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tokens: and a global alignment to an absolute reference frame, here defined by the Gaia DR2 catalogue (Gaia Collaboration 2018 ). As there are no telescope offsets between the paired F 814 W direct and G800L grism exposures, the alignment of the former is applied directly to the latter. Finally, we combine the F 814 W direct exposures into a rectified mosaic with astrodrizzle at a final sampling of arcsec pix −1 and generate a source catalogue and segmentation map from this mosaic using the sep software package. 9 The global contamination model for a given field is constructed in two passes. In the first, we assume that each object has a spectrum flat in units of f λ flux density with a normalization set by its observed flux in the F 814 W direct image (integrated over the object’s segmentation polygon). In the second pass, we step through objects in the catalogue sorted by increasing magnitude and compute a third-order polynomial fit to each spectrum after subtracting the contamination model from any neighbours (i.e. either the flat spectra of fainter sources or the polynomial models of brighter sources). This refinement is iterated three times. In both passes, the spatial template of the dispersed 2D model spectra is taken from the observed F 814 W cut-out within an object’s segmentation polygon. Note that this approach neglects morphological variation across the G800L bandpass; e.g. the PSF, continuum colour gradients, and morphological differences between line- and continuum-emitting regions. However, it provides a spatial model with much higher fidelity than any simple parametrized representation (e.g. Gaussian or Sérsic approximations). The above process automatically yields a source and contamination model for each galaxy detected in the reference image. These can be used to ‘decontaminate’ source spectra – i.e. remove overlapping light from neighbouring sources – to provide more accurate line and continuum characterizations. This was critical in GLASS’ crowded cluster G102 and G141 pointings, but the ACS parallels are sparse enough that source contamination is often negligible (Section 3 ). Note that no spectra are modelled or extracted for objects not detected in the reference frame. As such, the catalogue presented here is truncated at a F 814 W flux of m 814 = 26 with various subsamples defined by brighter flux limits (Section 3 ). There are two main differences between grizli and the previous GLASS pipeline. First, grizli automatically handles HST grism spectra taken at one or more telescope roll angles that translate into different spectral dispersion PAs. This is implemented in that the 2D spectral models are computed in the pixel space of each separate grism exposure. This is the space in which the field-dependent grism dispersion configuration is measured and defined (e.g. Kümmel et al. 2009 ). This approach allows for evaluating model fits in the space of the original detector pixels with a well-calibrated noise model. Recent works