A group of astronomers on the University of Hawaiʻi at Mānoa Institute for Astronomy (IfA) has produced the world’s largest three-dimensional astronomical imaging catalog of stars, galaxies and quasars. The group used knowledge from UH’s Panoramic Survey Telescope and Rapid Response System or Pan-STARRS1 (PS1) on Haleakalā. The PS1 3π survey is the world’s largest deep multi-color optical survey, spanning three-quarters of the sky. IfA astronomers utilized novel computational instruments to the catalog, to decipher which of the three billion objects are stars, galaxies or quasars. For the galaxies, the software program additionally derived estimates of their distances.
The ensuing 3-D catalog is now accessible as a high-level science product through the Mikulski Archive for Space Telescopes. It is roughly 300 GB in measurement, and science customers can question the catalog by means of the MAST CasJobs SQL interface, or obtain your complete assortment as a computer-readable desk.
Creating a 3-D catalog
Astronomers took publicly accessible spectroscopic measurements that present definitive object classifications and distances, and fed them to an artificial intelligence algorithm. The AI course of was key in serving to the group work out the way to precisely decide the identical properties from numerous measures of the colours and sizes of the objects. This AI or Machine Learning method with a “feedforward neural network” achieved an general classification accuracy of 98.1% for galaxies, 97.8% for stars and 96.6% for quasars. Galaxy distance estimates are correct to virtually 3%.
Lead research creator Robert Beck, a former cosmology postdoctoral fellow at IfA, described the method. “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.”
Previously, the most important map of the universe was created by the Sloan Digital Sky Survey (SDSS), which covers solely one-third of the sky. The new catalog doubles the world surveyed, has larger statistics, and accommodates particular areas the SDSS missed.
IfA astronomer and co-author on 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.”
“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,” defined Pan-STARRS Director and IfA Associate Astronomer, Ken Chambers. “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.”
Szapudi et al., Pan-STARRS1 Source Types and Redshifts with Machine Learning (PS1-STRM). (2020). DOI: 10.17909/t9-rnk7-gr88
University of Hawaii at Manoa
Astronomers produce largest 3-D catalog of galaxies (2020, October 9)
retrieved 10 October 2020
This doc is topic to copyright. Apart from any truthful dealing for the aim of personal research or analysis, no
half could also be reproduced with out the written permission. The content material is offered for data functions solely.