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tokens: models and derived systematics models and residuals for both transits in Fig. 2 . 3.2 Spectroscopic light-curve analysis To construct our spectroscopic light curves, we began by binning over the narrow wavelength regions shown in blue in Fig. 1 . For each transit, we extracted 53 individual light curves using a uniform bin width of 75 Å whilst avoiding the GMOS detector gaps. We show the resulting raw light curves in the left-hand panels of Figs 3 and 4 . The raw light curves for both transits show significant systematics which are largely independent of wavelength. This is a similar situation to that encountered for the FORS2 data set in Wilson et al. ( 2020 ) and we proceeded in much the same way by dividing each of the spectroscopic light curves by the common-mode corrections derived from the white light curves. Additionally, to remove any remaining high-frequency systematics, we also subtract the residuals from the white light curves fits. The net result of this procedure is to provide significant improvements to the precision of our transmission spectrum whilst preserving the relative values of the planet-to-star radius ratios. After applying the common-mode correction described above we find the best-fitting model for each spectroscopic light curve using the same process and the same systematics model as described in Section 3.1 . The main difference is that we fix the mid-transit time to the best-fitting value from the white light-curve analysis, whilst allowing the planet-to-star radius ratio, limb darkening parameters, linear baseline parameters and kernel hyperparameters to vary for each fit. As for the white light curves, we perform a joint analysis allowing for only a single planet-to-star radius ratio and limb darkening coefficient pair for each individual wavelength channel, but allow the hyperparameters to vary separately for each transit. To add another layer of flexibility to our model, we use wide Gaussian priors with a standard deviation of 0.1 for the limb darkening coefficients, with a mean given by the best-fitting values determined using pyldtk . We again used the same differential evolution algorithm to optimize the log-likelihood as for the white light curves, and fine-tuned using a Nelder–Mead simplex algorithm. Outliers deviating more than 4 σ from the predictive distribution were removed for each fit (this procedure typically clipped 1–2 points per light curve) and we used the same MCMC method described above to marginalize our posterior distributions. The best-fitting common-mode corrected GP models for the spectroscopic light curves are shown in the middle panels of Figs 3 and 4 and we list the measured planet-to-star radius ratios and uncertainties in Table 2 . Our GMOS transmission spectrum for WASP-121b is shown in Fig. 6 and the results are discussed further in Section 5 . Table 2. Transmission spectrum for WASP-121b recovered from the GMOS low-resolution spectroscopic light curves. 3.3 Modelling