Search

Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of traditional Bayesian inference. In this paper we explore the use of deep learning, namely Conditional Variational Autoencoders (CVAE), to estimate parameters of microlensed binary black hole (simulated) waveforms. We find that our CVAE model yields accurate parameter estimation and significant computational savings compared to Bayesian methods such as bilby (up to five orders of magnitude faster inferences). Moreover, the incorporation of CVAE-generated priors in bilby reduces the average runtime of the latter in about 48% with no penalty on its accuracy. Our results suggest that a CVAE model is a promising tool for future low-latency searches of lensed signals. Further applications to actual signals and integration with advanced pipelines could help extend the capabilities of GW observatories in detecting microlensing events.

In the study of rotating neutron stars the $I$-Love-$Q$ relations refer to the existence of various approximate, equation of state-independent relations involving the moment of inertia, the Love number and the quadrupole moment. These relations are relevant for observational astrophysics, since they allow (in theory) the inference of any two quantities within the $I$-Love-$Q$ triad out of the third one alone. However, the quantities involved in the relations are, in fact, normalized by a parameter $M_0$ that arises in the usual perturbative analytical approach as the mass of the background configuration. Since $M_0$ is not the mass of the rotating star $M_S$, it is not an observational quantity, which may affect the application of the relations to actual observations. This situation is usually ignored in most studies by taking $M_0$ to be the mass of the star, an approximation that can, in some cases, be inconsistent. In this paper we extract the value of $M_0$ using an $\textit{extended}$ version of the universal relations that involve a fourth parameter, $\delta M$, proportional to the difference $M_S-M_0$. We analyze to which degree this extended set of relations yields a more precise inference of compact star properties and equation of state parameters., Comment: 15 pages, 6 figures, 4 tables

Measuring accurate long-duration gravitational waves from extreme mass ratio inspirals (EMRIs) could provide scientifically fruitful knowledge of massive black hole populations and robust tests for general relatively during the LISA mission. However, the immense computational requirements surrounding EMRI data processing and analysis makes their detection and analysis challenging. We further develop and explore a sparse dictionary learning (SDL) algorithm to expeditiously reconstruct EMRI gravitational waveforms lasting as long as 1 year. A suite of year-long EMRI systems are studied to understand the detection and accurate waveform retrieval prospects of the method. We show that full-year EMRIs can be reconstructed within 2 minutes, some with a false alarm rate less than 0.001/yr and with 1.16 day time windows with mismatch as low as 0.06. This provides an encouraging prospect to use the SDL method for long-duration GW searches like that for EMRIs in this study.

Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched-filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular for future detectors anticipated to have high detection rates, remains an ongoing effort. In this paper, we develop and test a sparse dictionary learning (SDL) algorithm for the rapid detection of GWs. We evaluate the algorithms biases and estimate its GW detection rate for an astrophysical population of binary black holes. The SDL algorithm is assessed using both, simulated data injected into the proposed A+ detector sensitivity and real data containing confident detections from the third LVK observing run. We find that our SDL algorithm can reconstruct a single binary black hole signal in less than 1 s. This suggests that SDL could be regarded as a promising approach for rapid, efficient GW detection in future observing runs of ground-based detectors.

In this paper, we apply a novel approach based on physics-informed neural networks to the computation of quasinormal modes of black hole solutions in modified gravity. In particular, we focus on the case of Einstein-scalar-Gauss-Bonnet theory, with several choices of the coupling function between the scalar field and the Gauss-Bonnet invariant. This type of calculation introduces a number of challenges with respect to the case of General Relativity, mainly due to the extra complexity of the perturbation equations and to the fact that the background solution is known only numerically. The solution of these perturbation equations typically requires sophisticated numerical techniques that are not easy to develop in computational codes. We show that physics-informed neural networks have an accuracy which is comparable to traditional numerical methods in the case of numerical backgrounds, while being very simple to implement. Additionally, the use of GPU parallelization is straightforward thanks to the use of standard machine learning environments., Comment: 13 pages, 9 figures. v2: Matches published version

We present several high-accuracy surrogate models for gravitational-wave signals from equal-mass head-on mergers of Proca stars, computed through the Newman-Penrose scalar $\psi_4$. We also discuss the current state of the model extensions to mergers of Proca stars with different masses, and the particular challenges that these present. The models are divided in two main categories: two-stage and monolithic. In the two-stage models, a dimensional reduction algorithm is applied to embed the data in a reduced feature space, which is then interpolated in terms of the physical parameters. For the monolithic models, a single neural network is trained to predict the waveform from the input physical parameter. Our model displays mismatches below $10^{-3}$ with respect to the original numerical waveforms. Finally, we demonstrate the usage of our model in full Bayesian parameter inference through the accurate recovery of numerical relativity signals injected in zero-noise, together with the analysis of GW190521. For the latter, we observe excellent agreement with existing results that make use of full numerical relativity., Comment: 16 pages, 14 figures. v2: Matches published version

We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the maximum (peak) frequency and the GW strain amplitude ($\Delta h$) multiplied by the luminosity distance ($D$) attained at core bounce, respectively, $(f_{peak})$ and $(D \cdot \Delta h)$. Our datasets are built from a catalog of numerically generated CCSN waveforms assembled by Richers et al. 2017. Those waveforms are injected into noise from the Advanced Laser Interferometer Gravitational Wave Observatory and Advanced Virgo detectors corresponding to the O2 and O3a observing runs. For a network signal-to-noise ratio (SNR) above 5, our classification network using time series detects Galactic CCSN GW signals buried in detector noise with a false positive rate of 0.10% and a 98% accuracy, being able to detect all signals with SNR>10. The inference of $f_{peak}$ is more accurate than for $D \cdot \Delta h $, particularly for our datasets with the shortest time window (0.25 s) and for a minimum SNR=15. From the calibration plots of predicted versus true values of the two parameters, the standard deviation ($\sigma$) and the slope deviation with respect to the ideal value are computed. We find $\sigma_{D \cdot \Delta h} = 52.6$ cm and $\sigma_{f_{peak}} = 18.3$ Hz, with respective slope deviations of 11.6% and 8.3%. Our best model is also tested on waveforms from a recent CCSN catalog built by Mitra et al. 2023, different from the one used for the training. For these new waveforms, the true values of the two parameters are mostly within the $1\sigma$ band around the network's predicted values. Our results show that DL techniques hold promise to infer physical parameters of Galactic rotational CCSN events.

