![]() The Galaxy and regions with missing sources have been masked out. Overdensity map of galaxies between redshift 0.1 and 0.2 within the PS1-STRM catalog. Overall, this process achieved a classification accuracy of 98.1% for galaxies (and distance estimates accurate to almost 3%), 97.8% for stars, and 96.6% for quasars. The machine learning process they employed, known as a “ feedforward neural network,” was intrinsic to helping the team accurately determine the properties of different objects and sort them based on their size and photometric redshift. “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.” They then fed these to an artificial intelligence algorithm, which sorted them into stars, galaxies, quasars, or unsure (it also derived refined estimates for the galaxies’ distances).Īs Beck described the process in a recent University of Hawaii News press release: Credit: Novel Computational ToolsĪs they describe in their study, the team began by taking publicly-available spectroscopic measurements of the 2,902,054,648 objects studied in the PS1 3pi survey, which provides them with definitive object classifications and distances. ![]() The mountain in the distance is Mauna Kea, about 130 kilometers southeast. The study was led by Robert Beck, a former cosmology postdoctoral fellow at the IfA (now a professor at Eötvös Loránd University in Hungary), and included members from both institutions, as well as Stanford Health Care’s Platform Services. Their work is described in a paper that appeared in the August 31st issue of the Monthly Notices of the Royal Astronomical Society. And now, a team of astronomers from the IfA have used this data to create the Pan-STARRS1 Source Types and Redshifts with Machine Learning (PS1-STRM), the world’s largest three-dimensional astronomical catalog. In 2018, the University of Hawaii at Manoa’s Institute for Astronomy (IfA) released the PS1 3pi survey, the world’s largest digital sky survey that spanned three-quarters of the sky and encompassed 3 billion objects. As part of the Haleakala Observatory overseen by the University of Hawaii, Pan-STARRS1 relies on a system of cameras, telescopes, and a computing facility to conduct an optical imaging survey of the sky, as well as astrometry and photometry of know objects. Atop the summit of Haleakala on the Hawaiian island of Maui sits the Panoramic Survey Telescope and Rapid Response System, or Pan-STARRS1 (PS1).
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