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Photometric redshift estimation: An active learning approach

Vilalta, R. and Ishida, E. E. O. and Beck, R. and Sutrisno, R. and de Souza, R. S. and Mahabal, A. (2017) Photometric redshift estimation: An active learning approach. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE , Piscataway, NJ, pp. 1-8. ISBN 978-1-5386-2727-3. http://resolver.caltech.edu/CaltechAUTHORS:20180220-071833166

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Abstract

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/SSCI.2017.8285192DOIArticle
http://ieeexplore.ieee.org/document/8285192/PublisherArticle
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.
Record Number:CaltechAUTHORS:20180220-071833166
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180220-071833166
Official Citation:R. Vilalta, E. E. O. Ishida, R. Beck, R. Sutrisno, R. S. de Souza and A. Mahabal, "Photometric redshift estimation: An active learning approach," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 1-8. doi: 10.1109/SSCI.2017.8285192
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84877
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:22 Feb 2018 03:39
Last Modified:22 Feb 2018 03:39

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