Multiband reconstruction of 16 gravitational lens systems using PISCO data

ABSTRACT Next-generation surveys such as the Euclid survey, the Legacy Survey of Space and Time (LSST), and the China Space Station Telescope (CSST) survey are expected to discover $\sim \!10^5$ galaxy–galaxy scale strong gravitational lenses. This motivates the development of scalable and robust lens modelling approaches that can efficiently and reliably learn from wide-field survey data sets before high-resolution follow-up. We design a scalable, Bayesian, Lenstronomy-based pipeline and apply it to a sample of 16 lens candidates observed with the Parallel Imager for Southern Cosmology Observations (PISCO) on the Magellan telescope. PISCO provides four-band imaging (z, i, r, g) with colours, depth, and seeing conditions comparable to LSST. To fully exploit the constraining power of this data set, our pipeline performs simultaneous multiband modelling, using a common mass profile across all four bands while allowing independent light profiles in each. This approach leverages colour information to provide joint constraints on the lens mass and yields reduced uncertainties compared to single-band analyses. Fifteen out of 16 PISCO lens candidates are successfully recovered with interpretable lensing configurations, including DESJ0533–2536, the first reported hyperbolic-umbilic galaxy–galaxy scale strong lensing candidate. We further assess how much model complexity can be reliably constrained given the resolution and seeing of PISCO-like data. Overall, our results demonstrate that scalable, multiband lens modelling of ground-based data can extract meaningful constraints on mass and source morphology, providing a practical pathway to maximize the scientific return from large samples in upcoming surveys.