Towards Probabilistic Cataloging with BLISS: the Bayesian Light Source Separator

          @misc{ scivideos_PIRSA:26060034,
            doi = {10.48660/26060034},
            url = {https://pirsa.org/26060034},
            author = {Avestruz, Camille},
            keywords = {Cosmology},
            language = {en},
            title = {Towards Probabilistic Cataloging with BLISS: the Bayesian Light Source Separator},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {jun},
            note = {PIRSA:26060034 see, \url{https://scivideos.org/pirsa/26060034}}
          }
          

Abstract

Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As a result, the majority of imaged galaxies will visually overlap, a phenomenon known as blending. Blending is expected to be a leading source of systematic error in astronomical measurements. We present the Bayesian Light Source Separator (BLISS), a framework for probabilistic detection, deblending, and measurement. We demonstrate the potential of this method with numerical experiments using synthetic observations where truth is known. We highlight experiments that show (i) robustness to spatially varying backgrounds and point spread functions, (ii) how propagating the probabilistic detections to per-object flux posteriors substantially improves aperture flux residuals, and (iii) how our method retains accurate and well-calibrated posterior approximations for shear estimation under increasingly complex observational systematics. BLISS is a scalable, uncertainty-aware tool for mitigating blending-induced systematics in next-generation cosmological surveys.