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

Journal Article · 16 August 2021 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stab2343 · OSTI ID:1821511

Carrick, Jonathan E.; Hook, Isobel M.; Swann, Elizabeth; Boone, Kyle; Frohmaier, Chris ; Kim, Alex G. ; Sullivan, Mark

ABSTRACT 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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Contributing Organization: The LSST Dark Energy Science Collaboration
• Grant/Contract Number: AC02-05CH11231; AC02-76SF00515
• OSTI ID: 1821511
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 508; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English