Galaxy Classification with Neural Networks in SDSS
Abstract
The acquisition of astronomical data is growing on par with the sophistication of observing equipment, with the size of astronomical datasets rapidly outpacing the ability of astronomers to analyze them. In order to keep up with this expanding amount of data, astronomers must increasingly turn to more powerful and more sophisticated computational techniques. In the past few years, this has come to include the use of newer machine learning techniques, such as Convolutional Neural Networks (CNNs), to aid in such analyses. We approach the problem of big data in regards to galaxy classification with such techniques, using image data from SDSS in concert with classification labels from the GalaxyZoo project to train our model and differentiate between galaxy types.
- Publication:
-
American Astronomical Society Meeting Abstracts #233
- Pub Date:
- January 2019
- Bibcode:
- 2019AAS...23324516C