Coadded astronomical images are created by stacking multiple single-exposure images. Because coadded images are smaller in terms of data size than the single-exposure images they summarize, loading and processing them is less computationally expensive. However, image coaddition introduces additional dependence among pixels, which complicates principled statistical analysis of them. We present a principled Bayesian approach for performing light source parameter inference with coadded astronomical images. Our method implicitly marginalizes over the single-exposure pixel intensities that contribute to the coadded images, giving it the computational efficiency necessary to scale to next-generation astronomical surveys. As a proof of concept, we show that our method for estimating the locations and fluxes of stars using simulated coadds outperforms a method trained on single-exposure images.
翻译:添加天文图像是用堆叠多个单接触图像生成的。 因为堆叠图像在数据大小上比它们所汇总的单接触图像小, 装入和处理这些图像的计算成本较低。 但是, 图像加在一起会增加像素的依赖性, 这使得对像素进行有原则的统计分析更加复杂。 我们提出了一个有原则的贝叶斯方法, 用堆叠的天文图像来进行光源参数推导。 我们的方法暗含地将单接触像素强度边缘化于有助于加固图像的单接触像素强度上, 从而使得它具有测量下一代天文测量所需的计算效率。 作为概念的证明, 我们展示了我们使用模拟的叠加图像来估计恒星位置和通量的方法, 超越了经过单接触图像培训的方法。