Towards constraining warm dark matter with stellar streams through
  neural simulation-based inference
  A statistical analysis of the observed perturbations in the density of
stellar streams can in principle set stringent contraints on the mass function
of dark matter subhaloes, which in turn can be used to constrain the mass of
the dark matter particle. However, the likelihood of a stellar density with
respect to the stream and subhaloes parameters involves solving an intractable
inverse problem which rests on the integration of all possible forward
realisations implicitly defined by the simulation model. In order to infer the
subhalo abundance, previous analyses have relied on Approximate Bayesian
Computation (ABC) together with domain-motivated but handcrafted summary
statistics. Here, we introduce a likelihood-free Bayesian inference pipeline
based on Amortised Approximate Likelihood Ratios (AALR), which automatically
learns a mapping between the data and the simulator parameters and obviates the
need to handcraft a possibly insufficient summary statistic. We apply the
method to the simplified case where stellar streams are only perturbed by dark
matter subhaloes, thus neglecting baryonic substructures, and describe several
diagnostics that demonstrate the effectiveness of the new method and the
statistical quality of the learned estimator.
