Accurate Baryon Acoustic Oscillations reconstruction via semi-discrete optimal transport

Abstract

Optimal transport theory has recently reemerged as a vastly resourceful field of mathematics with elegant applications across physics and computer science. Harnessing methods from geometry processing, we report on the efficient implementation for a specific problem in cosmology -- the reconstruction of the linear density field from low redshifts, in particular the recovery of the Baryonic Acoustic Oscillation (BAO) scale. We demonstrate our algorithm's accuracy by retrieving the BAO scale in noise-less cosmological simulations that are dedicated to cancel cosmic variance; we find uncertainties to be reduced by a factor of 4.3 compared with performing no reconstruction, and a factor of 3.1 compared with standard reconstruction.

arXiv commentAccepted for publication in PRL. 7 pages incl. references; 2 figures; 1 table. v1 initial submission. v2 fixed affiliation. v3 matches published version, but larger figures

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Cited by in corpus (6)

• Effective cosmic density field reconstruction with convolutional neural network
• Cosmological forecasts for future galaxy surveys with the linear point standard ruler: Toward consistent BAO analyses far from a fiducial cosmology
• Optimal Transport Reconstruction of Baryon Acoustic Oscillations
• Updated neutrino mass constraints from galaxy clustering and CMB lensing-galaxy cross-correlation measurements
• Baryon Acoustic Oscillations analyses with Density-Split Statistics
• Large-scale semi-discrete optimal transport with distributed Voronoi diagrams