Telescopes don't make catalogues!

TL;DR

This paper argues that astronomical data analysis should move beyond traditional catalogues by providing likelihood-based data products that allow for hypothesis testing directly against raw image pixels.

Contribution

It proposes three methods for creating catalogue-like data outputs that retain near-original image information for scientific analysis.

Findings

Proposes likelihood-based catalogue entries with uncertainties.

Introduces K-catalogue sampling to represent posterior distributions.

Suggests web services for on-demand likelihood computations.

Abstract

Astronomical instruments make intensity measurements; any precise astronomical experiment ought to involve modeling 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 with 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 re-compute on demand the full image-level likelihood for any user-supplied catalogue.