Foreground-background Galaxy Separations with Color Information & Weak Lensing Analysis with SuperBIT

Merging galaxy clusters serve as cosmic particle colliders, revealing how dark matter particles – which account for roughly 80% of a typical galaxy cluster’s mass – interact and disperse upon collision. Analyzing the mass distribution of these merging systems, along with other probes like x-ray and imaging data, carries a wealth of information about how dark matter interacts with baryonic matter, and can be used to constrain dark matter models. Weak gravitational lensing remains the most unbiased technique for mapping the mass distribution of such clusters as it doesn’t directly depend on any cosmological model.

In this talk, I present the weak gravitational lensing pipeline developed for the Super-pressure Balloon-borne Imaging Telescope (SuperBIT), a stratospheric telescope that imaged ~30 merging galaxy clusters in 2023. I will focus on a pixel-mask technique that I developed to separate foreground and background objects using galaxy color information from three SuperBIT bands. This selection is crucial for preserving the lensing signal strength, as contamination from unlensed objects would lead to systematically underestimated cluster masses. I will close with the outlook for SuperBIT's weak lensing analysis, the first to produce mass maps from stratospheric observations.