J-PAS: forecast on the primordial power spectrum reconstruction

TL;DR

This paper evaluates J-PAS's ability to reconstruct the primordial power spectrum non-parametrically, focusing on detecting oscillatory features within a specific scale range using Bayesian methods and simulated data.

Contribution

It introduces a non-parametric Bayesian approach to reconstruct the primordial power spectrum and assesses J-PAS's capability to detect features with various survey configurations.

Findings

J-PAS can detect oscillatory features as small as 2%.

Combining redshift bins and tracers enhances feature detection.

The method effectively localizes scales of primordial features.

Abstract

We investigate the capability of the J-PAS survey to constrain the primordial power spectrum using a non-parametric Bayesian method. Specifically, we analyze simulated power spectra generated by a local oscillatory primordial feature template motivated by non-standard inflation. The feature is placed within the range of scales where the signal-to-noise ratio is maximized, and we restrict the analysis to $k \in [0.02,0.2] \text{ h} \text{ Mpc}^{-1}$, set by the expected J-PAS coverage and the onset of non-linear effects. Each primordial power spectrum is reconstructed by linearly interpolating $N$ knots in the $\{\log k, \log P_{\mathcal{R}}(k)\}$ plane, which are sampled jointly with the cosmological parameters $\{H_0,\Omega_b h^2, \Omega_c h^2\}$ using PolyChord. To test the primordial features, we apply two statistical tools: the Bayes factor and a hypothesis test that localizes the scales where features are detected. We assess the recovery under different J-PAS specifications, including redshift binning, tracer type, survey area, and filter strategy. Our results show that combining redshift bins and tracers allows the detection of oscillatory features as small as 2\%.