Telescopes don’t make catalogues, they make intensity measurements; any precise experiment performed with a telescope ought to involve modelling those measurements. People make catalogues, but because a catalogue requires hard decisions about calibration and detection, no catalogue can contain all of the information in the raw pixels relevant to most scientific investigations. Here we advocate making catalogue-like data outputs that permit investigators to test hypotheses with almost the power of the original image pixels. The key is to provide users approximations to likelihood tests against the raw image pixels. We advocate three options, in order of increasing difficulty: The first is to define catalogue entries and associated uncertainties such that the catalogue contains the parameters of an approximate description of the image-level likelihood function. The second is to produce a K-catalogue sampling in “catalogue space” that samples a posterior probability distribution of catalogues given the data. The third is to expose a web service or equivalent that can compute the full image-level likelihood for any user-supplied catalogue.