Explaining gravitational-wave (GW) observations of binary neutron star (BNS) mergers requires an understanding of matter beyond nuclear saturation density. Our current knowledge of the properties of high-density matter relies on electromagnetic and GW observations, nuclear physics experiments, and general relativistic numerical simulations. In this paper we perform numerical-relativity simulations of BNS mergers subject to non-convex dynamics, allowing for the appearance of expansive shock waves and compressive rarefactions. Using a phenomenological non-convex equation of state we identify observable imprints on the GW spectra of the remnant. In particular, we find that non-convexity induces a significant shift in the quasi-universal relation between the peak frequency of the dominant mode and the tidal deformability (of order $\Delta f_{\rm peak}\gtrsim 380\,\rm Hz$) with respect to that of binaries with convex (regular) dynamics. Similar shifts have been reported in the literature, attributed however to first-order phase transitions from nuclear/hadronic matter to deconfined quark matter. We argue that the ultimate origin of the frequency shifts is to be found in the presence of anomalous, non-convex dynamics in the binary remnant., Comment: 13 pages, 8 figures. Minor changes. Journal version

We explore the prospects for identifying differences in simulated gravitational-wave signals of binary neutron star (BNS) mergers associated with the way thermal effects are incorporated in the numerical-relativity modelling. We consider a hybrid approach in which the equation of state (EoS) comprises a cold, zero temperature, piecewise-polytropic part and a thermal part described by an ideal gas, and a tabulated approach based on self-consistent, microphysical, finite-temperature EoS. We use time-domain waveforms corresponding to BNS merger simulations with four different EoS. Those are injected into Gaussian noise given by the sensitivity of the third-generation detector Einstein Telescope and reconstructed using BayesWave, a Bayesian data-analysis algorithm that recovers the signals through a model-agnostic approach. The two representations of thermal effects result in frequency shifts of the dominant peaks in the spectra of the post-merger signals, for both the quadrupole fundamental mode and the late-time inertial modes. For some of the EoS investigated those differences are large enough to be told apart, especially in the early post-merger phase when the signal amplitude is the loudest., Comment: 15 pages, 8 figures, 3 tables

Gravitational waves are emitted from deep within a core-collapse supernova, which may enable us to determine the mechanism of the explosion from a gravitational-wave detection. Previous studies suggested that it is possible to determine if the explosion mechanism is neutrino-driven or magneto-rotationally powered from the gravitational-wave signal. However, long duration magneto-rotational waveforms, that cover the full explosion phase, were not available during the time of previous studies, and explosions were just assumed to be magneto-rotationally driven if the model was rapidly rotating. Therefore, we perform an updated study using new 3D long-duration magneto-rotational core-collapse supernova waveforms that cover the full explosion phase, injected into noise for the Advanced LIGO, Einstein Telescope and NEMO gravitational-wave detectors. We also include a category for failed explosions in our signal classification results. We then determine the explosion mechanism of the signals using three different methods: Bayesian model selection, dictionary learning, and convolutional neural networks. The three different methods are able to distinguish between neutrino-driven explosions and magneto-rotational explosions, even if the neutrino-driven explosion model is rapidly rotating. However they can only distinguish between the non-exploding and neutrino-driven explosions for signals with a high signal to noise ratio., Comment: Accepted in Physical Review D

We assess the impact of accurate, self-consistent modelling of thermal effects in neutron-star merger remnants in the context of third-generation gravitational-wave detectors. This is done through the usage, in Bayesian model selection experiments, of numerical-relativity simulations of binary neutron star (BNS) mergers modelled through: a) nuclear, finite-temperature (or ``tabulated'') equations of state (EoSs), and b) their simplifed piecewise (or ``hybrid'') representation. These cover four different EoSs, namely SLy4, DD2, HShen and LS220. Our analyses make direct use of the Newman-Penrose scalar $\psi_4$ outputted by numerical simulations. Considering a detector network formed by three Cosmic Explorers, we show that differences in the gravitational-wave emission predicted by the two models are detectable with a natural logarithmic Bayes Factor $\log{\cal{B}}\geq 5$ at average distances of $d_L \simeq 50$Mpc, reaching $d_L \simeq 100$Mpc for source inclinations $\iota \leq 0.8$, regardless of the EoS. This impact is most pronounced for the HShen EoS. For low inclinations, only the DD2 EoS prevents the detectability of such modelling differences at $d_L \simeq 150$Mpc. Our results suggest that the usage a self-consistent treatment of thermal effects is crucial for third-generation gravitational wave detectors., Comment: 9 pages, 3 Figures

We investigate the time evolution of spherically symmetric boson stars in Palatini $f(\mathcal{R})$ gravity through Numerical Relativity computations. Employing a novel approach that establishes a correspondence between modified gravity with scalar matter and General Relativity with modified scalar matter, we are able to use the techniques of Numerical Relativity to simulate these systems. Specifically, we focus on the quadratic theory $f(\mathcal{R})=\mathcal{R}+\xi\mathcal{R}^2$ and compare the obtained solutions with those in General Relativity, exploring both positive and negative values of the coupling parameter $\xi$. Our findings reveal that boson stars in Palatini $f(\mathcal{R})$ gravity exhibit both stable and unstable evolutions. The latter give rise to three distinct scenarios: migration towards a stable configuration, complete dispersion, and gravitational collapse leading to the formation of a baby universe structure.

