PAU巡天：用多窄带数据进行星-星系分类
The PAU Survey: star-galaxy classification with multi narrow-band data
摘要：
恒星和星系的分类是一个众所周知的天文学问题，已经使用不同的方法处理，其中大多数依赖于形态信息。在本文中，我们解决了这个问题，使用窄谱测光的低分辨率光谱，由PAUS（加速宇宙物理学）调查提供。我们发现，从40个窄带滤波器的光度通量和不包括形态信息，它是可能的，以非常高的精度，98.4%的纯度与98.8%的完整性的对象比I = 22.5分离恒星和星系。这种精度是用卷积神经网络作为分类算法，应用于物体的光谱。我们还将该方法应用于ALHAMBRA光度测量，并为其黄金样品提供了更新的分类。
英文摘要：
Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.
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。
