Sacrificing information for the greater good: how to select photometric bands for optimal accuracy

TL;DR

This paper introduces a feature selection algorithm that improves the accuracy of estimating physical properties like star formation rates and redshifts from photometric data using machine learning, outperforming traditional methods.

Contribution

The study presents a novel feature selection approach combined with k-NN regression to enhance photometric property estimation, demonstrating superior accuracy over standard features and methods.

Findings

Improved estimation accuracy for sSFRs over traditional SED fitting.

More accurate photo-z estimates than SDSS's existing method.

Highlights the importance of feature selection for machine learning in astronomy.

Abstract

Large-scale surveys make huge amounts of photometric data available. Because of the sheer amount of objects, spectral data cannot be obtained for all of them. Therefore it is important to devise techniques for reliably estimating physical properties of objects from photometric information alone. These estimates are needed to automatically identify interesting objects worth a follow-up investigation as well as to produce the required data for a statistical analysis of the space covered by a survey. We argue that machine learning techniques are suitable to compute these estimates accurately and efficiently. This study promotes a feature selection algorithm, which selects the most informative magnitudes and colours for a given task of estimating physical quantities from photometric data alone. Using k nearest neighbours regression, a well-known non-parametric machine learning method, we show that using the found features significantly increases the accuracy of the estimations compared to using standard features and standard methods. We illustrate the usefulness of the approach by estimating specific star formation rates (sSFRs) and redshifts (photo-z's) using only the broad-band photometry from the Sloan Digital Sky Survey (SDSS). For estimating sSFRs, we demonstrate that our method produces better estimates than traditional spectral energy distribution (SED) fitting. For estimating photo-z's, we show that our method produces more accurate photo-z's than the method employed by SDSS. The study highlights the general importance of performing proper model selection to improve the results of machine learning systems and how feature selection can provide insights into the predictive relevance of particular input features.