We study the tidal problem and the resulting $I$-Love-$Q$ approximate universal relations for rotating superfluid neutron stars in the Hartle-Thorne formalism. Superfluid stars are described in this work by means of a two-fluid model consisting of superfluid neutrons and all other charged constituents. We employ a stationary and axisymmetric perturbation scheme to second order around a static and spherically symmetric background. Recently, we used this scheme to study isolated rotating superfluid stars. In this paper it is applied to analyze the axially symmetric sector of the tidal problem in a binary system. We show that a consistent use of perturbative matching theory amends the original two-fluid formalism for the tidal problem to account for the possible non-zero value of the energy density at the boundary of the star. This is exemplified by building numerically different stellar models spanning three equations of state. Significant departures from universality are found when the correct matching relations are not taken into account. We also present an augmented set of universal relations for superfluid neutron stars which includes the contribution to the total mass of the star at second order, $\delta M$. Therefore, our results complete the set of universal relations for rotating superfluid stars, generalizing our previous findings in the perfect fluid case., Comment: 18 pages, 3 figures. Reference added, typos and minor corrections, and some discussion expanded, to agree with the published version

The modelling of astrophysical systems such as binary neutron star mergers or the formation of magnetars from the collapse of massive stars involves the numerical evolution of magnetised fluids at extremely large Reynolds numbers. This is a major challenge for (unresolved) direct numerical simulations which may struggle to resolve highly dynamical features as, e.g. turbulence, magnetic field amplification, or the transport of angular momentum. Sub-grid models offer a means to overcome those difficulties. In a recent paper we presented MInIT, an MHD-instability-induced-turbulence mean-field, sub-grid model based on the modelling of the turbulent (Maxwell, Reynolds, and Faraday) stress tensors. While in our previous work MInIT was assessed within the framework of the magnetorotational instability, in this paper we further evaluate the model in the context of the Kelvin-Helmholtz instability (KHI). The main difference with other sub-grid models (as e.g. the alpha-viscosity model or the gradient model) is that in MInIT we track independently the turbulent energy density at sub-grid scales, which is used, via a simple closure relation, to compute the different turbulent stresses relevant for the dynamics. The free coefficients of the model are calibrated using well resolved box simulations of magnetic turbulence generated by the KHI. We test the model against these simulations and show that it yields order-of-magnitude accurate predictions for the evolution of the turbulent Reynolds and Maxwell stresses., Comment: 12 pages, 13 figures Accepted for publication in MNRAS

Core-collapse supernovae (CCSNe) are prime candidates for gravitational-wave detectors. The analysis of their complex waveforms can potentially provide information on the physical processes operating during the collapse of the iron cores of massive stars. In this work we analyze the early-bounce rapidly rotating CCSN signals reported in the waveform catalog of Richers et al 2017, which comprises over 1800 axisymmetric simulations extending up to about 10~ms of post-bounce evolution. It was previously established that for a large range of progenitors, the amplitude of the bounce signal, $\Delta h$, is proportional to the ratio of rotational-kinetic energy to potential energy, T/|W|, and the peak frequency, $f_{\rm peak}$, is proportional to the square root of the central rest-mass density. In this work, we exploit these relations to suggest that it could be possible to use such waveforms to infer protoneutron star properties from a future gravitational wave observation, if the distance and inclination are well known. Our approach relies on the ability to describe a subset of the waveforms in the early post-bounce phase in a simple form depending only on two parameters, $\Delta h$ and $f_{\rm peak}$. We use this template to perform a Bayesian inference analysis of waveform injections in Gaussian colored noise for a network of three gravitational wave detectors formed by Advanced LIGO and Advanced Virgo. We show that, for a galactic event, it is possible to recover the peak frequency and amplitude with an accuracy better than 10% for about 80% and 60% of the signals, respectively, given known distance and inclination angle. However, inference on waveforms from outside the Richers catalog is not reliable, indicating a need for carefully verified waveforms of the first 10 ms after bounce of rapidly rotating supernovae of different progenitors with agreement between different codes., Comment: 23 pages, 17 figures, accepted for publication in PRD

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100,000 elements for each of four different waveform models (or approximants) in order to test how approximant choice affects feature extraction. Our choices include \texttt{SEOBNRv4P} and \texttt{IMRPhenomPv3}, which contain only the dominant quadrupole emission mode, alongside \texttt{IMRPhenomPv3HM} and \texttt{NRHybSur3dq8}, which also account for high-order modes. Each dataset element is injected into detector noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK) collaboration. We identify the Temporal Convolutional Network (TCN) architecture as the overall best performer in terms of training and validation losses and absence of overfitting to data. Comparison of results between datasets shows that the choice of waveform approximant for the creation of a dataset conditions the feature extraction ability of a trained network. Hence, care should be taken when building a dataset for the training of neural networks, as certain approximants may result in better network convergence of evaluation metrics. However, this performance does not necessarily translate to data which is more faithful to numerical relativity simulations. We also apply this network on actual signals from LVK runs, finding that its feature-extracting performance can be effective on real data., Comment: Fixed Fig. 7 not being rendered

Dynamical captures of black holes may take place in dense stellar media due to the emission of gravitational radiation during a close passage. Detection of such events requires detailed modelling, since their phenomenology qualitatively differs from that of quasi-circular binaries. Very few models can deliver such waveforms, and none includes information from Numerical Relativity (NR) simulations of non quasi-circular coalescences. In this study we present a first step towards a fully NR-informed Effective One Body (EOB) model of dynamical captures. We perform 14 new simulations of single and double encounter mergers, and use this data to inform the merger-ringdown model of the TEOBResumS-Dali approximant. We keep the initial energy approximately fixed to the binary mass, and vary the mass-rescaled, dimensionless angular momentum in the range $(0.6, 1.1)$, the mass ratio in $(1, 2.15)$ and aligned dimensionless spins in $(-0.5, 0.5)$. We find that the model is able to match NR to $97%$, improving previous performances, without the need of modifying the base-line template. Upon NR informing the model, this improves to $99%$ with the exception of one outlier corresponding to a direct plunge. The maximum EOBNR phase difference at merger for the uninformed model is of $0.15$ radians, which is reduced to $0.1$ radians after the NR information is introduced. We outline the steps towards a fully informed EOB model of dynamical captures, and discuss future improvements., Comment: 21 pages, 15 figures

