使用机器学习方法的星系光度样本的恒星形成率
Star Formation Rates for photometric samples of galaxies using machine learning methods
摘要：
恒星形成速率（SFR）是制约星系形成和演化理论的关键。SFR通常通过需要大量望远镜时间的光谱观测来估计。我们探索一种替代方法的基础上的光度估计的全球SFR的大样本星系，通过使用自动参数空间优化，监督机器学习模型等方法。我们证明，用这种方法，准确的多波段测光允许估计可靠的SFR。我们还研究了如何使用光度，而不是光谱红移，影响派生的全球SFRs的准确性。最后，我们提供了一个公开的目录SFR超过27万个星系提取的斯隆数字巡天数据发布7。该目录可通过Vizier设施在以下链接ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377获得。
英文摘要：
Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377.
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