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Astronomical instruments make intensity measurements; any preciseastronomical experiment ought to involve modeling those measurements. Peoplemake catalogues, but because a catalogue requires hard decisions aboutcalibration and detection, no catalogue can contain all of the information inthe raw pixels relevant to most scientific investigations. Here we advocatemaking catalogue-like data outputs that permit investigators to test hypotheseswith almost the power of the original image pixels. The key is to provide userswith approximations to likelihood tests against the raw image pixels. Weadvocate three options, in order of increasing difficulty: The first is todefine catalogue entries and associated uncertainties such that the cataloguecontains the parameters of an approximate description of the image-levellikelihood function. The second is to produce a K-catalogue sampling in"catalogue space" that samples a posterior probability distribution ofcatalogues given the data. The third is to expose a web service or equivalentthat can re-compute on demand the full image-level likelihood for anyuser-supplied catalogue.

Telescopes don't make catalogues!