Identifying and Repairing Catastrophic Errors in Galaxy Properties Using Dimensionality Reduction
Abstract
Our understanding of galaxy evolution is derived from large surveys designed to maximize efficiency by only observing the minimum amount of information needed to infer properties for a typical galaxy. However, for a few percent of galaxies in every survey, this is insufficient and derived properties can be catastrophically wrong, including identifying a relatively nearby object as one of the most distant in the Universe. Further, it is currently impossible to determine which objects have failed, so that these contaminate every study of galaxy properties. We develop a novel method to identify and repair these objects by combining the astronomical codes which infer galaxy properties with dimensionality reduction algorithms which group similar objects to determine which inferred properties are out of place. This method provides an improvement over the best existing techniques by at least a factor of two, which will improve every study that relies on large populations.
- Publication:
-
American Astronomical Society Meeting Abstracts #235
- Pub Date:
- January 2020
- Bibcode:
- 2020AAS...23510924H