Nova | American Astronomical Society | 2016 Apr 27
In this age of large astronomical surveys, one major scientific bottleneck is the analysis of enormous data sets. Traditionally, this task requires human input — but could computers eventually take over? A pair of scientists explore this question by testing whether computers can classify galaxies as well as humans.[attachment=0]fig110.jpg[/attachment][Credit: NASA/ESA/Hubble SM4 ERO Team]
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Galaxy Zoo is an internet-based citizen science project that uses non-astronomer volunteers to classify galaxy images. This is an innovative way to provide more manpower, but it’s still only practical for limited catalog sizes. How do we handle the data from upcoming surveys like the Large Synoptic Survey Telescope (LSST), which will produce billions of galaxy images when it comes online?
In a recent study by Evan Kuminski and Lior Shamir, two computer scientists at Lawrence Technological University in Michigan, a machine learning algorithm known as Wndchrm was used to classify a dataset of Sloan Digital Sky Survey (SDSS) galaxies into ellipticals and spirals. The authors’ goal is to determine whether their algorithm can classify galaxies as accurately as the human volunteers for Galaxy Zoo. ...
A Computer-Generated Visual Morphology Catalog of ~3,000,000 SDSS Galaxies - Evan Kuminski, Lior Shamir
- Astrophysical Journal Supplement Series 223(2):20 (Apr 2016) DOI: 10.3847/0067-0049/223/2/20
arXiv.org > astro-ph > arXiv:1602.06854 > 22 Feb 2016 (v1), 27 Mar 2016 (v2)