论文标题
COSMOS2020：在大型星系调查中估计物理参数的多种多样学习
COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys
论文作者
论文摘要
我们提出了一种新的方法，可以从光谱能量分布（SED），替代模板拟合技术以及基于自组织图（SOM）中估算星系物理特性，以学习光度星系目录的高维歧管。该方法先前已在Davidzon等人的流体动力学模拟中进行了测试。 （2019年）首次将这里应用于真实数据。它的实施至关重要的是，使用高质量的Panchrostic数据集构建SOM，我们选择为“ Cosmos2020” Galaxy Catalog。在使用COSMOS2020进行训练和校准步骤之后，可以通过SOM处理其他星系，以获得其出色的质量和恒星形成速率（SFR）的估计。两种量结果与从更扩展的光度基线得出的独立测量值吻合，并且它们的组合（即，SFR与恒星质量图）显示了与先前研究一致的星星形成星系的主要序列。我们讨论了这种方法与传统的配件相比的优势，强调了使SOM以“数据驱动”方式构建的经验SED的集合，而不是通常的合成模板。这种方法还允许，即使对于极大的数据集，也可以进行有效的视觉检查来识别光度误差或特殊的星系类型。考虑到这个新估计器的计算速度，我们认为它将在对欧几里得（Euclid）等大区域调查或Vera Cooper Rubin望远镜上的时空调查（如时空调查）中发挥重要作用。
We present a novel method to estimate galaxy physical properties from spectral energy distributions (SEDs), alternate to template fitting techniques and based on self-organizing maps (SOM) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has been previously tested with hydrodynamical simulations in Davidzon et al. (2019) while here is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high quality, panchromatic data set, which we elect to be the "COSMOS2020" galaxy catalog. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOM to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities result to be in good agreement with independent measurements derived from more extended photometric baseline, and also their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star forming galaxies consistent with previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of having, instead of the usual synthetic templates, a collection of empirical SEDs built by the SOM in a "data-driven" way. Such an approach also allows, even for extremely large data sets, an efficient visual inspection to identify photometric errors or peculiar galaxy types. Considering in addition the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys like Euclid or the Legacy Survey of Space and Time at the Vera Cooper Rubin Telescope.
