21世纪天体物理学的统计和计算挑战
21st Century Statistical and Computational Challenges in Astrophysics
摘要：
现代天文学已经迅速提高了我们观察宇宙更深处的能力，获得了大量的宇宙人口样本。从这些数据集中获得天体物理学见解需要广泛的复杂统计和机器学习方法。宇宙学中长期存在的问题包括星系团的特征和从光度颜色估计星系距离。贝叶斯推断是将天文数据与非线性天体物理模型联系起来的核心，它解决了太阳物理学、星团性质和系外行星系统中的问题。无可能性的方法越来越重要。在复杂噪声背景下探测微弱信号是发现恒星周期性行为和探测爆发性引力波事件的重要手段。开放的问题涉及异方差测量误差的治疗和理解概率分布表征天体物理系统。天体统计学领域需要在研究项目的设计和分析阶段加强与统计学家的合作，并共同开发新的统计方法。他们将共同为天文种群和宇宙本身带来更多的天体物理学见解。
英文摘要：
Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and to jointly develop new statistical methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.
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