Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
arXiv:2605.21483v1 Announce Type: cross Abstract: Precise measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect - a probe of the large-scale distribution of baryonic matter, a key observable for cosmological inference - requires accurate reconstruction of galaxy velocities from spectroscopic surveys. The signal-to-noise ratio (SNR) of kSZ measurements scales directly with the correlation coefficient $r$ between reconstructed and true velocities. We introduce Velocityformer, an equivariant graph transformer architecture designed to match the specific symmetry of the observational data. W
The continuous advancements in AI, specifically graph transformers, are enabling more sophisticated data processing for complex scientific applications like cosmological reconstruction.
Accurate cosmological velocity reconstruction offers crucial insights into the large-scale structure of the universe, vital for advancing physics and astronomy beyond current models.
This development introduces a more precise method for analyzing astronomical data, potentially leading to a higher signal-to-noise ratio in measurements like the kinematic Sunyaev-Zel'dovich effect.
- · Astrophysicists
- · AI researchers
- · Space agencies
- · Scientific instrument manufacturers
- · Traditional data analysis methods
- · Less precise cosmological models
Improved accuracy in mapping the distribution and movement of baryonic matter across the cosmos.
New observational constraints on cosmological models, potentially refining our understanding of dark matter and dark energy.
The application of similar AI architectures to other complex scientific data challenges, accelerating discovery in fields beyond astronomy.
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Read at arXiv cs.LG