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 considers the task of estimating the specific star formation rate (sSFR) of galaxies. It is shown that a nearest neighbours algorithm can produce better sSFR estimates than traditional SED fitting. We show that we can obtain accurate estimates of the sSFR even at high redshifts using only broad-band photometry based on the u, g, r, i and z filters from Sloan Digital Sky Survey (SDSS). We addtionally demonstrate that combining magnitudes estimated with different methods from the same photometry can lead to a further improvement in accuracy. 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. Furthermore, the use of massively parallel computation on graphics processing units (GPUs) for handling large amounts of astronomical data is advocated.
K. Stensbo-Smidt, F. Gieseke, C. Igel, et. al.
Wed, 18 Nov 15
Comments: 10 pages, 12 figures, 1 table. Submitted to MNRAS