使用人工智能和真实星系图像约束星系形成模拟中的参数
Using Artificial Intelligence and real galaxy images to constrain parameters in galaxy formation simulations
摘要：
宇宙学的星系形成模拟仍然受到空间/质量分辨率的限制，并且不能从第一原理模拟一些过程，如恒星形成，这是驱动星系演化的关键。因此，他们仍然依赖于一组“有效参数”，试图捕捉规模和物理过程，不能直接解决在模拟。在这项研究中，我们表明，可以使用机器学习技术应用于真实和模拟的星系图像，通过利用天文图像的全部信息内容来区分这些参数的不同值，而不是将其压缩成一组有限的值，如大小或恒星/气体质量。在这项工作中，我们将我们的方法应用到NIHAO模拟和附近星系的HI图的THINGS和VLA-ANGST观测，以测试不同的恒星形成密度阈值$n$的能力，以再现所观察到的HI图。我们发现，观测表明需要一个高的值的$n \gtrsim 80$，厘米$^{-3}$（虽然确切的数值是模型依赖），这对星系中的暗物质分布有重要的后果。我们的研究表明，通过创新方法，可以充分利用星系图像的信息内容，并以可解释的，非参数和定量的方式比较模拟和观测。
英文摘要：
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they still rely on a set of 'effective parameters' that try to capture the scales and the physical processes that cannot be directly resolved in the simulation. In this study we show that it is possible to use Machine Learning techniques applied to real and simulated images of galaxies to discriminate between different values of these parameters by making use of the full information content of an astronomical image instead of collapsing it into a limited set of values like size, or stellar/ gas masses. In this work we apply our method to the NIHAO simulations and the THINGS and VLA-ANGST observations of HI maps in nearby galaxies to test the ability of different values of the star formation density threshold $n$ to reproduce observed HI maps. We show that observations indicate the need for a high value of $n \gtrsim 80$ ,cm$^{-3}$ (although the exact numerical value is model-dependent), which has important consequences for the dark matter distribution in galaxies. Our study shows that with innovative methods it is possible to take full advantage of the information content of galaxy images and compare simulations and observations in an interpretable, non-parametric and quantitative manner.
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。
