The weirdest SDSS galaxies: results from an outlier detection algorithm

TL;DR

This paper introduces a general outlier detection algorithm based on an unsupervised Random Forest, capable of discovering diverse and unknown peculiar objects in large astronomical datasets like SDSS galaxy spectra.

Contribution

The authors develop a versatile outlier detection method that uncovers a wide range of unusual astronomical objects without prior class-specific training.

Findings

Identified galaxies with extreme emission line ratios and strong absorption lines.

Discovered gravitational lenses, double-peaked emission line galaxies, and galaxy pairs.

Detected galaxies hosting supernovae and with unusual gas kinematics.

Abstract

How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and close galaxy pairs. We find galaxies with high ionisation lines, galaxies which host supernovae, and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them peculiar. It can be executed on imaging, time-series, and other spectroscopic data, operates well with thousands of features, is not sensitive to missing values, and is easily parallelisable.