论文标题
使用深度学习预测星系的恒星形成特性
Predicting star formation properties of galaxies using deep learning
论文作者
论文摘要
了解星系的恒星形成特性是宇宙时期的函数，这是对星系进化研究的关键练习。传统上，恒星种群合成模型已被用来获得表征星系中恒星形成的最佳拟合参数。随着数千个星系的多频通量测量值可用，使用机器学习表征恒星形成的另一种方法变得可行。在这项工作中，我们介绍了深度学习技术来预测三种重要的恒星形成特性 - 恒星质量，恒星形成速率和灰尘光度。我们通过与标准恒星种群综合代码的输出进行比较来表征深度学习模型的性能。
Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters that characterise star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties -- stellar mass, star formation rate and dust luminosity. We characterise the performance of our deep learning models through comparisons with outputs from a standard stellar population synthesis code.
