A neural network-based methodology to select young stellar object
  candidates from IR surveys
  Observed Young Stellar Objects (YSOs) are used to study star formation and
characterize star forming regions. For this purpose, YSO candidate catalogs are
compiled from various surveys, especially in the infrared (IR), and simple
selection schemes in colour-magnitude diagrams (CMDs) are often used to
identify and classify YSOs. We propose a methodology for YSO classification
through Machine Learning (ML) using Spitzer IR data. We detail our approach in
order to ensure reproducibility and provide an in-depth example on how to
efficiently apply ML to an astrophysical classification. We used feedforward
Artificial Neural Networks (ANNs) that use the four IRAC bands ($3.6, 4.5, 5.8$
and $8 \mu m$) and the $24\ \mu m$ MIPS band from Spitzer to classify point
source objects into CI and CII YSO candidates or as contaminants. We found that
ANNs can efficiently be applied to YSO classification with a contained number
of neurons ($\sim$ 25). Knowledge gathered on one star-forming region has shown
to be partly efficient for prediction in new regions. Best generalization
capacity was achieved using a combination of several star-forming regions to
train the network. Carefully rebalancing the training proportions was necessary
to achieve good results. We achieved above 90% and 97% recovery rate for CI and
CII YSOs, respectively, with precision above 80% and 90% for our most general
result. We took advantage of the ANN great flexibility to define, for each
object, an effective membership probability to each output class. Using a
threshold in this probability was found to efficiently improve the
classification results at a reasonable cost of object exclusion. With this
selection, we reached 90% precision on CI YSOs, for more than half of them. Our
catalog of YSO candidates in Orion (365 CI, 2381 CII) and NGC 2264 (101 CI, 469
CII) predicted by our final ANN is publicly available at CDS.
