论文标题
通过有条件扩散模型从光度法产生天文光谱
Generating astronomical spectra from photometry with conditional diffusion models
论文作者
论文摘要
速度和信息之间的权衡控制我们对天文对象的理解。快速相对的光度观测提供了全球性能，同时又昂贵且耗时的光谱测量值可以更好地了解其演变的物理学。在这里，我们通过直接从光度法产生光谱来解决这个问题，通过该光谱，我们可以从易于获得的图像中获得它们复杂性的估计。这是通过使用多模式条件扩散模型来完成的，其中选择生成光谱中的最佳选择是通过对比度网络选择的。对最少处理的SDSS星系数据的初步实验显示出令人鼓舞的结果。
A trade-off between speed and information controls our understanding of astronomical objects. Fast-to-acquire photometric observations provide global properties, while costly and time-consuming spectroscopic measurements enable a better understanding of the physics governing their evolution. Here, we tackle this problem by generating spectra directly from photometry, through which we obtain an estimate of their intricacies from easily acquired images. This is done by using multi-modal conditional diffusion models, where the best out of the generated spectra is selected with a contrastive network. Initial experiments on minimally processed SDSS galaxy data show promising results.
