Constraints on primordial non-Gaussianity from 800,000 photometric quasars

Abstract

We derive robust constraints on primordial non-Gaussianity (PNG) using the clustering of 800,000 photometric quasars from the Sloan Digital Sky Survey in the redshift range $0.5<z<3.5$. These measurements rely on the novel technique of {\it extended mode projection} to control the impact of spatially-varying systematics in a robust fashion, making use of blind analysis techniques. This allows the accurate measurement of quasar halo bias at the largest scales, while discarding as little as possible of the data. The standard local-type PNG parameters $f_{\mathrm{NL}}$ and $g_{\mathrm{NL}}$ both imprint a $k^{-2}$ scale-dependent effect in the bias. Constraining these individually, we obtain $-49<f_{\mathrm{NL}}<31$ and $-2.7\times 10^5<g_{\mathrm{NL}}<1.9\times 10^5$, while their joint constraints lead to $-105<f_{\mathrm{NL}}<72$ and $-4.0\times 10^5<g_{\mathrm{NL}}<4.9\times 10^5$ (all at 95% CL) . Introducing a running parameter $n_{f_{\mathrm{NL}}}$ to constrain $b(k)\propto k^{-2+n_{f_{\mathrm{NL}}}}$ and a generalised PNG amplitude ${\tilde{f}}_{\mathrm{NL}}$, we obtain $-45.5\exp (3.7n_{f_{\mathrm{NL}}})<{\tilde{f}}_{\mathrm{NL}}<34.4\exp (3.3n_{f_{\mathrm{NL}}})$ at 95% CL. These results incorporate uncertainties in the cosmological parameters, redshift distributions, shot noise, and the bias prescription used to relate the quasar clustering to the underlying dark matter. These are the strongest constraints obtained to date on PNG using a single population of large-scale structure tracers, and are already at the level of pre-{\it Planck} constraints from the cosmic microwave background. A conservative forecast for a {\it Large Synoptic Survey Telescope}-like survey incorporating mode projection yields $\sigma (f_{\mathrm{NL}})\sim 5$ -- competitive with the {\it Planck} result -- highlighting the power of upcoming large scale structure surveys to probe the initial conditions of the universe.

5 pages, 5 figures. v3: version accepted in PRL. v4: systematics templates publicly available on www.earlyuniverse.org/code, no change to paper

References in corpus (21)

• The 2.5 m Telescope of the Sloan Digital Sky Survey
• The imprints of primordial non-gaussianities on large-scale structure: scale dependent bias and abundance of virialized objects
• Report of the Dark Energy Task Force
• Clustering of High Redshift ($z\ge 2.9$) Quasars from the Sloan Digital Sky Survey
• Gravity and Large-Scale Non-local Bias
• The bispectrum of galaxies from high-redshift galaxy surveys: primordial non-Gaussianity and non-linear galaxy bias
• Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
• Clustering Analyses of 300,000 Photometrically Classified Quasars--I. Luminosity and Redshift Evolution in Quasar Bias
• Primordial non-Gaussianity: large-scale structure signature in the perturbative bias model
• CMB Constraints on Primordial non-Gaussianity from the Bispectrum (f_{NL}) and Trispectrum (g_{NL} and τ_{NL}) and a New Consistency Test of Single-Field Inflation
• Luminosity and redshift dependent quasar clustering
• Fast Estimator of Primordial Non-Gaussianity from Temperature and Polarization Anisotropies in the Cosmic Microwave Background
• Clustering Analyses of 300,000 Photometrically Classified Quasars--II. The Excess on Very Small Scales
• Signature of Primordial Non-Gaussianity on Matter Power Spectrum
• Exploiting the full potential of photometric quasar surveys: Optimal power spectra through blind mitigation of systematics
• The High Redshift Integrated Sachs-Wolfe Effect
• Scale-Dependent Non-Gaussianity as a Generalization of the Local Model
• Optimal dataset combining in f_nl constraints from large scale structure in an idealised case
• First constraints on the running of non-Gaussianity
• Impact of Uncertainties in the Cosmological Parameters on the Measurement of Primordial non-Gaussianity
• Detectability of large-scale power suppression in the galaxy distribution

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• Large-scale dark matter simulations
• Constraints on Single-Field Inflation from the BOSS Galaxy Survey
• Primordial Non-Gaussianity from Biased Tracers: Likelihood Analysis of Real-Space Power Spectrum and Bispectrum
• Quaia, the Gaia-unWISE Quasar Catalog: An All-Sky Spectroscopic Quasar Sample
• Can we actually constrain $f_{\mathrm{NL}}$ using the scale-dependent bias effect? An illustration of the impact of galaxy bias uncertainties using the BOSS DR12 galaxy power spectrum
• On the impact of galaxy bias uncertainties on primordial non-Gaussianity constraints
• Predictions for local PNG bias in the galaxy power spectrum and bispectrum and the consequences for $f_{\mathrm{NL}}$ constraints
• Scale-Dependent Galaxy Bias from Massive Particles with Spin during Inflation
• Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation
• Using Large Scale Structure to test Multifield Inflation
• Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys
• Quijote-PNG: Simulations of primordial non-Gaussianity and the information content of the matter field power spectrum and bispectrum
• Cosmological constraints from the power spectrum of eBOSS quasars
• Gravitational wave consistency relations for multifield inflation
• Hunting high and low: Disentangling primordial and late-time non-Gaussianity with cosmic densities in spheres
• Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing
• Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias
• First Constraints on Small-Scale Non-Gaussianity from UV Galaxy Luminosity Functions
• Non-local bias in the halo bispectrum with primordial non-Gaussianity
• Constraints on anisotropic primordial non-Gaussianity from intrinsic alignments of SDSS-III BOSS galaxies
• New fitting formula for cosmic non-linear density distribution
• Light Fields during Inflation from BOSS and Future Galaxy Surveys
• General relativistic corrections and non-Gaussianity in large scale structure
• Constraining primordial non-Gaussianity by combining next-generation galaxy and 21 cm intensity mapping surveys
• PNG-UNITsims: Halo clustering response to primordial non-Gaussianities as a function of mass
• Density reconstruction from biased tracers and its application to primordial non-Gaussianity
• Improving constraints on primordial non-Gaussianity using neural network based reconstruction
• Cosmology using numerical relativity
• Non-local bispectra from super cosmic variance
• Quijote-PNG: Quasi-maximum likelihood estimation of Primordial Non-Gaussianity in the non-linear halo density field
• Statistics of a single sky: constrained random fields and the imprint of Bardeen potentials on galaxy clustering
• Primordial non-Gaussianity with Angular correlation function: Integral constraint and validation for DES
• Constraining primordial and gravitational mode coupling with the position-dependent bispectrum of the large-scale structure
• Constraints on Running of Non-Gaussianity from Large Scale Structure Probes
• Validating galaxy clustering models with Fixed & Paired and Matched-ICs simulations: application to Primordial Non-Gaussianities
• Removing Interlopers From Intensity Mapping Probes Of Primordial Non-Gaussianity
• Cosmology at high redshift -- a probe of fundamental physics
• Non-Gaussian assembly bias from a semi-analytic galaxy formation model
• Lecture notes on non-Gaussianity
• Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos
• The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE
• Multi-wavelength spectroscopic probes: prospects for primordial non-Gaussianity and relativistic effects
• Systematics mitigation for catalogue-based angular power spectra
• Quijote PNG: The information content of the halo power spectrum and bispectrum
• Towards detecting Primordial non-Gaussianity in the CMB using Spherical Convolutional Neural Networks