论文标题
用于天文源检测和排除的部分属性实例分段
Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending
论文作者
论文摘要
天文源排列是将单个恒星或星系（源）分离到由多个，可能重叠源组成的图像的贡献的过程。天文来源显示出各种大小和亮度，并且可能在图像中显示出很大的重叠。天文成像数据可以进一步挑战现成的计算机视觉算法，这是由于其高动态范围，低信噪比和非常规图像格式。这些挑战使来源融合了一个天文学研究的开放区域，在这项工作中，我们引入了一种称为部分属性实例分段的新方法，该方法可以以可用于深度学习模型的方式进行源检测和融合。我们提供了一种新颖的神经网络实施，以证明该方法。
Astronomical source deblending is the process of separating the contribution of individual stars or galaxies (sources) to an image comprised of multiple, possibly overlapping sources. Astronomical sources display a wide range of sizes and brightnesses and may show substantial overlap in images. Astronomical imaging data can further challenge off-the-shelf computer vision algorithms owing to its high dynamic range, low signal-to-noise ratio, and unconventional image format. These challenges make source deblending an open area of astronomical research, and in this work, we introduce a new approach called Partial-Attribution Instance Segmentation that enables source detection and deblending in a manner tractable for deep learning models. We provide a novel neural network implementation as a demonstration of the method.
