arXiv subjects

G. Cañas-Herrera

Publications and source records attributed to G. Cañas-Herrera.

128 records · Page 8Linked to original sources

arXiv2024

Bayesian inference methodology for Primordial Power Spectrum reconstructions from Large Scale Structure

We use Bayesian inference and nested sampling to develop a non-parametric method to reconstruct the primordial power spectrum $P_{\mathcal{R}}(k)$ from Large Scale Structure (LSS) data. The performance of the method is studied by applying it to simulations of the clustering of two different object catalogues, low-$z$ (ELGs) and high-$z$ (QSOs), and considering two different photometric errors. These object clusterings are derived from different templates of the primordial power spectrum motivated by models of inflation: the Standard Model power law characterized by the two parameters $A_s$ and $n_s$; a local feature template; and a global oscillatory template. Our reconstruction method involves sampling $N$ knots in the log $\{k,P_{\mathcal{R}}(k)\}$ plane. We use two statistical tests to examine the reconstructions for signs of primordial features: a global test comparing the evidences and a novel local test quantifying the power of the hypothesis test between the power law model and the marginalized probability over $N$ model. The method shows good performance in all scenarios considered. In particular, the tests show no feature detection for the SM. The method is able to detect power spectrum deviations at a level of $\approx 2\%$ for all considered features, combining either the low-$z$ or the high-$z$ redshift bins. Other scenarios with different redshift bins, photometric errors, feature amplitudes and detection levels are also discussed. In addition, we include a first application to real data from the Sloan Digital Sky Survey Luminous Red Galaxy Data Release 4 (SDSS LRG 04), finding no preference for deviations from the primordial power law. The method is flexible, model independent, and suitable for its application to existing and future LSS catalogues.

astro-ph.CO↗

arXiv2024

Euclid: The search for primordial features

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measurements from the $Euclid$ space mission will provide unique constraints on the primordial power spectrum, thanks to the redshift coverage and high-accuracy measurement of nonlinear scales, thus allowing us to investigate deviations from the standard power-law primordial power spectrum. We consider two models with primordial undamped oscillations superimposed on the matter power spectrum, one linearly spaced in $k$-space the other logarithmically spaced in $k$-space. We forecast uncertainties applying a Fisher matrix method to spectroscopic galaxy clustering, weak lensing, photometric galaxy clustering, cross correlation between photometric probes, spectroscopic galaxy clustering bispectrum, CMB temperature and $E$-mode polarization, temperature-polarization cross correlation, and CMB weak lensing. We also study a nonlinear density reconstruction method to retrieve the oscillatory signals in the primordial power spectrum. We find the following percentage relative errors in the feature amplitude with $Euclid$ primary probes for the linear (logarithmic) feature model: 21% (22%) in the pessimistic settings and 18% (18%) in the optimistic settings at 68.3% confidence level (CL) using GC$_{\rm sp}$+WL+GC$_{\rm ph}$+XC. Combining all the sources of information explored expected from $Euclid$ in combination with future SO-like CMB experiment, we forecast ${\cal A}_{\rm lin} \simeq 0.010 \pm 0.001$ at 68.3% CL and ${\cal A}_{\rm log} \simeq 0.010 \pm 0.001$ for GC$_{\rm sp}$(PS rec + BS)+WL+GC$_{\rm ph}$+XC+SO-like both for the optimistic and pessimistic settings over the frequency range $(1,\,10^{2.1})$.

astro-ph.CO↗