Back to Volume
Paper: Machine-Assisted Discovery Through Identification and
Explanation of Anomalies in Astronomical Surveys
Page: 183
Authors: Wagstaff, K. L.; Huff, E.; Rebbapragada, U.
Abstract: Data volumes in modern astronomical surveys are large, and human attention is comparatively scarce. The most interesting sources are rare and may therefore go permanently buried and unknown in large archives. Many science goals from planned sky surveys (e.g., Roman, SPHEREx, and Euclid) require exquisitely precise measurements taken over billions of galaxies and stars. Existing validation techniques appear unlikely to scale to the next generation of large sky surveys. We propose the use of machine learning to identify, group, and explain anomalies within very large data sets. The goal is to quickly distinguish erroneous measurements and expected patterns in the data from sources and statistical correlations with true astrophysical origins. We illustrate the process of identifying and explaining anomalies in a study conducted on sources observed by the Dark Energy Survey. We found that 96% of automatically identified outliers in a subset of 11M sources were likewise discarded by humans. In addition, several unusual objects led to follow-up spectral observations with the Palomar Observatory. We hypothesize that this discovery process, when applied to other large-scale sky survey data sets, can result in improved science yield and catalog validation.
Back to Volume