This article was originally published on the National Center for Supercomputing Applications website. In 2007, the Sloan Digital Sky Survey (SDSS) launched a citizen science campaign called Galaxy Zoo to enlist the public's help in classifying the hundreds of thousands of galaxy images captured by an optical telescope. Through this highly successful crowdsourcing effort, volunteers reviewed the images online to help determine whether each galaxy had a spiral or elliptical structure. Leveraging data generated by the Galaxy Zoo project, a team of scientists is now applying the power of artificial intelligence (AI) and high-performance supercomputers to accelerate efforts to analyze the increasingly massive datasets produced by ongoing and future cosmological surveys. In a new study, researchers from the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign and the Argonne Leadership Computing Facility (ALCF) at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a novel combination of deep learning methods to provide a highly accurate approach to classifying hundreds of millions of unlabeled galaxies.
Large scale astronomical surveys continue to increase their depth and scale, providing new opportunities to observe large numbers of celestial objects with ever increasing precision. At the same time, the sheer scale of ongoing and future surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include the citizen science approach adopted by the Sloan Digital Sky Survey (SDSS). These SDSS datasets have been used recently to train neural network models to classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of both surveys. While this represents a significant step to classify unlabeled images of astrophysical objects in DES, the key issue at heart still remains, i.e., the classification of unlabelled DES galaxies that have not been observed in previous surveys. To start addressing this timely and pressing matter, we demonstrate that knowledge from deep learning algorithms trained with real-object images can be transferred to classify elliptical and spiral galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art accuracy 99.6%. More importantly, to initiate the characterization of unlabelled DES galaxies that have not been observed in previous surveys, we demonstrate that our neural network model can also be used for unsupervised clustering, grouping together unlabeled DES galaxies into spiral and elliptical types. We showcase the application of this novel approach by classifying over ten thousand unlabelled DES galaxies into spiral and elliptical classes. We conclude by showing that unsupervised clustering can be combined with recursive training to start creating large-scale DES galaxy catalogs in preparation for the Large Synoptic Survey Telescope era.
A seemingly nondescript galaxy is actually a behemoth cobbled together out of various cosmic spare parts, Frankenstein-style, a new study suggests. UGC 1382, which lies about 250 million light-years from Earth, had long been regarded as an old, small and ordinary elliptical galaxy. But new observations show that it's actually a spiral, 718,000 light-years wide -- seven times larger than Earth's own Milky Way. "The center of UGC 1382 is actually younger than the spiral disk surrounding it," study co-author Mark Seibert, of the Observatories of the Carnegie Institution for Science in Pasadena, California, said in a statement. This is like finding a tree whose inner growth rings are younger than the outer rings."
The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. High-energy physics and cosmology seem worlds apart in terms of sheer scale, but the invisible components that comprise the field of one inform the composition and dynamics of the other -- collapsing stars, star-birthing nebulae and, perhaps, dark matter. For decades, the techniques by which researchers in both fields studied their domains seemed almost incompatible, as well. High-energy physics relied on accelerators and detectors to glean some insight from the energetic interactions of particles, while cosmologists gazed through all manner of telescopes to unveil the secrets of the universe. " … it would be interesting to know if image classification techniques from machine learning that have been used successfully by Google and Facebook can simplify or shorten the development of algorithms that identify particle signatures in our 3D detectors."
Galaxies come in many shapes and sizes, and according to our current understanding, the large umbrella groups of elliptical and spiral galaxies make up the bulk of the galaxies in the known universe. And if we leave aside the so-called irregular galaxies, there are the lenticular galaxies, among which are the very unusual but not-so-rare spindle galaxies. Only a dozen of these spindle-shaped curiosities had been observed till recently, but eight more have now been added to the list, and researchers have also suggested a mechanism that could be responsible for the creation of these cigar-shaped galaxies. Properly called prolate rotators, these galaxies are likened to spindles not just for their shape but also for the fact that they spin along their lengths. A systematic study of data from the Calar Alto Legacy Integral Field Area (CALIFA) Survey -- 600 galaxies were mapped with imagine spectroscopy at the Calar Alto Observatory in Spain -- showed researchers eight new spindle galaxies, taking their total known count to 20.