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possible gravitational-wave emissions. For neutrino-driven explosions, the distance at which we reach 50% detection efficiency is up to 8.9 kpc, while more energetic magnetorotationally-driven explosions are detectable at larger distances. The distance reaches for selected models of the black hole formation, and quantum chromodynamics phase transition are also provided. We then constrain the core-collapse supernova engine across a wide frequency range from 50 Hz to 2 kHz. The upper limits on gravitational-wave energy and luminosity emission are at low frequencies down to $10^{-4}\,M_\odot c^2$ and $6 \times 10^{-4}\,M_\odot c^2$/s, respectively. The upper limits on the proto-neutron star ellipticity are down to 3 at high frequencies. Finally, by combining the results obtained with the data from the first and second observing runs of LIGO and Virgo, we improve the constraints of the parameter spaces of the extreme emission models. Specifically, the proto-neutron star ellipticities for the long-lasting bar mode model are down to 1 for long emission (1 s) at high frequency., Comment: 20 pages, 11 figures; https://dcc.ligo.org/LIGO-P2200361

We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that requires the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss and how the activation functions of the neural model are dynamically accounted for. The performance of our GA-PINN model is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with our GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative $l^{2}$ error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ``ground truth'', and with the error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by our GA-PINN model is comparable to that obtained with a shock-capturing scheme and significantly higher than that achieved by a baseline PINN algorithm. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved ones, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN method discussed could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions., Comment: 25 pages, 16 figures

We consider equilibrium models of spherical boson stars in Palatini $f(\mathcal{R})=\mathcal{R}+\xi \mathcal{R}^2$ gravity and study their collapse when perturbed. The Einstein-Klein-Gordon system is solved using a recently established correspondence in an Einstein frame representation. We find that, in that frame, the endpoint is a nonrotating black hole surrounded by a quasi-stationary cloud of scalar field. However, the dynamics in the $f(\mathcal{R})$ frame is dramatically different. The innermost region of the collapsing object exhibits the formation of a finite-size, exponentially-expanding $\textit{ baby universe}$ connected with the outer (parent) universe via a minimal area surface (a throat or umbilical cord). Our simulations indicate that this surface is at all times hidden inside a horizon, causally disconnecting the baby universe from observers above the horizon. The implications of our findings in other areas of gravitational physics are also discussed.

Analytical models of magnetized, geometrically thick disks are relevant to understand the physical conditions of plasma around compact objects and to explore its emitting properties. This has become increasingly important in recent years in the light of the Event Horizon Telescope observations of Sgr A$^*$ and M87. Models of thick disks around black holes usually consider constant angular momentum distributions and do not take into account the magnetic response of the fluid to applied magnetic fields. We present a generalization of our previous work on stationary models of magnetized accretion disks with magnetic polarization (Pimentel et al. 2018). This extension is achieved by accounting for non-constant specific angular momentum profiles, done through a two-parameter ansatz for those distributions. We build a large number of new equilibrium solutions of thick disks with magnetic polarization around Kerr black holes, selecting suitable parameter values within the intrinsically substantial parameter space of the models. We study the morphology and the physical properties of those solutions, finding qualitative changes with respect to the constant angular momentum tori solutions (Pimentel et al. 2018). However, the dependences found on the angular momentum distribution or on the black hole spin do not seem to be strong. Some of the new solutions, however, exhibit a local maximum of the magnetization function, absent in standard magnetized tori. Due to the enhanced development of the magneto-rotational instability as a result of magnetic susceptibility, those models might be particularly well-suited to investigate jet formation through general-relativistic MHD simulations. The new equilibrium solutions reported here can be used as initial data in numerical codes to assess the impact of magnetic susceptibility in the dynamics and observational properties of black hole-thick disk systems., Comment: 19 pages, 10 figures. Accepted for publication in Physical Review D

We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.~glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual gravitational-wave signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification., Comment: 15 pages, 14 figures

Some recent, long-term numerical simulations of binary neutron star mergers have shown that the long-lived remnants produced in such mergers might be affected by convective instabilities. Those would trigger the excitation of inertial modes, providing a potential method to improve our understanding of the rotational and thermal properties of neutron stars through the analysis of the modes' imprint in the late post-merger gravitational-wave signal. In this paper we assess the detectability of those modes by injecting numerically generated post-merger waveforms into colored Gaussian noise of second-generation and future detectors. Signals are recovered using BayesWave, a Bayesian data-analysis algorithm that reconstructs them through a morphology-independent approach using series of sine-Gaussian wavelets. Our study reveals that current interferometers (i.e. the Handford-Livingston-Virgo network) recover the peak frequency of inertial modes only if the merger occurs at distances of up to 1 Mpc. For future detectors such as the Einstein Telescope, the range of detection increases by about a factor 10.

We present a numerical study of relativistic Bondi-Hoyle-Lyttleton (BHL) accretion onto an asymptotically flat black hole with synchronized hair. The hair is sourced by an ultralight, complex scalar field, minimally coupled to Einstein's gravity. Our simulations consider a supersonic flow parametrized by the asymptotic values of the fluid quantities and a sample of hairy black holes with different masses, angular momenta, and amount of scalar hair. For all models, steady-state BHL accretion solutions are attained that are characterized by the presence of a shock-cone and a stagnation point downstream. For the models of the sample with the largest component of scalar field, the shock-cone envelops fully the black hole, transitioning into a bow-shock, and the stagnation points move further away downstream. Analytical expressions for the mass accretion rates are obtained after fitting the numerical results, which can be used to analyze black-hole formation scenarios in the presence of ultralight scalar fields. The formation of a shock-cone leads to regions where sound waves can be trapped and resonant oscillations excited. We measure the frequencies of such quasi-periodic oscillations and point out a possible association with quasi-periodic oscillations in the X-ray light curve of Sgr~A* and microquasars., Comment: 24 pages, 3 tables, 10 figures. Minor changes

