Prof. David Alonso

Abstract:

<jats:title>ABSTRACT</jats:title> <jats:p>A key challenge in the search for primordial B-modes is the presence of polarized Galactic foregrounds, especially thermal dust emission. Power-spectrum-based analysis methods generally assume the foregrounds to be Gaussian random fields when constructing a likelihood and computing the covariance matrix. In this paper, we investigate how non-Gaussianity in the dust field instead affects CMB and foreground parameter inference in the context of inflationary B-mode searches, capturing this effect via modifications to the dust power-spectrum covariance matrix. For upcoming experiments such as the Simons Observatory, we find no dependence of the tensor-to-scalar ratio uncertainty $\sigma (r)$ on the degree of dust non-Gaussianity or the nature of the dust covariance matrix. We provide an explanation of this result, noting that when frequency decorrelation is negligible, dust in mid-frequency channels is cleaned using high-frequency data in a way that is independent of the spatial statistics of dust. We show that our results hold also for non-zero levels of frequency decorrelation that are compatible with existing data. We find, however, that neglecting the impact of dust non-Gaussianity in the covariance matrix can lead to inaccuracies in goodness-of-fit metrics. Care must thus be taken when using such metrics to test B-mode spectra and models, although we show that any such problems can be mitigated by using only cleaned spectrum combinations when computing goodness-of-fit statistics.</jats:p>

Abstract:

We study the angular clustering of Quaia, a Gaia- and unWISE-based catalog of over a million quasars with an exceptionally well-defined selection function. With it, we derive cosmology constraints from the amplitude and growth of structure across cosmic time. We divide the sample into two redshift bins, centered at z = 1.0 and z = 2.1, and measure both overdensity auto-correlations and cross-correlations with maps of the Cosmic Microwave Background convergence measured by Planck. From these data, and including a prior from measurements of the baryon acoustic oscillations scale, we place constraints on the amplitude of the matter power spectrum σ8 = 0.766 ± 0.034, and on the matter density parameter Ωm = 0.343+0.017−0.019. These measurements are in reasonable agreement with Planck at the ∼ 1.4σ level, and are found to be robust with respect to observational and theoretical uncertainties. We find that our slightly lower value of σ8 is driven by the higher-redshift sample, which favours a low amplitude of matter fluctuations. We present plausible arguments showing that this could be driven by contamination of the CMB lensing map by high-redshift extragalactic foregrounds, which should also affect other cross-correlations with tracers of large-scale structure beyond z ∼ 1.5. Our constraints are competitive with those from state-of-the-art 3×2-point analyses, but arise from a range of scales and redshifts that is highly complementary to those covered by cosmic shear data and most galaxy clustering samples. This, coupled with the unprecedented combination of volume and redshift precision achieved by Quaia, allows us to break the usual degeneracy between Ωm and σ8.

Abstract:

Covering ∼ 5600 deg2 to rms sensitivities of ∼70−100 μJy beam−1, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency (∼150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies within the survey. We discuss systematics in the data and an improved methodology for generating random catalogues, compared to that used for LoTSS-DR1, before presenting the angular clustering for ∼900 000 sources ≥1.5 mJy and a peak signal-to-noise ≥ 7.5 across ∼80 per cent of the observed area. Using the clustering, we infer the bias assuming two evolutionary models. When fitting angular scales of 0.5 ≤ θ < 5◦, using a linear bias model, we find LoTSS-DR2 sources are biased tracers of the underlying matter, with a bias of bC = 2.14+0.22 −0.20 (assuming constant bias) and bE(z = 0) = 1.79+0.15 −0.14 (for an evolving model, inversely proportional to the growth factor), corresponding to bE = 2.81+0.24 −0.22 at the median redshift of our sample, assuming the LoTSS Deep Fields redshift distribution is representative of our data. This reduces to bC = 2.02+0.17 −0.16 and bE(z = 0) = 1.67+0.12 −0.12 when allowing preferential redshift distributions from the Deep Fields to model our data. Whilst the clustering amplitude is slightly lower than LoTSS-DR1 (≥2 mJy), our study benefits from larger samples and improved redshift estimates.