Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey

TL;DR

This study uses machine learning to identify anomalous sources in the WISE survey and confirms their nature through spectroscopic follow-up, revealing diverse rare objects such as dust-rich galaxies and unusual quasars.

Contribution

It demonstrates the effectiveness of one-class SVMs in detecting rare and peculiar astronomical sources in infrared survey data.

Findings

Identified hot dust galaxies and Wolf-Rayet galaxies among anomalies.

Detected broad-line QSOs, including rare low-ionisation BAL quasars.

Found that the algorithm effectively detects rare objects among bright sources.

Abstract

We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey (SDSS). Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright ($g_{AB}$<19.5) objects marked as 'anomalous' by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: i) low redshift (z~0.03-0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources.