Identification of single spectral lines in large spectroscopic surveys
  using UMLAUT: an Unsupervised Machine Learning Algorithm based on Unbiased
  Topology
  The identification of an emission line is unambiguous when multiple spectral
features are clearly visible in the same spectrum. However, in many cases, only
one line is detected, making it difficult to correctly determine the redshift.
We developed a freely available unsupervised machine-learning algorithm based
on unbiased topology (UMLAUT) that can be used in a very wide variety of
contexts, including the identification of single emission lines. To this
purpose, the algorithm combines different sources of information, such as the
apparent magnitude, size and color of the emitting source, and the equivalent
width and wavelength of the detected line. In each specific case, the algorithm
automatically identifies the most relevant ones (i.e., those able to minimize
the dispersion associated with the output parameter). The outputs can be easily
integrated into different algorithms, allowing us to combine supervised and
unsupervised techniques and increasing the overall accuracy. We tested our
software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents
one of the closest existing analogs to the near-IR spectroscopic surveys that
are going to be performed by the future Euclid and Roman missions. These
missions will investigate the large-scale structure of the universe by
surveying a large portion of the extragalactic sky in near-IR slitless
spectroscopy, detecting a relevant fraction of single emission lines. In our
tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The
accuracy is slightly higher (84.4%) when combining our unsupervised approach
with a supervised approach we previously developed.
