Year: 2023
Type: article
Abstract: When applied to the nonlinear matter distribution of the universe, neural networks have been shown to be very statistically sensitive probes of cosmological parameters, such as the linear perturbation amplitude ${\ensuremath{\sigma}}_{8}$. However, when used as a ``black box,'' neural networks are not robust to baryonic uncertainty. We propose a robust architecture for constraining primordial non-Gaussianity ${f}_{NL}$, by training a neural network to locally estimate ${\ensuremath{\sigma}}_{8}$, and correlating these local estimates with the large-scale density field. We apply our method to $N$-body simulations, and show that $\ensuremath{\sigma}({f}_{NL})$ is 3.5 times better than the constraint obtained from a standard halo-based approach. We show that our method has the same robustness property as large-scale halo bias: baryonic physics can change the normalization of the estimated ${f}_{NL}$, but cannot change whether ${f}_{NL}$ is detected.
Institutions: University of Wisconsin–Madison, Perimeter Institute
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Canadian Institute for Advanced Research, Aspen Center for Physics, Innovation, Science and Economic Development Canada, Government of Canada, Institut Périmètre de physique théorique, Ministry of Colleges and Universities, Natural Sciences and Engineering Research Council of Canada
OpenAlex ID: W4327937795