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Abstract:

Fast radio bursts (FRBs) are sufficiently energetic to be detectable from luminosity distances up to at least seven billion parsecs (redshift ). Probing the maximum energies and luminosities of FRBs constrains their emission mechanism and cosmological population. Here, we investigate the maximum energetics of a highly active repeater, FRB 20220912A, using 1500 h of observations. We detect 130 high-energy bursts and find a break in the burst energy distribution, with a flattening of the power-law slope at higher energy – consistent with the behaviour of another highly active repeater, FRB 20201124A. There is a roughly equal split of integrated burst energy between the low- and high-energy regimes. Furthermore, we model the rate of the highest energy bursts and find a turnover at a characteristic spectral energy density of erg Hz. This characteristic maximum energy agrees well with observations of apparently one-off FRBs, suggesting a common physical mechanism for their emission. The extreme burst energies push radiation and source models to their limit: at this burst rate a typical magnetar ( G) would deplete the energy stored in its magnetosphere in 2150 h, assuming a radio efficiency . We find that the high-energy bursts ( erg Hz) play an important role in exhausting the energy budget of the source.

Abstract:

Abstract The peculiar velocity field of the local Universe provides direct insights into its matter distribution and the underlying theory of gravity, and is essential in cosmological analyses for modelling deviations from the Hubble flow. Numerous methods have been developed to reconstruct the density and velocity fields at z ≲ 0.05, typically constrained by redshift-space galaxy positions or by direct distance tracers such as the Tully–Fisher relation, the fundamental plane, or Type Ia supernovae. We introduce a validation framework to evaluate the accuracy of these reconstructions against catalogues of direct distance tracers. Our framework assesses the goodness-of-fit of each reconstruction using Bayesian evidence, residual redshift discrepancies, velocity scaling, and the need for external bulk flows. Applying this framework to a suite of reconstructions—including those derived from the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm and from linear theory—we find that the non-linear BORG reconstruction consistently outperforms others. We highlight the utility of such a comparative approach for supernova or gravitational wave cosmological studies, where selecting an optimal peculiar velocity model is essential. Additionally, we present calibrated bulk flow curves predicted by the reconstructions and perform a density–velocity cross-correlation using a linear theory reconstruction to constrain the growth factor, yielding S8 = 0.793 ± 0.035. The result is in good agreement with both weak lensing and Planck, but is in strong disagreement with some peculiar velocity studies.

Abstract:

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (𝑁-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large 𝑁-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multistage neural spline flow-based networks to learn this mapping at redshift 𝑧 =0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution 𝑁-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from 𝑁-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors.