Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published November 2017 | public
Book Section - Chapter

Photometric redshift estimation: An active learning approach


A long-lasting problem in astronomy is the accurate estimation of galaxy distances based solely on the information contained in photometric filters. Due to observational selection effects, the spectroscopic (source) sample lacks coverage throughout the feature space (e.g. colors and magnitudes) compared to the photometric (target) sample; this results in a clear mismatch in terms of photometric measurement distributions. We propose a solution to this problem based on active learning, a machine learning technique where a sampling strategy enables us to select the most informative instances to build a predictive model; specifically, we use active learning following a Query by Committee approach. We show that by making wisely selected queries in the target domain, we are able to increase our predictive performance significantly. We also show how a relatively small number of queries (spectroscopic follow-up measurements) suffices to improve the performance of photometric redshift estimators significantly.

Additional Information

© 2017 IEEE. This work was partly supported by the Center for Advanced Computing and Data Systems (CACDS), and by the Texas Institute for Measurement, Evaluation, and Statistics (TIMES) at the University of Houston. We thank the IAA Cosmostatistics Initiative5 (COIN) - where our interdisciplinary research team was formed. COIN is a non-profit organization whose aim is to nourish the synergy between astrophysics, cosmology, statistics and machine learning communities. EEOI and RSS thank Bruno Quint for suggesting the query evolution visualization.

Additional details

August 19, 2023
October 18, 2023