Closing the Observational Gap in Cosmic Dynamics: AI-Enabled Reconstruction of the Universe's Vorticity and Rotational Flow Morphology
Ziyong Wu, Xu Xiao, Fuyu Dong, Juhan Kim, Yan-Chuan Cai, Yang Wang, Xi Kang, Le Zhang, Xin Wang, Xiao-Dong Li

TL;DR
This paper demonstrates an AI-based method to reconstruct the cosmic vorticity and flow morphology from galaxy survey data, revealing structures consistent with cosmological models and addressing observational challenges.
Contribution
It introduces an AI framework trained on simulations to empirically recover the universe's vorticity field from galaxy observations, bridging a long-standing observational gap.
Findings
Reconstructed vorticity fields show coherent vortical structures.
Power spectra match LambdaCDM predictions.
Velocity field reduces redshift-space distortions, improving clustering analysis.
Abstract
The cosmic vorticity field, an essential tracer of nonlinear structure formation, has remained observationally inaccessible because transverse galaxy motions are difficult to measure and analytic models struggle to capture shell-crossing. Here we report an empirical reconstruction of this field by applying an artificial intelligence framework trained on simulations of the concordance LambdaCDM model to Sloan Digital Sky Survey galaxies. The recovered three-dimensional velocity and vorticity fields reveal coherent vortical structures, including spiral flows in clusters, filaments, and voids, and the cosmic web inferred from vorticity closely matches that derived from density segmentation. The power spectra of the reconstructed velocity and vorticity fields agree statistically with LambdaCDM predictions, and the inferred velocity field effectively removes redshift-space distortions,…
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