Big data astronomy: Using statistics in a new way to decipher the universe

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The digital age has been a huge boon to the fields of statistics and astronomy. However, according to Dr. Max Bonamente, a professor of physics and astronomy at the University of Alabama at Huntsville (UAH), most astronomers are not trained enough to realize the substantial benefits that can be gained by bringing these disciplines together. He and his colleagues are working to change all that through pioneering research in the burgeoning field of astrostatistics.

Dr. Bonamente has published an article on Monthly Notices of the Royal Astronomical Society showing an innovative new twist in probability distributions that promises to revolutionize the way cosmological data can be interpreted.

“Traditionally, astronomers have been poor statisticians: we like to ‘make statistics as we go’,” explains the researcher. “My latest paper is a new method for accounting for systematic errors. It describes a new probability distribution method I developed that I hadn’t thought of before. It’s nerdy stuff, but it has real-life implications in terms of drawing conclusions from observations. Many astronomers don’t have the mathematical background necessary to do statistics carefully. It’s difficult, because statistics is hard mathematics at its core. Few people want to take the extra time to do it. Of course, not everyone thinks so.”

This is evidenced by the success of a workshop called iid2022: Statistical Methods for Event Data and subtitled, Illuminating the Dynamic Universe, recently hosted by UAH, a part of the University of Alabama system. Dr. Bonamente and his colleague Dr. Lingling Zhao, an assistant professor of space science, organized the seminar.

The meeting was designed to train young scientists in proper statistical methods for data analysis and interpretation, and included hands-on collaborative analysis of sample problems using advanced software. The meeting also provided a forum for astronomers and researchers in related fields to exchange recent advances in event data analysis.

“Event data” is the collection of single events in astronomy, typically light photons, but also neutrinos or other particles. These events can be studied through statistical applications as a function of position (images), time (such as light curves) or energy or wavelength (spectra). Events can also be defined as sets of quantities, such as gravitational wave events or galaxy clusters detected through measurements of the cosmic microwave background, which is the cooled remnant of the first light that could ever travel freely through the universe.

Born in Italy, Dr. Bonamente moved to the United States in 1997 and is a UAH alumnus, earning both an MS and a PhD. in physics at UAH, where he developed the use of a statistical method called Markov chain Monte Carlo (MCMC) for the analysis of cosmological events. MCMCs comprise a class of special algorithms used in probability distributions, a mathematical function that provides the probabilities of the occurrence of several possible outcomes for an experiment.

‘These methods allowed the data to be analyzed faster and more accurately,’ notes the researcher. “Machine learning is everywhere in astronomy these days. We used MCMCs to measure the Hubble constant, for example, which was a big deal at the time.” The Hubble constant is one of the most important numbers in cosmology, because it tells us how fast the universe is expanding.

Astrostatistics represents the future of big data management and analysis in astronomy, as the latest technologies are producing staggering amounts of data of truly staggering complexity. The challenge to analyze this data is growing exponentially as new data collection mechanisms evolve in radio, microwave, infrared, X-ray, gamma ray, interferometer and optical instruments which will require new statistical algorithms and techniques to make sense of it. all that.

‘Most astronomers or physicists don’t know much about probability theory, let alone statistics,’ emphasizes Dr. good. “A scientist’s job should be to be careful and not give in to the desire to find a great new result when it’s not there. So marrying mathematics and astronomy is the natural direction for me.”

More information:
Massimiliano Bonamente, Systematic errors in maximum likelihood regression of Poisson count data: introduction of the overdisperse 2 distribution, Monthly Notices of the Royal Astronomical Society (2023). DOI: 10.1093/mnras/stad463

About the magazine:
Monthly Notices of the Royal Astronomical Society

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