sample. Feature standardization and RidgeCV selection use the 80% training partition only. Insets report the held-out sample size, R2, and Pearson r; complete MAE, RMSE, and Spearman ρ values are given in Table 2.

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