We present a proof-of-concept of a novel and fully Bayesian methodology designed to detect
haloes of different masses in cosmological observations subject to noise and systematic uncertainties.
Our methodology combines the previously published Bayesian large-scale structure
inference algorithm, HAmiltonian Density Estimation and Sampling algorithm (HADES), and
a Bayesian chain rule (the Blackwell–Rao estimator), which we use to connect the inferred
density field to the properties of dark matter haloes. To demonstrate the capability of our
approach, we construct a realistic galaxy mock catalogue emulating the wide-area 6-degree
Field Galaxy Survey, which has a median redshift of approximately 0.05. Application of HADES
to the catalogue provides us with accurately inferred three-dimensional density fields and corresponding
quantification of uncertainties inherent to any cosmological observation. We then
use a cosmological simulation to relate the amplitude of the density field to the probability of
detecting a halo with mass above a specified threshold. With this information, we can sum over
the HADES density field realisations to construct maps of detection probabilities and demonstrate
the validity of this approach within our mock scenario. We find that the probability of
successful detection of haloes in the mock catalogue increases as a function of the signal to
noise of the local galaxy observations. Our proposed methodology can easily be extended to
account for more complex scientific questions and is a promising novel tool to analyse the
cosmic large-scale structure in observations.
Key words: methods: numerical – methods: statistical – galaxies: haloes – galaxies: clusters