Contributed talks on strong lensing science with Rubin.

Contributed talks:

• S04: Priyanka Gawade - A Deep Learning Approach to Model Strong Gravitational Lenses from Ground-based Surveys
• S18: Anupreeta More - Testing LSST DIA pipeline on HSC imaging to identify lensed transients
• S19: Anindya Ganguly - Discovering (Un)Lensed Kilonovae in Rubin-LSST: Simulations and Detection Methodology
• S30: Connor Stone - Caustics: a differentiable, GPU accelerated, gravitational lensing simulator
• S36: Vibhore Negi - Generating mock simulated lensed images for the Rubin LSST
• S46: Shenming Fu - Initial catalog query for LSST Lensed AGN
• S66: Narayan Khadka - SLSIm and LSST strong lensing development

Additional talk:

Testing Rubin Image Quality for Strong Lensing Searches (Alma Gonzalez)
We present an analysis of the impact of different image coaddition strategies on the search for strong gravitational lenses in the first Rubin Observatory data. If lens candidates are identified in a preliminary search, we examine how various coaddition selections affect their detectability. In the absence of detected candidates, we report results based on simulated (injected) lens systems. We also assess the influence of coaddition choices on key image-processing tasks, such as deblending, which are critical for identifying strong lensing features.

Go to the list of all contributed abstracts.