通过自编码器从多模态 $\ extit{Gaia}$ 数据中学习变源的新颖表示
Learning novel representations of variable sources from multi-modal $\textit{Gaia}$ data via autoencoders
AI总结：
提出基于变分自编码器的机器学习方法，融合 Gaia 多模态数据生成 15 维潜在表示，有效实现变源无监督分类并助力天体物理发现。
摘要：
Gaia Data Release 3 (DR3) 首次发布了数百万个变源的历元测光数据、BP/RP (XP) 低分辨率平均光谱以及监督分类结果。这一庞大的数据集为通过结合多种 Gaia 数据产品来研究它们的变源性提供了独特的机会。为准备 DR4，我们提出并评估了一种机器学习方法，能够摄取多种 Gaia 数据产品，以实现对恒星和类星体变源性的无监督分类。使用包含 400 万个 Gaia DR3 源的数据集训练了三个变分自编码器 (VAE)，这是一类专为数据压缩和生成设计的人工神经网络 (ANN)。其中一个 VAE 在 Gaia XP 低分辨率光谱上进行训练，另一个基于 Gaia G 波段星等差分布的新方法进行训练，第三个在折叠的 Gaia G 波段光变曲线上进行训练。每个 Gaia 源被压缩为 15 个数字，代表通过结合这三个模型的输出所生成的 15 维潜在空间中的坐标。正如通过对该潜在空间的监督和无监督分类分析所证明的那样，ANN 生成的学习潜在表示能有效区分 Gaia DR3 中存在的主要变源类别。结果突出了光变曲线与低分辨率光谱数据之间的强协同作用，强调了结合不同 Gaia 数据产品的益处。潜在变量的二维投影揭示了大量超密度区域，其中大多数与天体物理性质高度相关，展现了该潜在空间在天体物理发现方面的潜力。我们表明，我们新颖的潜在表示的属性使其在变源性分析任务（包括分类、聚类和异常值检测）中极具价值。
英文摘要：
Gaia Data Release 3 (DR3) published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a unique opportunity to study their variability by combining multiple Gaia data products. In preparation for DR4, we propose and evaluate a machine learning methodology capable of ingesting multiple Gaia data products to achieve an unsupervised classification of stellar and quasar variability. A dataset of 4 million Gaia DR3 sources is used to train three variational autoencoders (VAE), which are artificial neural networks (ANNs) designed for data compression and generation. One VAE is trained on Gaia XP low-resolution spectra, another on a novel approach based on the distribution of magnitude differences in the Gaia G band, and the third on folded Gaia G band light curves. Each Gaia source is compressed into 15 numbers, representing the coordinates in a 15-dimensional latent space generated by combining the outputs of these three models. The learned latent representation produced by the ANN effectively distinguishes between the main variability classes present in Gaia DR3, as demonstrated through both supervised and unsupervised classification analysis of the latent space. The results highlight a strong synergy between light curves and low-resolution spectral data, emphasising the benefits of combining the different Gaia data products. A two-dimensional projection of the latent variables reveals numerous overdensities, most of which strongly correlate with astrophysical properties, showing the potential of this latent space for astrophysical discovery. We show that the properties of our novel latent representation make it highly valuable for variability analysis tasks, including classification, clustering and outlier detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
