  Statistical inference of population parameters of astrophysical sources is
challenging. It requires accounting for selection effects, which stem from the
artificial separation between bright detected and dim undetected sources that
is introduced by the analysis pipeline itself. We show that these effects can
be modeled self-consistently in the context of sequential simulation-based
inference. Our approach couples source detection and catalog-based inference in
a principled framework that derives from the truncated marginal neural ratio
estimation (TMNRE) algorithm. It relies on the realization that detection can
be interpreted as prior truncation. We outline the algorithm, and show first
promising results.
