Catalogue of new Herbig Ae/Be and classical Be stars. A machine learning
  approach to Gaia DR2
  The intermediate-mass pre-main sequence Herbig Ae/Be stars are key to
understanding the differences in formation mechanisms between low- and
high-mass stars. The study of the general properties of these objects is
hampered by the fact that few and mostly serendipitously discovered sources are
known. Our goal is to identify new Herbig Ae/Be candidates to create a
homogeneous and well defined catalogue of these objects. We have applied
machine learning techniques to 4,150,983 sources with data from Gaia DR2,
2MASS, WISE, and IPHAS or VPHAS+. Several observables were chosen to identify
new Herbig Ae/Be candidates based on our current knowledge of this class, which
is characterised by infrared excesses, photometric variabilities, and H$\alpha$
emission lines. Classical techniques are not efficient for identifying new
Herbig Ae/Be stars mainly because of their similarity with classical Be stars,
with which they share many characteristics. By focusing on disentangling these
two types of objects, our algorithm has also identified new classical Be stars.
We have obtained a large catalogue of 8470 new pre-main sequence candidates and
another catalogue of 693 new classical Be candidates with a completeness of
$78.8\pm1.4\%$ and $85.5\pm1.2\%$, respectively. Of the catalogue of pre-main
sequence candidates, at least 1361 sources are potentially new Herbig Ae/Be
candidates according to their position in the Hertzsprung-Russell diagram. In
this study we present the methodology used, evaluate the quality of the
catalogues, and perform an analysis of their flaws and biases. For this
assessment, we make use of observables that have not been accounted for by the
algorithm and hence are selection-independent, such as coordinates and parallax
based distances. The catalogue of new Herbig Ae/Be stars that we present here
increases the number of known objects of the class by an order of magnitude.
