21st Century Statistical and Computational Challenges in Astrophysics

TL;DR

The paper reviews recent statistical and computational methods in astrophysics, highlighting challenges in data analysis, Bayesian inference, and the need for interdisciplinary collaboration to enhance understanding of cosmic phenomena.

Contribution

It provides a comprehensive overview of current statistical challenges and emerging methods in astrophysics, emphasizing the importance of collaboration between statisticians and astronomers.

Findings

Bayesian inference is central to linking data with models.

Likelihood-free methods are increasingly important.

Addressing heteroscedastic errors remains an open issue.

Abstract

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.