Shear Mapping in Python (SMPy): Modular, Extensible, and Accessible Dark Matter Mapping

Abstract

Understanding the Universe's large-scale distribution of dark matter is a central objective in the era of precision cosmology. A key technique for the study of dark matter is weak gravitational lensing: a phenomenon where light from distant galaxies is sheared as it passes through the gravitational field of a massive object, like a galaxy cluster. This shear, which manifests as a slight (weak) distortion of shapes over thousands of galaxies, allows astrophysicists to infer the distribution of total matter, including both luminous and dark matter. Obtaining a mass distribution from a catalog of galaxy shears involves reconstructing the convergence ($\kappa$), the projected surface mass density divided by the critical surface density for the lens-source system. The Shear Mapping in Python (SMPy) package provides a standardized, well-documented, and open-source solution for creating convergence maps from weak lensing galaxy shear measurements. SMPy implements three mapping methods: the classic Kaiser-Squires inversion, aperture mass mapping, and, to our knowledge, the first publicly available implementation of the full KS+ algorithm, which corrects for missing data, field borders, and reduced shear. SMPy was initially developed to support the Superpressure Balloon-borne Imaging Telescope (SuperBIT) and has since evolved into a general-purpose tool suitable for analyzing the weak lensing data from cosmological surveys.

arXiv comment7 pages, 2 figures. Published in the Journal of Open Source Software. Code: https://github.com/GeorgeVassilakis/SMPy ; documentation: https://smpy-docs.readthedocs.io

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