Tuning target selection algorithms to improve galaxy redshift estimates

Abstract

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data.

16 pages, 9 figures, updated to match MNRAS accepted version. Minor text changes, results unchanged

References in corpus (10)

• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• The 2.5 m Telescope of the Sloan Digital Sky Survey
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
• Photometric redshift analysis in the Dark Energy Survey Science Verification data
• Anomaly detection for machine learning redshifts applied to SDSS galaxies
• Feature importance for machine learning redshifts applied to SDSS galaxies
• GAz: A Genetic Algorithm for Photometric Redshift Estimation
• Data augmentation for machine learning redshifts applied to SDSS galaxies
• Measuring photometric redshifts using galaxy images and Deep Neural Networks

Cited by in corpus (4)

• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
• Morpho-z: improving photometric redshifts with galaxy morphology
• Stacking for machine learning redshifts applied to SDSS galaxies