April 23, 2024, 4:43 a.m. | Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.13702v1 Announce Type: cross
Abstract: The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation …

abstract alignment arxiv astro-ph.co astro-ph.ga correlation correlations cs.lg galaxy however inferences interactions intrinsic processes surveys type understanding

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