We use physics-informed neural networks (PINNs) to compute the first quasi-normal modes of the Kerr geometry via the Teukolsky equation. This technique allows us to extract the complex frequencies and separation constants of the equation without the need for sophisticated numerical techniques, and with an almost immediate implementation under the \texttt{PyTorch} framework. We are able to compute the oscillation frequencies and damping times for arbitrary black hole spins and masses, with accuracy typically below the percentual level as compared to the accepted values in the literature. We find that PINN-computed quasi-normal modes are indistinguishable from those obtained through existing methods at signal-to-noise ratios (SNRs) larger than 100, making the former reliable for gravitational-wave data analysis in the mid term, before the arrival of third-generation detectors like LISA or the Einstein Telescope, where SNRs of ${\cal O}(1000)$ might be achieved., Comment: 12 pages, 7 figures. v2: Matches published version, minor typos corrected

We discuss slowly-rotating, general relativistic, superfluid neutron stars in the Hartle-Thorne formulation. The composition of the stars is described by a simple two-fluid model which accounts for superfluid neutrons and all other constituents. We apply a perturbed matching framework to derive a new formalism for slowly-rotating superfluid neutron stars, valid up to second-order perturbation theory, building on the original formulation reported by Andersson and Comer in 2001. The present study constitutes an extension of previous work in the single-fluid case where it was shown that the Hartle-Thorne formalism needs to be amended since it does not provide the correct results when the energy density does not vanish at the surface of the star. We discuss in detail the corrections that need to be applied to the original two-fluid formalism in order to account for non vanishing energy densities at the boundary. In the process, we also find a correction needed in the computation of the deformation of the stellar surface in the original two-fluid model in all cases (irrespective of the value of the energy density at the surface). The discrepancies found between the two formalisms are illustrated by building numerical stellar models, focusing on the comparison in the calculation of the stellar mass, the deformation of the star, and in the Kepler limit of rotation. In particular, using a toy-model equation of state for which the energy density does not vanish at the boundary of the star we demonstrate that the corrections to the formalism we find impact the structure of slowly-rotating superfluid neutron stars in a significant way., Comment: 24 pages, 16 figures: v2. minor corrections and changes to match the published version

The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures long-range global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals from binary neutron star coalescences, built from five distinct, cold equations of state (EOS) of nuclear matter. From the analysis of the mass dependence of the tidal deformability parameter for each EOS class it is shown that the AST model achieves a promising performance in correctly classifying the EOS purely from the gravitational wave signals, especially when the component masses of the binary system are in the range $[1,1.5]M_{\odot}$. Furthermore, the generalization ability of the model is investigated by using gravitational-wave signals from a new EOS not used during the training of the model, achieving fairly satisfactory results. Overall, the results, obtained using the simplified setup of noise-free waveforms, show that the AST model, once trained, might allow for the instantaneous inference of the cold nuclear matter EOS directly from the inspiral gravitational-wave signals produced in binary neutron star coalescences., Comment: 11 pages, 11 figures

The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from $10^2 M_{\odot}$ to $10^4 M_{\odot}$. Our method proves extremely successful for binaries with total mass greater than $\sim 3\times 10^3$ $ M_{\odot}$ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater., Comment: 6 pages, 3 figures

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system's adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative., Comment: 9 pages, Accepted and to appear in Proceedings of the 17th International Conference on the Foundations of Digital Games (FDG), 2022

Insufficient numerical resolution of grid-based, direct numerical simulations (DNS) hampers the development of instabilitydriven turbulence at small (unresolved) scales. As an alternative to DNS, sub-grid models can potentially reproduce the effects of turbulence at small scales in terms of the resolved scales, and hence can capture physical effects with less computational resources. We present a new sub-grid model, the MHD-instability-induced-turbulence (MInIT) mean-field model. MInIT is a physically motivated model based on the evolution of the turbulent (Maxwell, Reynolds, and Faraday) stress tensors and their relation with the turbulent energy densities of the magneto-rotational (MRI) and parasitic instabilities, modeled with two partial differential evolution equations with stiff source terms. Their solution allows obtaining the turbulent stress tensors through the constant coefficients that link them to the energy densities. The model is assessed using data from MRI in-box DNS and applying a filtering operation to compare the filtered data with that from the model. Using the $L_2$-norm as the metric for the comparison, we find less than one order-of-magnitude difference between the two sets of data. No dependence on filter size or length scale of unresolved scales is found, as opposed to results using the gradient model (which we also use to contrast our model) in which the $L_2$-norm of some of the stresses increases with filter size. We conclude that MInIT can help DNS by properly capturing small-scale turbulent stresses which has potential implications on the dynamics of highly-magnetized rotating compact objects, such as those formed during binary neutron star mergers., Comment: 20 pages, 15 figures

We present a systematic study of the dynamics and gravitational-wave emission of head-on collisions of spinning vector boson stars, known as Proca stars. To this aim we build a catalogue of about 800 numerical-relativity simulations of such systems. We find that the wave-like nature of bosonic stars has a large impact on the gravitational-wave emission. In particular, we show that the initial relative phase $\Delta \epsilon =\epsilon_1-\epsilon_2$ of the two complex fields forming the stars (or equivalently, the relative phase at merger) strongly impacts both the emitted gravitational-wave energy and the corresponding mode structure. This leads to a non-monotonic dependence of the emission on the frequency of the secondary star $\omega_2$, for fixed frequency $\omega_1$ of the primary. This phenomenology, which has not been found for the case of black-hole mergers, reflects the distinct ability of the Proca field to interact with itself in both constructive and destructive manners. We postulate this may serve as a smoking gun to shed light on the possible existence of these objects., Comment: Updated version

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. In this work-in-progress paper, we present a new environment for research into situation awareness and attention guidance (SAAG): an augmented reality version of the board game Carcassonne. We also present our initial work in producing a SAAG pipeline, including the creation of game state encodings, the development and training of a gameplay AI, and the design of situation modelling and gaze tracking systems.

