A big problem in space research is whether the same stars or galaxies are seen in different sky surveys. Telescopes today gather a ton of data about thousands or even billions of objects using various types of light. However, connecting this data from different surveys is quite difficult.

Old methods couldn’t handle the massive amounts of diverse data. This made it tough to tell when two surveys looked at the same thing, like a star or a galaxy, in the vast images covering huge parts of the sky. This caused a problem because scientists couldn’t combine measurements of the same object from different sky surveys.

Johns Hopkins University researchers have devised a new way to solve this problem. They created an intelligent computer program (algorithm) that scores pairs of observations from different sky surveys. These scores tell us how likely it is that the observations are of the same object. The program looks at where the things are, how bright they are, their colors, and other details to decide if they’re the same.

This method is super accurate and works well with massive amounts of data. It helps connect observations of faint and bright objects, even if they were captured differently. The program can sift through catalogs with billions of entries and find matches between celestial bodies. The scores also help confirm if the matches are correct.

This new way of connecting data uses the strengths of data science and knowledge about space measurements. It considers the probabilities of things like position, brightness, colors, and more while understanding the uncertainties in the observations. This opens up exciting opportunities for science because now we can reliably say when we see the same things in different surveys.

By combining data about individual stars, galaxies, and other objects, scientists can learn more about their nature, where they are, how they move, and how they change over time. This method lets us combine measurements from different types of light, like ultraviolet, optical, infrared, X-ray, gamma ray, and radio waves, giving us a better view of unique objects seen by various telescopes scanning different parts of the sky. It’s a new way to discover more about everything from changing stars to substantial black holes.

Check out the Paper and Reference Article. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 33k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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The post This Paper from Johns Hopkins Highlights Data Science’s Role in Accelerating Probabilistic Catalog Matching for Space Discoveries Across Time and Telescopes appeared first on MarkTechPost.