Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used
technique that has matured significantly in the last decade. Model predictions and fitting …

We review the current state of data mining and machine learning in astronomy. Data Mining
can have a somewhat mixed connotation from the point of view of a researcher in this field. If …

Most existing star-galaxy classifiers use the reduced summary information from catalogs,
requiring careful feature extraction and selection. The latest advances in machine learning …

We present several quantum algorithms for performing nearest-neighbor learning. At the
core of our algorithms are fast and coherent quantum methods for computing distance …

We describe the simulated data sample for the Photometric Large Synoptic Survey
Telescope (LSST) Astronomical Time Series Classification Challenge (PLAsTiCC), a …

With the growth of large photometric surveys, accurately estimating photometric redshifts,
preferably as a probability density function (PDF), and fully understanding the implicit …

Wide field images taken in several photometric bands allow simultaneous measurement of
redshifts for thousands of galaxies. A variety of algorithms to make this measurement have …

The next generation of cosmology experiments will be required to use photometric redshifts
rather than spectroscopic redshifts. Obtaining accurate and well-characterized photometric …

Aims. We compute photometric redshifts in the fourth public release of the Canada-France-
Hawaii Telescope Legacy Survey. This unique multi-colour catalogue comprises $ u^*, g', r' …

We present a study of photometric redshift performance for galaxies and active galactic
nuclei detected in deep radio continuum surveys. Using two multiwavelength data sets, over …