{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/primordial-non-gaussianity-without-tails-how","title":"Primordial non-Gaussianity without tails -- how to measure fNL with the bulk of the density PDF","arxiv_id":"1912.06621","date":"2019-12-13","proceeding":null,"authors":["Oliver Friedrich","Cora Uhlemann","Francisco Villaescusa-Navarro","Tobias Baldauf","Marc Manera","Takahiro Nishimichi"],"abstract":"We investigate the possibility to detect primordial non-Gaussianity by analysing the bulk of the probability distribution function (PDF) of late-time cosmic density fluctuations. For this purpose we devise a new method to predict the impact of general non-Gaussian initial conditions on the late-time density PDF. At redshift $z=1$ and for a smoothing scale of 30Mpc/$h$ our predictions agree with the high-resolution Quijote N-body simulations to $\\sim 0.2\\%$ precision. This is within cosmic variance of a $\\sim 100(\\mathrm{Gpc}/h)^3$ survey volume. When restricting to this 30Mpc/$h$ smoothing scale and to mildly non-linear densities ($\\delta[30\\mathrm{Mpc}/h] \\in [-0.3, 0.4]$) and also marginalizing over potential ignorance of the amplitude of the non-linear power spectrum an analysis of the PDF for such a survey volume can still measure the amplitude of different primordial bispectrum shapes to an accuracy of \\smash{$\\Delta f_{\\mathrm{NL}}^{\\mathrm{loc}}=\\pm 7.4\\ ,\\ \\Delta f_{\\mathrm{NL}}^{\\mathrm{equi}}=\\pm 22.0\\ ,\\ \\Delta f_{\\mathrm{NL}}^{\\mathrm{ortho}}=\\pm 46.0$} . When pushing to smaller scales and assuming a joint analysis of the PDF with smoothing radii of 30Mpc/$h$ and 15Mpc/$h$ ($\\delta[15\\mathrm{Mpc}/h] \\in [-0.4, 0.5]$) this improves to \\smash{$\\Delta f_{\\mathrm{NL}}^{\\mathrm{loc}}=\\pm 3.3\\ ,\\ \\Delta f_{\\mathrm{NL}}^{\\mathrm{equi}}=\\pm 11.0\\ ,\\ \\Delta f_{\\mathrm{NL}}^{\\mathrm{ortho}}=\\pm 17.0\\ $} - even when marginalizing over the non-linear variances at both scales as two free parameters. Especially, such an analysis could simultaneously measure $f_{\\mathrm{NL}}$ and the amplitude and slope of the non-linear power spectrum. However, at 15Mpc/$h$ our predictions are only accurate to $\\lesssim 0.8\\%$ for the considered density range. We discuss how this has to be improved in order to push to these small scales and make full use of upcoming surveys with a PDF-based analysis.","url_abs":"https://arxiv.org/abs/1912.06621v3","url_pdf":"https://arxiv.org/pdf/1912.06621v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"primordial-non-gaussianity-without-tails-how","repo_url":"https://github.com/OliverFHD/CosMomentum","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}