AI 中文总结
研究利用多模态模型，结合光学宽带成像与SPHEREx近红外分光光度法，通过对比学习分离恒星和星系。经实验，变换表示训练的分类器效果更好，能将恒星污染控制在百分之一以下，凸显多模态方法对现代星系调查的实用价值。
AI 中文摘要
恒星污染是当前和下一代调查中进行高精度大规模结构分析的关键系统误差。针对局部原初非高斯性（$\sigma(f_{\rm NL}^{\rm loc}) \sim \mathcal{O}(1)$）的实验要求恒星污染率低于百分之一，以避免将银河系结构引起的虚假大尺度功率误识别为真实的宇宙学信号。在这项工作中，我们探索使用多模态模型进行恒星-星系分离，利用光学宽带成像数据和SPHEREx近红外低分辨率分光光度法的信息。通过对比学习将两种模态整合，将基于图像和光谱的嵌入投影到共享潜在空间。我们发现，在这些变换后的表示上训练的分类器优于在原始嵌入上训练的分类器，并且在使用更简单的分类器时性能下降更少。这些结果表明，多模态对齐沿着更适合源分类的维度组织嵌入空间。对于基于图像的情况，这种改进尤为显著，我们将其与变换后的图像嵌入中高分辨红外光谱特征的可预测性增加联系起来。应用基于红移误差的选择并外推到整个SPHEREx足迹，我们证明在大多数河外天空中，恒星污染可以控制在百分之一以下，完整性的权衡主要局限于低红移。我们的工作突出了多模态方法对现代星系调查（如SPHEREx和鲁宾LSST）的实用性。
英文摘要
Stellar contamination is a critical systematic for increasingly precise large-scale structure analyses from ongoing and next-generation surveys. Experiments targeting constraints on local primordial non-Gaussianity with $σ(f_{\rm NL}^{\rm loc}) \sim \mathcal{O}(1)$ demand sub-percent stellar contamination rates to avoid misidentifying spurious large-scale power induced by Galactic structure as true cosmological signal. In this work, we explore the use of multimodal models for star--galaxy separation, harnessing the information from both optical broad-band imaging data and SPHEREx near-infrared low-resolution spectrophotometry. The two modalities are integrated using contrastive learning, which projects image- and spectrum-based embeddings into a shared latent space. We find that classifiers trained on these transformed representations outperform those trained on the original embeddings and show less performance degradation when simpler classifiers are used. These results suggest that multimodal alignment organizes the embedding space along dimensions that are better suited to source classification. The improvement is particularly strong for image-based classification, which we connect to increased predictability of highly-discriminative infrared spectral features from the transformed image embeddings. Applying redshift error-based selections and extrapolating to the full SPHEREx footprint, we demonstrate that stellar contamination can be controlled at the sub-percent level across most of the extragalactic sky, with completeness tradeoffs largely confined to low redshift. Our work highlights the utility of multimodal methods for modern galaxy surveys such as SPHEREx and $\textit{Rubin}$ LSST.
Comments22 pages, 14 figures
