was first outlined in (Opitom et al., 2020). Therein, Opitom et al. (2020) analysed MUSE data from the 2016 67P apparition, and attempted to extract gas emissions from the datacubes. The observations were conducted at a heliocentric distance of 2.5 au, and no notable gas emissions were detected above the noise level present, so the spectrum is solely representative of the dust coma and solar reflectance. We leveraged this 2016 reference dust spectrum to isolate the gas emissions in the updated dust and solar continuum subtraction process, outlined below.

We fit a 2D Gaussian to the central 51 pixels of each datacube to locate the optocentre and calculate the centroid of each exposure of the comet. We used the centroid to shift the comet optocentre to the middle of the datacube, and cropped the datacube to 280×280 spaxels, which removed dead spaxels and uneven fringes from each exposure. We then interpolated the 2016 reference dust spectrum to the same sampling and wavelength of the input datacube. In order to model the underlying shape difference between the 2016 reference dust spectrum and input spectrum, we masked the 2016 reference dust spectrum and input comet spectrum to 12 regions that corresponded to regions free of gas emission (dominated by dust). This ensured that we isolated the shape of the dust continuum, without gas contamination. We then divided the masked input spectrum by the masked 2016 reference dust spectrum, which approximated the shape difference between the two. Using this masked difference spectrum, we fit an 11th-order polynomial to the 12 dust regions, which allowed us to best approximate the shape of the smooth underlying dust continuum between these dust regions after much trial and error. We used this technique to extract a final difference spectrum across the entire wavelength range. We used this spectrum to correct the shape and slope deviations of the 2016 reference dust spectrum to the input comet spectrum. Finally, we scaled the shape- and slope-corrected 2016 reference dust spectrum to the flux intensity of the input comet spectrum, and subtracted it from the comet spectrum, which resulted in the final spectrum populated by the residual gas emissions (seen in middle panel of Figure 2. This process was conducted iteratively for all spectra in a datacube, and across all datacubes across our campaign. With this technique, we created gas emission datacubes from our entire campaign. Similarly, we also created pure dust continuum datacubes from the fitted dust continua, which we then used for the computation of dust spectral slopes, discussed at the end of this section. This method robustly disentangles the various spectral contributions in the central wavelength domain, however, has limitations at the blue and red extrema of the instrument, where the strongest C2 and CN emission bands are. Despite our optimisations, we were still over- and under-subtracting the dust and solar contributions by ∼1-5%, so we pivoted to a new technique for the C2 Swan band and Red CN bands. We created new datacubes that corresponded to 5000-5300 ˚A (C2 Swan), and 9000-9300 ˚A (Red CN), 11 selected two gas-free regions on either side of the bands, fit a 1D polynomial to extract the shape and slope, subtracted the dust and solar continuum, and extracted the residual C2 and CN emissions, separately.