We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision ($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional pre-processing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines and, when integrated in more complex pipelines, for real-time inference of physical source parameters., Comment: 11 pages, 9 figures

We present the first systematic search for exotic compact mergers in Advanced LIGO and Virgo events. We compare the short gravitational-wave signals GW190521, GW190426$\_$190642, GW200220$\_$061928 and the trigger 200114$\_$020818 (or S200114f) to a new catalogue of 759 numerical simulations of head-on mergers of horizonless exotic compact objects known as Proca stars, interpreted as self-gravitating lumps of (fuzzy) dark matter sourced by an ultralight (vector) bosonic particle. The Proca-star merger hypothesis is strongly rejected with respect to the black hole merger one by GW190426, weakly rejected by GW200220 and weakly favoured by GW190521 and S200114f. GW190521 and GW200220 yield highly consistent boson masses of $\mu_{\rm B} = 8.69^{+0.61}_{-0.75}\times10^{-13}$ eV and $\mu_{\rm B} = 9.13^{+1.18}_{-1.30}\times10^{-13}$ eV at the $90\%$ credible level. We conduct a preliminary population study of the compact binaries behind these events. Excluding (including) S200114f as a real event, and ignoring boson-mass consistencies across events, we estimate a fraction of Proca-star mergers of $\zeta = 0.27^{+0.43}_{-0.25} \ (0.39^{+0.38}_{-0.33})$. We discuss the impact of boson-mass consistency across events in such estimates. Our results maintain GW190521 as a Proca-star merger candidate and pave the way towards population studies considering exotic compact objects., Comment: Version accepted in Phys.Rev.D. 27 pages, 16 Figures, 4 Appendixes

We investigate the effects ultralight bosonic field dark matter may have on the dynamics of unstable differentially-rotating neutron stars prone to the bar-mode instability. To this aim we perform numerical simulations in general relativity of rotating neutron stars accreting an initial spherically symmetric bosonic field cloud, solving the Einstein-(complex, massive) Klein-Gordon-Euler and the Einstein-(complex) Proca-Euler systems. We find that the presence of the bosonic field can critically modify the development of the bar-mode instability of neutron stars, depending on the total mass of the bosonic field and on the boson particle mass. In some cases, the accreting bosonic field can even quench the dominant $\ell=m=2$ mode of the bar-deformation by dynamically forming a mixed (fermion-boson) star that retains part of the angular momentum of the original neutron star. However, the mixed star undergoes the development of a mixed bar that leads to significant gravitational-wave emission, substantially different to that of the isolated neutron star. Our results indicate that dark-matter accretion in neutron stars could change the frequency of the expected emission of the bar-mode instability, which would have an important impact on ongoing searches for continuous gravitational waves., Comment: 18 pages, 8 figures

Detection and parameter inference of gravitational-wave signals \ncor{from compact mergers} rely on the comparison of the incoming detector strain data $d(t)$ to waveform templates for the gravitational-wave strain $h(t)$ that ultimately rely on the resolution of Einstein's equations via numerical relativity simulations. These, however, commonly output a quantity known as the Newman-Penrose scalar $\psi_4(t)$ which, under the Bondi gauge, is related to the gravitational-wave strain by $\psi_4(t)=\mathrm{d}^2h(t) / \mathrm{d}t^2$. Therefore, obtaining strain templates involves an integration process that introduces artefacts that need to be treated in a rather manual way. By taking second-order finite differences on the detector data and inferring the corresponding background noise distribution, we develop a framework to perform gravitational-wave data analysis directly using $\psi_4(t)$ templates. We first demonstrate this formalism, and the impact of integration artefacts in strain templates, through the recovery of numerically simulated signals from head-on collisions of Proca stars injected in Advanced LIGO noise. Next, we re-analyse the event GW190521 under the hypothesis of a Proca-star merger, obtaining results equivalent to those in Ref.[1], where we used the classical strain framework. We find, however, that integration errors would strongly impact our analysis if GW190521 was four times louder. Finally, we show that our framework fixes significant biases in the interpretation of the high-mass GW trigger S200114f arising from the usage of strain templates. We remove the need to obtain strain waveforms from numerical relativity simulations, avoiding the associated systematic errors., Comment: Version accepted for publication in Physical Review X. 27 pages, 11 Figures, 3 Appendixes

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games' narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution., Comment: 8 pages, Accepted and to appear in Proceedings of the 13th Workshop on Procedural Content Generation, at the Foundations of Digital Games (FDG), 2022

The data collected by the current network of gravitational-wave detectors are largely dominated by instrumental noise. Total variation methods based on L1-norm minimization have recently been proposed as a powerful technique for noise removal in gravitational-wave data. In particular, the regularized Rudin-Osher-Fatemi (rROF) model has proven effective to denoise signals embedded in either simulated Gaussian noise or actual detector noise. Importing the rROF model to existing search pipelines seems therefore worth considering. In this paper, we discuss the implementation of two variants of the rROF algorithm as two separate plug-ins of the coherent Wave Burst (cWB) pipeline designed to conduct searches of unmodelled gravitational-wave burst sources. The first approach is based on a single-step rROF method and the second one employs an iterative rROF procedure. Both approaches are calibrated using actual gravitational-wave events from the first three observing runs of the LIGO-Virgo-KAGRA collaboration, namely GW1501914, GW151226, GW170817, and GW190521, encompassing different types of compact binary coalescences. Our analysis shows that the iterative version of the rROF denoising algorithm implemented in the cWB pipeline effectively eliminates noise while preserving the waveform signals intact. Therefore, the combined approach yields higher signal-to-noise values than those computed by the cWB pipeline without the rROF denoising step. The incorporation of the iterative rROF algorithm in the cWB pipeline might hence impact the detectability capabilities of the pipeline along with the inference of source properties., Comment: 14 pages, 4 figures

