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 (κ), 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.
Versions
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BibTeX
@misc{vassilakis2026shear,
title = {{Shear Mapping in Python (SMPy): Modular, Extensible, and Accessible Dark Matter Mapping}},
author = {Georgios N. Vassilakis and Jacqueline E. McCleary and Maya Amit and Sayan Saha},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00225},
eprint = {2611.00225},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00225},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Shear Mapping in Python (SMPy): Modular, Extensible, and Accessible Dark Matter Mapping
AU - Georgios N. Vassilakis
AU - Jacqueline E. McCleary
AU - Maya Amit
AU - Sayan Saha
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00225
UR - https://arxiv.org/abs/2611.00225
AB - 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 (κ), 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.
ER -