标题： 使用综合光度和光谱数据的机器学习恒星特性推断 显示英文标题

标题： Machine-learning inference of stellar properties using integrated photometric and spectroscopic data

摘要： 恒星天体物理学依赖于多种观测方式——主要是光度光变曲线和光谱数据——从中推断出恒星的基本属性。 尽管机器学习（ML）已在单个观测方式内推进了分析，但跨不同观测方式的互补信息仍大多未被充分利用。 我们提出了DESA（恒星天体物理学的双嵌入模型），这是一种新颖的多模态基础模型，它整合光变曲线和光谱数据，以学习一个统一且物理上有意义的潜在空间。 DESA首先使用混合监督/自监督方案对每个观测方式进行单独的编码器训练，然后通过DualFormer——一种专为天体物理数据设计的基于Transformer的跨模态集成模块——进行对齐。 DualFormer结合了交叉注意力和自注意力、一种新颖的双投影对齐损失，以及一种投影空间特征分解，从而生成具有物理结构的嵌入。 我们证明，DESA在各种任务中显著优于领先的单模态和自监督基线模型。 在零样本和少样本设置中，DESA的学习表示能够以高保真度恢复恒星颜色-星等图和赫罗图（$R^2 = 0.92$用于光度回归）。 在完整微调中，DESA在双星检测（AUC =$0.99$，AP =$1.00$）和恒星年龄预测（RMSE =$0.94$ Gyr）方面达到了最先进的准确率。 作为一个有说服力的例子，DESA能自然地将同步双星与年轻恒星分开，这两个群体的光变曲线几乎相同，仅从它们在UMAP空间中的嵌入位置即可实现，而无需外部运动学或光度信息。 因此，DESA为多模态、数据驱动的恒星群体分析提供了一个强大的新框架，实现了准确预测和新发现。 显示英文摘要

摘要： Stellar astrophysics relies on diverse observational modalities-primarily photometric light curves and spectroscopic data-from which fundamental stellar properties are inferred. While machine learning (ML) has advanced analysis within individual modalities, the complementary information encoded across modalities remains largely underexploited. We present DESA (Dual Embedding model for Stellar Astrophysics), a novel multi-modal foundation model that integrates light curves and spectra to learn a unified, physically meaningful latent space for stars. DESA first trains separate modality-specific encoders using a hybrid supervised/self-supervised scheme, and then aligns them through DualFormer, a transformer-based cross-modal integration module tailored for astrophysical data. DualFormer combines cross- and self-attention, a novel dual-projection alignment loss, and a projection-space eigendecomposition that yields physically structured embeddings. We demonstrate that DESA significantly outperforms leading unimodal and self-supervised baselines across a range of tasks. In zero- and few-shot settings, DESA's learned representations recover stellar color-magnitude and Hertzsprung-Russell diagrams with high fidelity ($R^2 = 0.92$ for photometric regressions). In full fine-tuning, DESA achieves state-of-the-art accuracy for binary star detection (AUC = $0.99$, AP = $1.00$) and stellar age prediction (RMSE = $0.94$ Gyr). As a compelling case, DESA naturally separates synchronized binaries from young stars, two populations with nearly identical light curves, purely from their embedded positions in UMAP space, without requiring external kinematic or luminosity information. DESA thus offers a powerful new framework for multimodal, data-driven stellar population analysis, enabling both accurate prediction and novel discovery.