Core-collapse supernovae (CCSN) are a prime source of gravitational waves. Estimations of their typical frequencies make them perfect targets for the current network of advanced, ground-based detectors. A successful detection could potentially reveal the underlying explosion mechanism through the analysis of the waveform. This has been illustrated using the SupernovaModel Evidence Extractor (SMEE; Logue et al. (2012)), an algorithm based on principal-component analysis and Bayesian model selection. Here, we present a complementary approach to SMEE based on (supervised) dictionary-learning and show that it is able to reconstruct and classify CCSN signals according to their morphology. Our waveform signals are obtained from (a) two publicly available catalogs built from numerical simulations of neutrino-driven (Mur) and magneto-rotational (Dim) CCSN explosions and (b) from a third 'mock' catalog of simulated sine-Gaussian (SG) waveforms. Those signals are injected into coloured Gaussian noise to simulate the background noise of Advanced LIGO in its broadband configuration and scaled to a freely-specifiable signal-to-noise ratio (SNR). We show that our approach correctly classifies signals from all three dictionaries. In particular, for SNR=15-20, we obtain perfect matches for both Dim and SG signals and about 85% true classifications for Mur signals. These results are comparable to those reported by SMEE for the same CCSN signals when those are injected in only one LIGO detector. We discuss the main limitations of our approach as well as possible improvements.

Mixed fermion-boson stars are stable, horizonless, everywhere regular solutions of the coupled Einstein-(complex, massive) Klein-Gordon-Euler system. While isolated neutron stars and boson stars are uniquely determined by their central energy density, mixed configurations conform an extended parameter space that depends on the combination of the number of fermions and (ultra-light) bosons. The wider possibilities offered by fermion-boson stars could help explain the tension in the measurements of neutron star masses and radii reported in recent multi-messenger observations and nuclear-physics experiments. In this work we construct equilibrium configurations of mixed fermion-boson stars with realistic equations of state for the fermionic component and different percentages of bosonic matter. We show that our solutions are in excellent agreement with multi-messenger data, including gravitational-wave events GW170817 and GW190814 and X-ray pulsars PSR J0030+0451 and PSR J0740+6620, as well as with nuclear physics constraints from the PREX-2 experiment., Comment: 9 pages, 7 figures

We present new equilibrium solutions of stationary models of magnetized thick disks (or tori) around Kerr black holes with synchronised scalar hair. The models reported here largely extend our previous results based on constant radial distributions of the specific angular momentum along the equatorial plane. We introduce a new way to prescribe the distribution of the disk's angular momentum based on a combination of two previous proposals and compute the angular momentum distribution outside the equatorial plane by resorting to the construction of von Zeipel cylinders. We find that the effect of the scalar hair on the black hole spacetime can yield significant differences in the disk morphology and properties compared to what is found if the spacetime is purely Kerr. Some of the tori built within the most extreme, background hairy black hole spacetime of our sample exhibit the appearance of two maxima in the gravitational energy density which impacts the radial profile distributions of the disk's thermodynamical quantities. The models reported in this paper can be used as initial data for numerical evolutions with GRMHD codes to study their stability properties. Moreover, they can be employed as illuminating sources to build shadows of Kerr black holes with scalar hair which might help further constrain the no-hair hypothesis as new observational data is collected., Comment: 21 pages, 10 figures

We study the gravitational-wave background from the observed cosmological quasar distribution. Using the DR9Q quasar catalogue from the ninth data release of the Sloan Digital Sky Survey (SDSS), we create a complete, statistically consistent sample of quasars from $z=0.3$ to $5.4$. Employing the spectroscopic information from the catalogue we estimate the masses of the supermassive black holes hosted by the quasars in the sample, resulting in a log-normal distribution of mean $10^{8.32\pm0.33}M_{\odot}$. The computation of the individual gravitational-wave strains relies on specific functional forms derived from simulations of gravitational collapse and mergers of massive black hole binaries. The background gravitational-wave emission is assembled by adding up the individual signals from each quasar modelled as plane waves whose interference can be constructive or destructive depending on the quasar evolutionary state. Our results indicate that the estimated gravitational-wave background discussed in this work could only be marginally detectable by LISA. This conclusion might change if more complete quasar catalogs than that provided by the SDSS were available., Comment: 7 pages, 4 figures. Accepted for publication in Physical Review D

We study numerically the nonlinear stability of {\it excited} fermion-boson stars in spherical symmetry. Such compound hypothetical stars, composed by fermions and bosons, are gravitationally bound, regular, and static configurations described within the coupled Einstein-Klein-Gordon-Euler theoretical framework. The excited configurations are characterized by the presence in the radial profile of the (complex, massive) scalar field -- the bosonic piece -- of at least one node across the star. The dynamical emergence of one such configuration from the accretion of a cloud of scalar field onto an already-formed neutron star, was numerically revealed in our previous investigation. Prompted by that finding we construct here equilibrium configurations of excited fermion-boson stars and study their stability properties using numerical-relativity simulations. In addition, we also analyze their dynamical formation from generic, constraint-satisfying initial data. Contrary to purely boson stars in the excited state, which are known to be generically unstable, our study reveals the appearance of a cooperative stabilization mechanism between the fermionic and bosonic constituents of those excited-state mixed stars. While similar examples of stabilization mechanisms have been recently discussed in the context of $\ell-$boson stars and multi-field, multi-frequency boson stars, our results seem to indicate that the stabilization mechanism is a purely gravitational effect and does not depend on the type of matter of the companion star., Comment: 11 pages, 16 figures

We explore equilibrium solutions of spherically symmetric boson stars in the Palatini formulation of $f(\mathcal{R})$ gravity. We account for the modifications introduced in the gravitational sector by using a recently established correspondence between modified gravity with scalar matter and general relativity with modified scalar matter. We focus on the quadratic theory $f(\mathcal{R})=R+\xi R^2$ and compare its solutions with those found in general relativity, exploring both positive and negative values of the coupling parameter $\xi$. As matter source, a complex, massive scalar field with and without self-interaction terms is considered. Our results show that the existence curves of boson stars in Palatini $f(\mathcal{R})$ gravity are fairly similar to those found in general relativity. Major differences are observed for negative values of the coupling parameter which results in a repulsive gravitational component for high enough scalar field density distributions. Adding self-interactions makes the degeneracy between $f(\mathcal{R})$ and general relativity even more pronounced, leaving very little room for observational discrimination between the two theories., Comment: 16 pages, 10 figures, RevTex4-1

