Work

Probing primordial non-Gaussianity by reconstructing the initial conditions

Year: 2025

Type: article

Abstract: We propose to constrain the primordial (local-type) non-Gaussianity signal by first reconstructing the initial density field to remove the late time non-Gaussianities introduced by gravitational evolution. Our reconstruction algorithm combines perturbation theory on large scales with a convolutional neural network on small scales. We reconstruct the squared potential (that sources the non-Gaussian signal) out to k = 0.2 h/Mpc to an accuracy of 99.8%. We cross-correlate this squared potential field with the reconstructed density field and verify that this computationally inexpensive estimator has the same information content as the full matter bispectrum. As a proof of concept, our approach can yield up to a factor of three improvement in the f NL constraints over pre-reconstruction, although it does not yet include the complications of galaxy bias or imperfections in the reconstruction. These potential improvements make it a promising alternative to current approaches to constraining primordial non-Gaussianity.

Source: Journal of Cosmology and Astroparticle Physics

DOI: 10.1088/1475-7516/2025/08/055

Authors: Xinyi Chen, Nikhil Padmanabhan, Daniel J. Eisenstein

Institutions: Yale University, Center for Astrophysics Harvard & Smithsonian

Language: English

Topic: Cosmology and Gravitation Theories

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Nuclear Safety and Security Commission