Testing and combining transient spectral classification tools on 4MOST-like blended spectra

Journal article

With the 4-metre Multi-Object Spectroscopic Telescope (4MOST) expected to provide an influx of transient spectra when it begins observations in early 2026 we consider the potential for real-time classification of these spectra. We investigate three extant spectroscopic transient classifiers: the Deep Automated Supernova and Host classifier (dash), Next Generation SuperFit (ngsf), and SuperNova IDentification (snid), with a focus on comparing the completeness and purity of the transient samples they produce. We manually simulate fibre losses critical for accurately determining host contamination and use the 4MOST Exposure Time Calculator to produce realistic, 4MOST-like, host-galaxy contaminated spectra. We investigate the three classifiers individually and in all possible combinations. We find that a combination of dash and ngsf can produce a supernova (SN) Ia sample with a purity of 99.9 per cent, while successfully classifying 70 per cent of SNe Ia. However, it struggles to classify non-SN Ia transients. We investigate photometric cuts to transient magnitude and the transient’s fraction of total fibre flux, finding that both can be used to improve non-SN Ia transient classification completeness by 8–44 per cent with SNe Ibc benefitting the most and superluminous (SL) SNe the least. Finally, we present an example classification plan for live classification and the predicted purities and completeness across five transient classes: Ia, Ibc, II, SL, and non-SN transients. We find that it is possible to classify 75 per cent of input spectra with 70 per cent purity in all classes except non-SN transients. Precise values can be varied using different classifiers and photometric cuts to suit the needs of a given study.

Authors: Milligan, A; Hook, I; Frohmaier, C; Smith, M; Dimitriadis, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Monthly Notices of the Royal Astronomical Society
• Volume: 543
• Issue: 1
• Pages: 247-272
• Publication date: 2025-08-28
• Acceptance date: 2025-08-20
• DOI: 10.1093/mnras/staf1419
• EISSN: 1365-2966
• ISSN: 0035-8711
• Language: English
• Keywords: software: machine learning; instrumentation: spectrographs; software: simulations; techniques: spectroscopic; transients: supernovae