Benchmarking AstroCLIP for galaxy property estimation: reproduction, robustness, and embedding-space analysis

Large imaging and spectroscopic surveys now produce heterogeneous data at a scale that challenges supervised approaches which depend on scarce labels and task-specific retraining. In this paper, we conduct a systematic evaluation and analysis of AstroCLIP, a cross-modal self-supervised model that aligns galaxy images and optical spectra within a shared embedding space. Our overarching aim is to extend the released benchmark with a more fine-grained assessment of robustness and embedding behaviour. Using the released DESI and DESI Legacy Imaging Survey evaluation suite, we first reproduce the main downstream galaxy-property regression results and then extend the evaluation in two novel ways: (i) by stratifying predictive performance across a neighbour-count proxy for local environment density, and (ii) by comparing the suite’s observational categories labelled Low-z (Bright) and High-z (Faint). We further inspect the embedding space using UMAP and unsupervised clustering, and quantify cluster–property agreement using the adjusted mutual information (AMI). Across tasks, spectral embeddings consistently outperform image embeddings; for example, zero-shot prediction reaches R² = 0.87 for log(M_∗) and R₂ = 0.63 for log(sSFR). Under our environment proxy, moderate-density bins often yield the strongest predictive performance, while very sparse or crowded bins tend to underperform. Image-based predictions benefit substantially from the Low-z (Bright) subset, whereas spectral embeddings are more stable across the observational split. At the same time, UMAP and clustering reveal only weak discrete separation by individual physical properties, so the results are most consistent with useful information being encoded in a largely continuous rather than sharply clustered form.