Hierarchical Bayesian CMB Component Separation with the No-U-Turn
  Sampler
  Key to any cosmic microwave background (CMB) analysis is the separation of
the CMB from foreground contaminants. In this paper we present a novel
implementation of Bayesian CMB component separation. We sample from the full
posterior distribution using the No-U-Turn Sampler (NUTS), a gradient based
sampling algorithm. Alongside this, we introduce new foreground modelling
approaches. We use the mean-shift algorithm to define regions on the sky,
clustering according to naively estimated foreground spectral parameters. Over
these regions we adopt a complete pooling model, where we assume constant
spectral parameters, and a hierarchical model, where we model individual
spectral parameters as being drawn from underlying hyper-distributions. We
validate the algorithm against simulations of the LiteBIRD and C-BASS
experiments, with an input tensor-to-scalar ratio of $r=5\times 10^{-3}$.
Considering multipoles $32\leq\ell\leq 121$, we are able to recover estimates
for $r$. With LiteBIRD only observations, and using the complete pooling model,
we recover $r=(10\pm 0.6)\times 10^{-3}$. For C-BASS and LiteBIRD observations
we find $r=(7.0\pm 0.6)\times 10^{-3}$ using the complete pooling model, and
$r=(5.0\pm 0.4)\times 10^{-3}$ using the hierarchical model. By adopting the
hierarchical model we are able to eliminate biases in our cosmological
parameter estimation, and obtain lower uncertainties due to the smaller
Galactic emission mask that can be adopted for power spectrum estimation.
Measured by the rate of effective sample generation, NUTS offers performance
improvements of $\sim10^3$ over using Metropolis-Hastings to fit the complete
pooling model. The efficiency of NUTS allows us to fit the more sophisticated
hierarchical foreground model, that would likely be intractable with
non-gradient based sampling algorithms.
