Constraining Single-Field Inflation with MegaMapper

Abstract

We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order $\sigma (f_{\mathrm{NL}}^{\mathrm{eq}})\approx 23$, $\sigma (f_{\mathrm{NL}}^{\mathrm{orth}})\approx 12$ in a joint power-spectrum and bispectrum analysis, varying both nuisance parameters and cosmology, including a conservative range of scales. Fixing cosmological parameters and quadratic bias parameter relations, the limits tighten significantly to $\sigma (f_{\mathrm{NL}}^{\mathrm{eq}})\approx 17$, $\sigma (f_{\mathrm{NL}}^{\mathrm{orth}})\approx 8$. These compare favorably with the forecasted bounds from CMB-S4: $\sigma (f_{\mathrm{NL}}^{\mathrm{eq}})\approx 21$, $\sigma (f_{\mathrm{NL}}^{\mathrm{orth}})\approx 9$, with a combined constraint of $\sigma (f_{\mathrm{NL}}^{\mathrm{eq}})\approx 14$, $\sigma (f_{\mathrm{NL}}^{\mathrm{orth}})\approx 7$; this weakens only slightly if one instead combines with data from the Simons Observatory. We additionally perform a range of Fisher analyses for the error, forecasting the dependence on nuisance parameter marginalization, scale cuts, and survey strategy. Lack of knowledge of bias and counterterm parameters is found to significantly limit the information content; this could be ameliorated by tight simulation-based priors on the nuisance parameters. The error-bars decrease significantly as the number of observed galaxies and survey depth is increased: as expected, deep dense surveys are the most constraining, though it will be difficult to reach $\sigma (f_{\mathrm{NL}})\approx 1$ with current methods. The NLPNG constraints will tighten further with improved theoretical models (incorporating higher-loop corrections and improved understanding of nuisance parameters), as well as the inclusion of additional higher-order statistics.

arXiv comment6 pages, 3 figures, accepted by Phys. Lett. B

References in corpus (17)

• Planck 2018 results. X. Constraints on inflation
• The Simons Observatory: Science goals and forecasts
• DBI in the Sky
• Large-Scale Galaxy Bias
• Redshift-Space Distortions, Pairwise Velocities and Nonlinearities
• The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure
• Signatures of Supersymmetry from the Early Universe
• Non-linear perturbation theory extension of the Boltzmann code CLASS
• Scale-dependent bias induced by local non-Gaussianity: A comparison to N-body simulations
• Primordial non-Gaussianity, scale-dependent bias, and the bispectrum of galaxies
• Constraining Primordial non-Gaussianity with Bispectrum and Power Spectum from Upcoming Optical and Radio Surveys
• Measuring neutrino masses with large-scale structure: Euclid forecast with controlled theoretical error
• Constraints on Multi-Field Inflation from the BOSS Galaxy Survey
• Constraints on Single-Field Inflation from the BOSS Galaxy Survey
• Perturbation theory approach to predict the covariance matrices of the galaxy power spectrum and bispectrum in redshift space
• Primordial Features from Linear to Nonlinear Scales
• Gravitation And the Universe from large Scale-Structures: The GAUSS mission concept

Cited by in corpus (13)

• The Open Effective Field Theory of Inflation
• Observational constraints on early dark energy
• Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias
• Decoherence out of fire: Purity loss in expanding and contracting universes
• Large Scale Limit of the Observed Galaxy Power Spectrum
• Light Fields during Inflation from BOSS and Future Galaxy Surveys
• Neutrino masses from large-scale structures: future sensitivity and theory dependence
• Improving constraints on primordial non-Gaussianity using neural network based reconstruction
• Bispectrum constraints on Primordial non-Gaussianities with the eBOSS DR16 quasars
• Non-Gaussian assembly bias from a semi-analytic galaxy formation model
• Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos
• Direct correlation of line intensity mapping and CMB lensing from evolution along the lightcone
• Enhancing Bispectrum Estimators for Galaxy Redshift Surveys with Velocities