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