Cosmology

Abstract:

The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.

Abstract:

We apply a suite of different estimators to the Quijote-png halo catalogs to find the best approach to constrain Primordial non-Gaussianity (PNG) at nonlinear cosmological scales, up to kmax = 0.5 h Mpc⁻¹. The set of summary statistics considered in our analysis includes the power spectrum, bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum. Marked statistics are used here for the first time in the context of the PNG study. We perform a Fisher analysis to estimate their cosmological information content, showing substantial improvements when marked observables are added to the analysis. Starting from these summaries, we train deep neural networks to perform likelihood-free inference of cosmological and PNG parameters. We assess the performance of different subsets of summary statistics; in the case of fNL^equil, we find that a combination of the power spectrum and a suitable marked power spectrum outperforms the combination of power spectrum and bispectrum, the baseline statistics usually employed in PNG analysis. A minimal pipeline to analyze the statistics we identified can be implemented either with our ML algorithm or via more traditional estimators, if these are deemed more reliable.