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The first direct detection of gravitational waves from binary neutron stars on the 17th of August, 2017, (GW170817) heralded the arrival of a new messenger for probing neutron star astrophysics and provided the first constraints on neutron star equation of state from gravitational wave observations. Significant computational effort was expended to obtain these first results and therefore, as observations of binary neutron star coalescence become more routine in the coming observing runs, there is a need to improve the analysis speed and flexibility. Here, we present a rapid approach for inferring the neutron star equation of state based on Normalising Flows. As a demonstration, using the same input data, our approach, ASTREOS, produces results consistent with those presented by the LIGO-Virgo collaboration but requires < 1 sec to generate neutron star equation of state confidence intervals. Furthermore, ASTREOS allows for non-parametric equation of state inference. This rapid analysis will not only facilitate neutron star equation of state studies but can potentially enhance future alerts for electromagnetic follow-up observations of binary neutron star mergers., Comment: 4 + 2 pages, 2 figures

We apply and extend standard tools for void statistics to cosmological simulations that solve Einstein's equations with numerical relativity (NR). We obtain a simulated void catalogue without Newtonian approximations, using a new watershed void finder which operates on fluid-based NR simulations produced with the Einstein Toolkit. We compare and contrast measures of void size and void fraction, and compare radial stacked density profiles to empirically-derived Hamaus-Sutter-Wandelt (HSW) density profiles and profiles based on distance to void boundaries. We recover statistics roughly consistent with Newtonian N-body simulations where such a comparison is meaningful. We study variation of dynamical spatial curvature and local expansion explicitly demonstrating the spatial fluctuations of these quantities in void regions. We find that voids in our simulations expand ~10-30% faster than the global average and the kinetic curvature density parameter in the centre of voids reaches ~60-80%. Within the limits of resolution of the simulations, the results are consistent with the parameters of the Timescape model of cosmological backreaction., Comment: 15 pages, 14 figures

The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. Combining the GW data with the optical counterpart, also known as AT2017gfo, classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonova brought about a revolution in the domain of multi-messenger astronomy, since there has been only one kilonova from a gravitational wave detected binary neutron star merger event so far, this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder trained on light curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once trained, the time scale for light curve generation is of the order of a few milliseconds, thus speeding up generating light curves by $1000$ times compared to the simulation. The mean squared error between the generated and original light curves is typically $0.015$ with a maximum of $0.08$ for each set of considered physical parameter; while having a maximum of $\approx0.6$ error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results., Comment: 19 pages, 7 figures (3 additional figures in appendix), accepted to ApJ

We present a novel fully Bayesian analysis to constrain short gamma-ray burst jet structures associated with cocoon, wide-angle and simple top-hat jet models, as well as the binary neutron star merger rate. These constraints are made given the distance and inclination information from GW170817, observed flux of GRB170817A, observed rate of short gamma-ray bursts detected by Swift, and the neutron star merger rate inferred from LIGO's first and second observing runs. A separate analysis is conducted where a fitted short gamma-ray burst luminosity function is included to provide further constraints. The jet structure models are further constrained using the observation of GW190425 and we find that the assumption that it produced a GRB170817-like short gamma-ray burst that went undetected due to the jet geometry is consistent with previous observations. We find and quantify evidence for low luminosity and wide-angled jet structuring in the short gamma-ray burst population, independently from afterglow observations, with log Bayes factors of $0.45{-}0.55$ for such models when compared to a classical top-hat jet. Slight evidence is found for a Gaussian jet structure model over all others when the fitted luminosity function is provided, producing log Bayes factors of $0.25{-}0.9\pm0.05$ when compared to the other models. However without considering GW190425 or the fitted luminosity function, the evidence favours a cocoon-like model with log Bayes factors of $0.14\pm0.05$ over the Gaussian jet structure. We provide new constraints to the binary neutron star merger rates of $1{-}1300$Gpc$^{-3}$yr$^{-1}$ or $2{-}680$Gpc$^{-3}$yr$^{-1}$ when a fitted luminosity function is assumed., Comment: 18 pages, 8 figures

We present an improved version of the nested sampling algorithm nessai in which the core algorithm is modified to use importance weights. In the modified algorithm, samples are drawn from a mixture of normalising flows and the requirement for samples to be independently and identically distributed (i.i.d.) according to the prior is relaxed. Furthermore, it allows for samples to be added in any order, independently of a likelihood constraint, and for the evidence to be updated with batches of samples. We call the modified algorithm i-nessai. We first validate i-nessai using analytic likelihoods with known Bayesian evidences and show that the evidence estimates are unbiased in up to 32 dimensions. We compare i-nessai to standard nessai for the analytic likelihoods and the Rosenbrock likelihood, the results show that i-nessai is consistent with nessai whilst producing more precise evidence estimates. We then test i-nessai on 64 simulated gravitational-wave signals from binary black hole coalescence and show that it produces unbiased estimates of the parameters. We compare our results to those obtained using standard nessai and dynesty and find that i-nessai requires 2.68 and 13.3 times fewer likelihood evaluations to converge, respectively. We also test i-nessai of an 80-second simulated binary neutron star signal using a Reduced-Order-Quadrature (ROQ) basis and find that, on average, it converges in 24 minutes, whilst only requiring $1.01\times10^6$ likelihood evaluations compared to $1.42\times10^6$ for nessai and $4.30\times10^7$ for dynesty. These results demonstrate the i-nessai is consistent with nessai and dynesty whilst also being more efficient., Comment: 41 pages, 12 figures, 2 tables, published in Machine Learning: Science and Technology, code available at https://doi.org/10.5281/zenodo.8124198, updated with accepted version

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a $\sim$90$\times$ speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future., Comment: 9 pages, 4 figures, accepted to the special issue of Universe, "Waiting for GODOT -- Present and Future of Multi-Messenger Astronomy"

We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler's settings., Comment: 20 pages, 11 figures, accepted to PRD, typos corrected, added new figure, other minor corrections, code available at https://doi.org/10.5281/zenodo.4550693

Most Milky Way globular clusters (GCs) exhibit measurable flattening, even if on a very low level. Both cluster rotation and tidal fields are thought to cause this flattening. Nevertheless, rotation has only been confirmed in a handful of GCs, based mostly on individual radial velocities at large radii. We are conducting a survey of the central kinematics of Galactic GCs using the new Integral Field Unit instrument VIRUS-W. We detect rotation in all 11 GCs that we have observed so far, rendering it likely that a large majority of the Milky Way GCs rotate. We use published catalogs of the ACS survey of GCs to derive central ellipticities and position angles. We show that in all cases where the central ellipticity permits an accurate measurement of the position angle, those angles are in excellent agreement with the kinematic position angles that we derive from the VIRUS-W velocity fields. We find an unexpected tight correlation between central rotation and outer ellipticity, indicating that rotation drives flattening for the objects in our sample. We also find a tight correlation between central rotation and published values for the central velocity dispersion, most likely due to rotation impacting the old dispersion measurements., Comment: 6 pages, 3 figures; accepted for publication in ApJ Letters