Halo detection via large-scale Bayesian inference
  We present a proof-of-concept of a novel and fully Bayesian methodology
designed to detect halos of different masses in cosmological observations
subject to noise and systematic uncertainties. Our methodology combines the
previously published Bayesian large-scale structure inference 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 halos. 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 of detection of halos 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.
