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Guillermo Martínez-Somonte

Publications and source records attributed to Guillermo Martínez-Somonte.

2 recordsLinked to original sources

Primordial power spectrum reconstructions from BOSS + eBOSS

We reconstruct the primordial power spectrum $P_{\mathcal{R}}(k)$ from the BOSS DR 12 LRG and eBOSS DR 16 QSO catalogs with a non-parametric Bayesian method. The $P_{\mathcal{R}}(k)$ is reconstructed by linearly interpolating $N$ knots in the $\{ \log k, \log P_{\mathcal{R}}(k) \}$ plane. We use a parametric model to describe the galaxy power spectra of the BOSS+eBOSS catalogs, assuming any power-law deviations and BAO contributions separately from the matter power spectrum template, composed of seven parameters $Θ_{\text{model}}$. This parametric model enables us to reconstruct $P_{\mathcal{R}}(k)$ at non-linear scales, reaching $k = 0.3 \text{ h} \text{ Mpc}^{-1}$. The method is validated by applying it to different Primordial Features (PF) templates and by recovering the input power law of \textsc{MD-Patchy} and \textsc{EZmock} mock catalogs, representative of the BOSS and eBOSS data. These mocks provide additional information on $Θ_{\text{model}}$, enabling us to impose Gaussian correlated priors on $Θ_{\text{model}}$. This prior set allows us to reconstruct $P_{\mathcal{R}}(k)$ more precisely and to alleviate the degeneracies between the model and knot parameters. The results for both individual and combined $z$-bins and galactic caps of the BOSS and eBOSS catalogs are consistent, showing no evidence of the presence of PF in $P_{\mathcal{R}}(k)$ and pointing to a quasi-scale-invariant power law as the preferred model for $P_{\mathcal{R}}(k)$, as predicted by most slow-roll inflationary models. With a different prior set that relaxes the Gaussian constraints on $Θ_{\text{model}}$ and imposes Planck-based priors on the extreme knots, the results also favor the power law. From the knot reconstructions, we robustly constrain the spectral index $n_s = 0.976 \pm 0.021$, compatible with the Planck value.

astro-ph.CO↗

J-PAS: forecast on the primordial power spectrum reconstruction

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 $\{H_0,Ω_b h^2, Ω_c h^2\}$ 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\%.

astro-ph.CO↗