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The X‐ray‐luminous quasar GB 1428+4217 at redshift 4.72 has been observed with ASCA. The observed 0.5–10 keV flux is 3.2 Å– 10−12 erg cm−2 s−1. We report here on the intrinsic 4 − 57 keV X‐ray spectrum, which is very flat (photon index 1.29). We find no evidence for flux variability within the ASCA data set or between it and ROSAT data. We show that the overall spectral energy distribution of GB 1428+4217 is similar to that of lower redshift MeV blazars, and present models that fit the available data. The Doppler beaming factor is likely to be at least 8. We speculate on the number density of such high‐redshift blazars, which must contain rapidly formed massive black holes.

The Supernova Cosmology Project has conducted the ‘See Change’ programme, aimed at discovering and observing high-redshift (1.13 ≤ z ≤ 1.75) Type Ia supernovae (SNe Ia). We used multifilter Hubble Space Telescope (HST) observations of massive galaxy clusters with sufficient cadence to make the observed SN Ia light curves suitable for a cosmological probe of dark energy at z > 0.5. This See Change sample of SNe Ia with multi-colour light curves will be the largest to date at these redshifts. As part of the See Change programme, we obtained ground-based spectroscopy of each discovered transient and/or its host galaxy. Here, we present Very Large Telescope (VLT) spectra of See Change transient host galaxies, deriving their redshifts, and host parameters such as stellar mass and star formation rate. Of the 39 See Change transients/hosts that were observed with the VLT, we successfully determined the redshift for 26, including 15 SNe Ia at z > 0.97. We show that even in passive environments, it is possible to recover secure redshifts for the majority of SN hosts out to z = 1.5. We find that with typical exposure times of 3−4 h on an 8-m-class telescope we can recover ∼75 per cent of SN Ia redshifts in the range of 0.97 < z < 1.5. Furthermore, we show that the combination of HST photometry and VLT spectroscopy is able to provide estimates of host galaxy stellar mass that are sufficiently accurate for use in a mass-step correction in the cosmological analysis.

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-metre 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 ROC 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 2 of the 4 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 optimisation of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.

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