Last month, associate Prof. Rafael S. de Souza from Shanghai Astronomical Observatory (SHAO), as the 2nd author, published an important review article on the statistical and computational challenges in astrophysics, on the “Annual Review of Statistics and Its Application” (ARSIA), one of the top review journals in Statistics in the world. The article entitled “Twenty-First-Century Statistical and Computational Challenges in Astrophysics” was written with Professor Eric D. Feigelson of Pennsylvania State University, Emille E.O. Ishida of Universite Clermont Auvergne,and Gutti Jogesh Badu of Pennsylvania State University.
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 data sets requires a wide range of sophisticated statistical and machine learning methods.
In this review, they present a non-comprehensive selection of issues important to the current understanding of cosmic phenomena where progress seems impossible without sophisticated statistical analysis.
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.
“In some cases, astrostatisticians have had considerable success with established methods. In other cases, new developments are underway or the problems need creative ideas.” Prof. Rafael said. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and joint development of new statistical methodologies. This collaboration will yield more astrophysical insights into astronomical populations and the cosmos itself.”
Prof. Rafael’s contribution to ARSIArounds out our best understanding to date on the use of neural networks for galaxy deblending, photometric redshift estimate of biases samples, and Bayesian models for inference of stellar cluster properties.
Prof. Rafael is the chair of the Cosmostatistics Initiative, a worldwide endeavor aimed to foster interdisciplinary collaborations to solve data-driven scientific challenges. He is also the chief editor of “Elements Series in Astrostatistics”, Prose Awards winner with this book "Bayesian Models for Astrophysical Data" by Cambridge University press, and the vice president of the International Astrostatistics Association. He obtained his Ph.D. in astrophysics from the University of Sao Paulo in 2009. Later he moved to Japan, South Korea, Hungary, and the USA to continue his research. In 2020, he joined the research group in Shanghai Astronomical Observatory, Chinese Academy of Sciences.
Science Contact: Rafael S. de Souza, Shanghai Astronomical Observatory, Chinese Academy of Science, email@example.com
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