Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We apply the cross-correlation technique to infer the Hubble constant (H 0) of the Universe using gravitational-wave (GW) sources without electromagnetic counterparts (dark sirens) from the third GW Transient Catalog (GWTC-3) and the photometric galaxy surveys 2MPZ and WISE-SuperCOSMOS, and combine these with the bright siren measurement of H 0 from GW170817. The posterior on H 0 with only dark sirens is uninformative due to the small number of well-localized GW sources. Using the eight well-localized dark sirens and the binary neutron star GW170817 with electromagnetic counterpart, we obtain a value of the Hubble constant H0=75.4−6+11 km s−1 Mpc−1 (median and 68.3% equal-tailed interval) after marginalizing over the matter density and the GW bias parameters. This measurement is mostly driven by the bright siren measurement, and any constraint from dark sirens is not statistically significant. In the future, with more well-localized GW events, the constraints on expansion history will improve.

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.

Abstract:

Searches for primordial non-Gaussianity in cosmological perturbations are a key means of revealing novel primordial physics. However, robustly extracting signatures of primordial non-Gaussianity from non-linear scales of the late-time Universe is an open problem. In this paper, we apply k-Nearest Neighbour cumulative distribution functions, kNN-CDFs, to the QUIJOTE-PNG simulations to explore the sensitivity of kNN-CDFs to primordial non-Gaussianity. An interesting result is that for halo samples with Mh〈10¹⁴ M⃠ h⁻¹, the kNN-CDFs respond to equilateral PNG in a manner distinct from the other parameters. This persists in the galaxy catalogues in redshift space and can be differentiated from the impact of galaxy modelling, at least within the halo occupation distribution (HOD) framework considered here. kNN-CDFs are related to counts-in-cells and, through mapping a subset of the kNN-CDF measurements into the count-in-cells picture, we show that our results can be modelled analytically. A caveat of the analysis is that we only consider the HOD framework, including assembly bias. It will be interesting to validate these results with other techniques for modelling the galaxy–halo connection, e.g. (hybrid) effective field theory or semi-analytical methods.