1-6001 ˚A, and 6016-6026 ˚A, and CN peaks at 9138.16-9147.7 ˚A, 9165.18-9174.7 ˚A, and 9185.1-9194.76 ˚A. Similarly, we summed the 5577.339 ˚A [OI] line from 5574.9-5581.2 ˚A, 6300.304 ˚A deviations from the summed radial profile that are caused by underlying dust features.

[OI] line from 6296.5-6305.8 ˚A, and the 6363.776 ˚A [OI] line from 6361.2-6365.7 ˚A. Finally, for the dust maps, we summed 7080-7120 ˚A, from the original datacube. We chose all of these wavelengths, seen as highlighted regions in the bottom panels of Figure 2, because they were not contaminated by sky lines and they offered the best SNR for the resultant maps. After we assembled all the molecular maps, we compiled them into nightly sequences, comprised of four 10-minute exposures per stack, one stack per night, on average. The night of 27 September 2021 consisted of six 10-minute exposures, instead of four. We rejected maps if they did not have the comet in the FoV, contained significant star trails through the inner coma, or exhibited major instrumental defects. Details regarding the quantity of rejections, and numbers per stack per night, can be found in Table 1. Our dust and molecular species maps yielded strong detections, however, we sought to probe the underlying structure in each of these components to study morphology, sublimation, and evolutionary processes. We accomplished this by applying division by azimuthal median (cf. Samarasinha and Larson, 2014). This technique removed the diffuse contribution of the coma by incrementally calculating the mean of an annulus and dividing it from itself in a series of concentric annuli, which left behind the more elusive substructure. We call this process azimuthal enhancement. We generated azimuthal enhanced Cartesian images, unenhanced Polar images, and enhanced Polar images. We performed analyses on each set of images, which consisted of 12 images each and amounted to 180 final science images. For dust features identified in the enhanced images, we initially measured position angles of the underlying substructure from our Polar images, where the x-axis is the azimuthal angle and y-axis is the radial extent. We then summed the first several arcseconds of radial extent to achieve a higher SNR, better profile of the dust coma, and retrieved measurements with the least amount of curvature. Finally, we fit Voigt (Gaussian+Lorentzian) models to our summed profiles, and extracted the angular location of the modelled maxima. This technique is applicable for near-Gaussian profiles, however, we often observed dust structure edges could not be modelled with a Voigt fit. Considering this, we utilised two additional techniques that approximated the position angles for these structures: (1) search for changes in slope by taking the gradient of the profile, and (2) find local deviations from the profile with continuous wavelet transform maps (CWT). Traditionally, CWT is used in the time and frequency domain, however, we applied it in the space and scale domain. This allowed us to measure the position angle (space) of any 13

We ensure that these deviations are not instrumental artefacts by varying the scale of the CWT from 1-40, where 1 represents ultra-fine features (likely instrumental defects and noise) and 50 represents large scale structures. We interpret that a structure is real if it is present throughout the scale range we probed, and has consistent position angles across those scales (see Figure C.14 in Appendix C). We coupled both techniques to test Voigt measurements and derive additional position angles of fan edges and faint non-Gaussian fans in the post-perihelion regime, discussed further on in this section.