NASA | JPL-Caltech | 2015 Jan 08
[img3="Astronomers have turned to a method called "machine learning" to help them understand the properties of large numbers of stars. Credit: NASA/JPL-Caltech"]http://www.jpl.nasa.gov/images/universe ... 108-16.jpg[/img3]Astronomers are enlisting the help of machines to sort through thousands of stars in our galaxy and learn their sizes, compositions and other basic traits.
The research is part of the growing field of machine learning, in which computers learn from large data sets, finding patterns that humans might not otherwise see. Machine learning is in everything from media-streaming services that predict what you want to watch, to the post office, where computers automatically read handwritten addresses and direct mail to the correct zip codes.
Now astronomers are turning to machines to help them identify basic properties of stars based on sky survey images. Normally, these kinds of details require a spectrum, which is a detailed sifting of the starlight into different wavelengths. But with machine learning, computer algorithms can quickly flip through available stacks of images, identifying patterns that reveal a star's properties. The technique has the potential to gather information on billions of stars in a relatively short time and with less expense. ...
A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves - A. A. Miller et al
- Astrophysical Journal 798(2) 122 (2015 Jan 10) DOI: 10.1088/0004-637X/798/2/122
arXiv.org > astro-ph > arXiv:1411.1073 > 04 Nov 2014