Identification of single spectral lines through supervised machine
  learning in a large HST survey (WISP): a pilot study for Euclid and WFIRST
  Future surveys focusing on understanding the nature of dark energy (e.g.,
Euclid and WFIRST) will cover large fractions of the extragalactic sky in
near-IR slitless spectroscopy. These surveys will detect a large number of
galaxies that will have only one emission line in the covered spectral range.
In order to maximize the scientific return of these missions, it is imperative
that single emission lines are correctly identified. Using a supervised
machine-learning approach, we classified a sample of single emission lines
extracted from the WFC3 IR Spectroscopic Parallel survey (WISP), one of the
closest existing analogs to future slitless surveys. Our automatic software
integrates a SED fitting strategy with additional independent sources of
information. We calibrated it and tested it on a "gold" sample of securely
identified objects with multiple lines detected. The algorithm correctly
classifies real emission lines with an accuracy of 82.6%, whereas the accuracy
of the SED fitting technique alone is low (~50%) due to the limited amount of
photometric data available (<=6 bands). While not specifically designed for the
Euclid and WFIRST surveys, the algorithm represents an important precursor of
similar algorithms to be used in these future missions.
