A team of astronomers from the University of Hawaiʻi at Mānoa’s Institute for Astronomy (IfA) has produced the most comprehensive astronomical imaging catalog of stars, galaxies, and quasars ever created with help from an artificially intelligent neural network.
The group of astronomers from the University of Hawaiʻi at Mānoa’s Institute for Astronomy (IfA) released a catalog containing 3 billion celestial objects in 2016, including stars, galaxies, and quasars (the active cores of supermassive black holes). Needless to say, the parsing of this extensive database—packed with 2 petabytes of data—was a task unfit for puny humans, and even grad students. A major goal coming out of the 2016 catalog release was to better characterize these distant specks of light, and to also map the arrangement of galaxies in all three dimensions. The Pan-STARRS team can now check these items off their to-do list, owing to the powers of machine learning. The results of their work have been published to the Monthly Notices of the Royal Astronomical Society.
Their PS1 telescope, located on the summit of Haleakalā on Hawaii’s Big Island, is capable of scanning 75% of the sky, and it currently hosts the world’s largest deep multicolor optical survey, according to a press release put out by the University of Hawaiʻi. By contrast, the Sloan Digital Sky Survey (SDSS) covers just 25% of the sky.
To provide the computer with a frame of reference, and to teach it how to discern celestial classes of objects from one another, the team turned to publicly available spectroscopic measurements. These measures of colors and sizes of objects numbered in the millions, as Robert