Scalar bosonic stars (BSs) stand out as a multi-purpose model of exotic compact objects. We enlarge the landscape of such (asymptotically flat, stationary, everywhere regular) objects by considering multiple fields (possibly) with different frequencies. This allows for new morphologies ${\it and}$ a stabilization mechanism for different sorts of unstable BSs. First, any odd number of complex fields, yields a continuous family of BSs departing from the spherical, equal frequency, $\ell-$BSs. As the simplest illustration, we construct the $\ell$ = ${\it 1}$ ${\it BSs}$ ${\it family}$, that includes several single frequency solutions, including even parity (such as spinning BSs and a toroidal, static BS) and odd parity (a dipole BS) limits. Second, these limiting solutions are dynamically unstable, but can be stabilized by a ${\it hybrid}$-$\ell$ construction: adding a sufficiently large fundamental $\ell=0$ BS of another field, with a different frequency. Evidence for this dynamical robustness is obtained by non-linear numerical simulations of the corresponding Einstein-(complex, massive) Klein-Gordon system, both in formation and evolution scenarios, and a suggestive correlation between stability and energy distribution is observed. Similarities and differences with vector BSs are anticipated., Comment: 8 pages, 8 figures. Revised version, more references

Multiplayer Online Battle Area (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial Intelligence and Computational Intelligence in Games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both, MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2) to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.se of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game., Comment: 6 pages

We present stationary solutions of geometrically thick discs (or tori) endowed with a self-consistent toroidal magnetic field distribution surrounding a non-rotating black hole in an analytical, static, spherically-symmetric $f(R)$-gravity background. These $f(R)$-gravity models introduce a Yukawa-like modification to the Newtonian potential, encoded in a single parameter $\delta$ which controls the strength of the modified potential and whose specific values affect the disc configurations when compared to the general relativistic case. Our models span different magnetic field strengths, from purely hydrodynamical discs to highly magnetized tori. The characteristics of the solutions are identified by analyzing the central density, mass, geometrical size, angular size, and the black hole metric deviations from the Schwarzschild space-time. In the general relativistic limit ($\delta=0$) our models reproduce previous results for a Schwarzschild black hole. For small values of the $\delta$ parameter, corresponding to $\sim 10\%$ deviations from general relativity, we find variations of $\sim 2 \%$ in the event horizon size, a $\sim 5\%$ shift in the location of the inner edge and center of the disc, while the outer edge increases by $\sim 10\%$. Our analysis for $|\delta|>0.1$, however, reveals notable changes in the black hole space-time solution which have a major impact in the morphological and thermodynamical properties of the discs. The comparison with general relativity is further investigated by computing the size of the photon ring produced by a source located at infinity. This allows us to place constraints on the parameters of the $f(R)$-gravity model based on the Event Horizon Telescope observations of the size of the light ring in M87 and SgrA$^*$., Comment: 14 pages, 7 figures, 2 tables. Version accepted for publication in PRD

We present stationary solutions of magnetized, viscous thick accretion disks around a Schwarzschild black hole. We assume that the tori are not self-gravitating, are endowed with a toroidal magnetic field, and obey a constant angular momentum law. Our study focuses on the role of the black hole curvature in the shear viscosity tensor and in their potential combined effect on the stationary solutions. Those are built in the framework of a causality-preserving, second-order gradient expansion scheme of relativistic hydrodynamics in the Eckart frame description which gives rise to hyperbolic equations of motion. The stationary models are constructed by numerically solving the general relativistic momentum conservation equation using the method of characteristics. We place constraints in the range of validity of the second-order transport coefficients of the theory. Our results reveal that the effects of the shear viscosity and curvature are particularly noticeable only close to the cusp of the disks. The surfaces of constant pressure are affected by viscosity and curvature and the self-intersecting isocontour - the cusp - moves to smaller radii (i.e. towards the black hole horizon) as the effects become more significant. For highly magnetized disks the shift in the cusp location is smaller. Our findings might have implications on the dynamical stability of constant angular momentum tori which, in the inviscid case, are affected by the runaway instability., Comment: 14 pages, 7 figures, some comments and references are added, version accepted for publication in PRD

The eventual detection of gravitational waves from core-collapse supernovae (CCSN) will help improve our current understanding of the explosion mechanism of massive stars. The stochastic nature of the late post-bounce gravitational wave signal due to the non-linear dynamics of the matter involved and the large number of degrees of freedom of the phenomenon make the source parameter inference problem very challenging. In this paper we take a step towards that goal and present a parameter estimation approach which is based on the gravitational waves associated with oscillations of proto-neutron stars (PNS). Numerical simulations of CCSN have shown that buoyancy-driven g-modes are responsible for a significant fraction of the gravitational wave signal and their time-frequency evolution is linked to the physical properties of the compact remnant through universal relations, as demonstrated in [1]. We use a set of 1D CCSN simulations to build a model that relates the evolution of the PNS properties with the frequency of the dominant g-mode, which is extracted from the gravitational-wave data using a new algorithm we have developed for our study. The model is used to infer the time evolution of a combination of the mass and the radius of the PNS. The performance of the method is estimated employing simulations of 2D CCSN waveforms covering a progenitor mass range between 11 and 40 solar masses and different equations of state. Considering signals embedded in Gaussian gravitational wave detector noise, we show that it is possible to infer PNS properties for a galactic source using Advanced LIGO and Advanced Virgo data at design sensitivities. Third generation detectors such as Einstein Telescope and Cosmic Explorer will allow to test distances of ${\cal O}(100\, {\rm kpc})$.