We construct an antihalo void catalogue of 150 voids with radii 𝑅>10ℎ⁻¹Mpc in the Local Super-Volume (⁠<135ℎ⁻¹Mpc from the Milky Way), using posterior resimulation of initial conditions inferred by field-level inference with Bayesian Origin Reconstruction from Galaxies (BORG). We describe and make use of a new algorithm for creating a single, unified void catalogue by combining different samples from the posterior. The catalogue is complete out to 135ℎ⁻¹Mpc⁠, with void abundances matching theoretical predictions. Finally, we compute stacked density profiles of those voids which are reliably identified across posterior samples, and show that these are compatible with Λ cold dark matter expectations once environmental selection (e.g. the estimated ∼4 per cent underdensity of the Local Super-Volume) is accounted for.

Analysing next-generation cosmological data requires balancing accurate modelling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full N-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from N-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency. Posterior resimulations-using the inferred initial conditions for N-body simulations-show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate halo mass function and stacked density profiles of haloes in different mass bins. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modelling, indicating the potential for extracting additional information at smaller scales.

We present a novel approach based on Bayesian field-level inference that provides representative ΛCDM initial conditions for simulation of the Local Group (LG) of galaxies and its neighbourhood, constrained by present-day observations. We extended the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm with a multi-resolution approach, allowing us to reach the smaller scales needed to apply the constraints. Our data model simultaneously accounts for observations of mass tracers within the dark haloes of the Milky Way (MW) and M31, for their observed separation and relative velocity, and for the quiet surrounding Hubble flow, represented by the positions and velocities of 31 galaxies at distances between one and four megaparsec. Our approach delivers representative posterior samples of ΛCDM realisations that are statistically and simultaneously consistent with all of these observations, leading to significantly tighter mass constraints than found if the individual datasets are considered separately. In particular, we estimate the virial masses of the MW and M31 to be log₁₀(M_200c/M_⊙) = 12.07 ± 0.08 and 12.33 ± 0.10, respectively, their sum to be log₁₀(ΣM_200c/M_⊙) = 12.52 ± 0.07, and the enclosed mass within spheres of radius R to be log₁₀(M(R)/M_⊙) = 12.71 ± 0.06 and 12.96 ± 0.08 for R = 1 Mpc and 3 Mpc, respectively. The M31-MW orbit is nearly radial for most of our ΛCDM realisations, and most of them feature a dark matter sheet aligning approximately with the supergalactic plane, despite the surrounding density field not being used explicitly as a constraint. High-resolution, high-fidelity resimulations from initial conditions identified using the approximate simulations of our inference scheme continue to satisfy the observational constraints, demonstrating a route to future high-resolution, full-physics ΛCDM simulations of ensembles of LG look-alikes, all of which closely mirror the observed properties of the real system and its immediate environment.

We investigate the accuracy requirements for field-level inference of cluster and void masses using data from galaxy surveys. We introduce a two-step framework that takes advantage of the fact that cluster masses are determined by flows on larger scales than the clusters themselves. First, we determine the integration accuracy required to perform field-level inference of cosmic initial conditions on these large scales by fitting to late-time galaxy counts using the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm. A 20-step COLA integrator is able to accurately describe the density field surrounding the most massive clusters in the local super-volume (⁠<135ℎ⁻¹Mpc⁠), but does not by itself lead to converged virial mass estimates. Therefore, we carry out ‘posterior resimulations’, using full N-body dynamics while sampling from the inferred initial conditions, and thereby obtain estimates of masses for nearby massive clusters. We show that these are in broad agreement with existing estimates, and find that mass functions in the local super-volume are compatible with ΛCDM.

We derive the best possible bounds that can be placed on Yukawa- and chameleon-like modifications to the Newtonian gravitational potential with a cavity optomechanical quantum sensor. By modelling the effects on an oscillating source-sphere on the optomechanical system from first-principles, we derive the fundamental sensitivity with which these modifications can be detected in the absence of environmental noise. In particular, we take into account the large size of the optomechanical probe compared with the range of the fifth forces that we wish to probe and quantify the resulting screening effect when both the source and probe are spherical. Our results show that optomechanical systems in high vacuum could, in principle, further constrain the parameters of chameleon-like modifications to Newtonian gravity.