The largest 3-D catalog of galaxies

Pan-STARRS1: Survey picture of the sky. (Credit R. White/STScI)

Previously, the universe’s largest map was created by the Sloan Digital Sky Survey (SDSS), which covers solely one-third of the sky. In a brand new research, astronomers used information from UH’s Panoramic Survey Telescope and Rapid Response System or Pan-STARRS1 (PS1) on Haleakalā to supply the world’s largest three-dimensional astronomical imaging catalog of stars. It is roughly 300 GB in dimension.

A workforce of astronomers on the University of Hawaiʻi at Mānoa Institute for Astronomy (IfA) utilized novel computational instruments to the catalog to decipher which of the three billion objects are stars, galaxies, or quasars. The ensuing 3-D catalog is now obtainable as a high-level science product through the Mikulski Archive for Space Telescopes. Science customers can question the catalog by means of the MAST CasJobs SQL interface or obtain the whole assortment as a computer-readable desk.

The PS1 3π survey is the world’s largest in-depth multi-color optical survey, spanning three-quarters of the sky.

Astronomers used publicly obtainable spectroscopic measurements and fed them to a man-made intelligence algorithm. The measurements present them definitive object classifications and distances. The algorithm helps them decide the identical properties from varied measures of the objects’ colours and sizes.

Map of density of the universe, for galaxies between 1.5 and 3 billion light years away
Map of density of the universe, for galaxies between 1.5 and three billion gentle years away.

Lead research creator Robert Beck, a former cosmology postdoctoral fellow at IfA, stated, “Utilizing a state-of-the-art optimization algorithm, we leveraged the spectroscopic training set of almost 4 million light sources to teach the neural network to predict source types and galaxy distances, while at the same time correcting for light extinction by dust in the Milky Way.”

“This AI or Machine Learning approach with a “feedforward neural network” achieved an total classification accuracy of 98.1% for galaxies, 97.8% for stars, and 96.6% for quasars. Galaxy distance estimates are correct to nearly 3%.”

IfA astronomer and co-author of the research, István Szapudi, famous that “already, a preliminary version of this catalog, covering a much smaller area, facilitated the discovery of the largest void in the universe, the possible cause of the Cold Spot. The new, more accurate, and larger photometric redshift catalog will be the starting point for many future discoveries.”

Pan-STARRS Director and IfA Associate Astronomer, Ken Chambers, stated, “This beautiful map of the universe provides one example of how the power of the Pan-STARRS big data set can be multiplied with artificial intelligence techniques and complementary observations. As Pan-STARRS collects more and more data, we will use machine learning to extract even more information about near-Earth objects, our solar system, our galaxy, and our universe.”

Journal Reference:
  1. Szapudi et al., Pan-STARRS1 Source Types and Redshifts with Machine Learning (PS1-STRM). (, 2020). DOI: 10.17909/t9-rnk7-gr88


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