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

The LSST Dark Energy Science Collaboration, Elizabeth Swann, Chris Frohmaier

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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 machine-learning algorithms struggle to classify supernovae when the training sample is magnitude limited, in contrast to representative training samples. Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint high-redshift supernovae observed from larger spectroscopic facilities; the algorithms’ range of average area under receiver operator characteristic curve (AUC) scores over 10 runs increases from 0.547–0.628 to 0.946–0.969 and purity of the classified sample reaches 95 per cent in all runs for two of the four algorithms. By creating new, artificial light curves using the augmentation software AVOCADO, we achieve a purity in our classified sample of 95 per cent in all 10 runs performed for all machine-learning algorithms considered. We also reach a highest average AUC score of 0.986 with the artificial neural network algorithm. Having ‘true’ faint supernovae to complement our magnitude-limited sample is a crucial requirement in optimization of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Early online date16 Aug 2021
Publication statusPublished - 1 Nov 2021


  • cosmology: observations
  • methods: data analysis
  • supernovae: general
  • UKRI
  • STFC
  • ST/R000514/1
  • ST/P00038X/1


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