研究从图像中获得光度红移估计的深度学习方法
Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
摘要：
了解星系的红移是许多宇宙学实验的首要要求之一，由于不可能对每个被观察的星系进行光谱学分析，光度红移（photo-z）估计仍然特别感兴趣。在这里，我们研究了直接从图像中获得photo-z估计的不同深度学习方法，并将其与传统的机器学习算法进行了比较，这些算法利用了通过光度测量检索的幅度。除了测试卷积神经网络（CNN）和接收模块CNN外，我们还引入了一种新的混合输入模型，该模型允许在同一模型中使用图像和幅度数据，以进一步改善估计的红移。我们还执行基准测试，以展示不同算法的性能和可扩展性。研究中使用的数据完全来自斯隆数字巡天（SDSS），其中使用了100万个星系，每个星系都有5个过滤器（ugriz）图像，具有完整的测光和光谱红移，作为地面真理。混合输入的初始CNN实现了均方误差（MSE）=0.009，这比传统的随机森林（RF）有显著的改进（30%），并且该模型在较低的红移下表现更好，在z<0.3的范围内实现了MSE=0.0007（比RF提高了50%）。这种方法可能对即将到来的调查非常有益，例如Vera C。鲁宾天文台的遗产空间和时间调查（LSST），这将需要大量的照片z估计产生尽可能快和准确。
英文摘要：
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with traditional machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model which allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filter (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE)=0.009, which was a significant improvement (30%) over the traditional Random Forest (RF), and the model performed even better at lower redshifts achieving a MSE=0.0007 (a 50% improvement over the RF) in the range of z<0.3. This method could be hugely beneficial to upcoming surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.
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