cea-01677174
https://cea.hal.science/cea-01677174
https://cea.hal.science/cea-01677174/document
https://cea.hal.science/cea-01677174/file/stx2623.pdf
doi:10.1093/mnras/stx2623
[CEA] CEA - Commissariat à l'énergie atomique
[INSU] INSU - Institut National des Sciences de l'Univers
[CNRS] CNRS - Centre national de la recherche scientifique
[IAP] Institut d'Astrophysique de Paris
[DSM-IPHT] IPHT
[CEA-UPSAY] CEA - Université Paris-Saclay
[UNIV-PARIS-SACLAY] Université Paris-Saclay
[CEA-UPSAY-SACLAY] CEA-UPSAY-SACLAY
[CEA-DRF] Direction de Recherche Fondamentale
[SORBONNE-UNIVERSITE] Sorbonne Université
[SORBONNE-UNIV] Sorbonne Université 01/01/2018
[SU-SCIENCES] Faculté des Sciences de Sorbonne Université
[TEST-HALCNRS] Collection test HAL CNRS
[SU-TI] Sorbonne Université - Texte Intégral
[ANR] ANR
[GS-MATHEMATIQUES] Graduate School Mathématiques
[GS-PHYSIQUE] Graduate School Physique
[ALLIANCE-SU] Alliance Sorbonne Université
Hunting high and low: disentangling primordial and late-time non-Gaussianity with cosmic densities in spheres
Uhlemann, C.
Pajer, E.
Pichon, C.
Nishimichi, T.
Codis, S.
Bernardeau, F.
[PHYS] Physics [physics]
[SDU] Sciences of the Universe [physics]
ART
cosmology: theory
large-scale structure of Universe
methods: analytical
numerical
Non-Gaussianities of dynamical origin are disentangled from primordial ones using the formalism of large deviation statistics with spherical collapse dynamics. This is achieved by relying on accurate analytical predictions for the one-point probability distribution function (PDF) and the two-point clustering of spherically-averaged cosmic densities (sphere bias).Sphere bias extends the idea of halo bias to intermediate density environments and voids as underdense regions. In the presence of primordial non-Gaussianity, sphere bias displays a strong scale dependence relevant for both high and low density regions, which is predicted analytically. The statistics of densities in spheres are built to model primordial non-Gaussianityvia an initial skewness with a scale-dependence that depends on the bispectrum of the underlying model. The analytical formulas with the measured nonlinear dark matter variance as iNon-Gaussianities of dynamical origin are disentangled from primordial ones using the formalism of large deviation statistics with spherical collapse dynamics. This is achieved by relying on accurate analytical predictions for the one-point probability distribution function (PDF) and the two-point clustering of spherically-averaged cosmic densities (sphere bias).Sphere bias extends the idea of halo bias to intermediate density environments and voids as underdense regions. In the presence of primordial non-Gaussianity, sphere bias displays a strong scale dependence relevant for both high and low density regions, which is predicted analytically. The statistics of densities in spheres are built to model primordial non-Gaussianity via an initial skewness with a scale-dependence that depends on the bispectrum of the underlying model. The analytical formulas with the measured nonlinear dark matter variance as input are successfully tested against numerical simulations. For local non-Gaussianity with a range from $f_{NL}$ $=$ $−$100 to $+$100 they are found to agree within 2% or better for densities $\rho$ $\in$ [0.5, 3] in spheres of radius 15 Mpc/$h$ down to $z$ = 0.35. The validity of the large deviation statistics formalism is thereby established for all observationally relevant local-type departures from perfectly Gaussian initial conditions. The corresponding estimators for the amplitude of the nonlinear variance $\sigma _8$ and primordial skewness $f_{NL}$ are validated using a fiducial joint maximum likelihood experiment. The influence of observational effects and the prospects for a future detection of primordial non-Gaussianity from joint one- and two-point densities-in-spheres statistics are discussed.
2018-03-01
2021-12-18
en
http://creativecommons.org/licenses/by/
Monthly Notices of the Royal Astronomical Society
Oxford University Press (OUP): Policy P - Oxford Open Option A