Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae

@article{Carrick2021OptimizingAM,
  title={Optimizing a magnitude-limited spectroscopic training sample for photometric classification of supernovae},
  author={Jonathan E. Carrick and Isobel M. Hook and E. Swann and Kyle Boone and Chris Frohmaier and A. G. Kim and M. Sullivan},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2021}
}
In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-m Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching rAB ≈ 22.5 mag. We run our simulations with the software snmachine, a photometric classification pipeline using machine learning. The… 
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