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We present a neural network classification model for detecting the presence of hyperonic degrees of freedom in neutron stars. The models take radii and/or tidal deformabilities as input and give the probability for the presence of hyperons in the neutron star composition. Different numbers of observations and different levels of uncertainty in the neutron star properties are tested. The models have been trained on a dataset of well-calibrated microscopic equations of state of neutron star matter based on a relativistic mean-field formalism. Real data and data generated from a different description of hyperonic matter are used to test the performance of the models., Comment: 13 pages, 8 figures

The composition of the core of neutron stars is still under debate. One possibility is that because of the high densities reached in their cores, matter could be deconfined into quark matter. The possible existence of hybrid stars is investigated using microscopic models to describe the different phases of matter. Within these microscopic models we aim at calculating the properties of neutron stars and matter. We want to probe the influence of pQCD calculations and analyze the properties that identify a transition to deconfined matter. A Bayesian approach using a Markov Chain Monte Carlo sampling process is applied to generate 8 sets of equations of state. A Maxwell construction describes the deconfinement transition. For the hadronic phase, we consider a stiff and a soft EOS obtained from the Relativistic Mean Field model with non-linear meson terms. For the quark phase, we use two different models: the NJL model with multiquark interactions and the Mean Field Theory of QCD, similar to the MIT bag model with a vector term. The model parameters were determined by Bayesian inference imposing observations from NICER and the phase transition density range. We have also applied restrictions from the pQCD calculations. Hybrid stars are compatible with current observational data. The restrictions of pQCD reduce the value of the maximum mass. However, even when applying this restriction, the models were able to reach 2.1 to 2.3 solar masses. The conformal limit was not attained at the center of the most massive stars. The vector interactions are essential to describe hybrid stars with a mass above two solar masses. The multiquark interactions affect the limits of some quantities considered as indicators of the presence of a deconfined phase. It is possible to find a set of EOS, that predict that inside NS the renormalized matter trace anomaly is always positive., Comment: There are 17 pages, 11 figures

We analyse the implications of information about local derivatives from the mass-radius diagram in neutron star matter. It is expected that the next generation of gravitational wave and electromagnetic detectors will allow the determination of the neutron star radius and mass with a small uncertainty. Observations of neutron stars clustered around a given neutron star mass allow the estimation of local derivatives in the $M(R)$ diagram, which can be used to constrain neutron star properties. From a model-independent description of the neutron star equation of state, it is shown that a $M(R)$ curve with a negative slope at 1.4$M_\odot$ predicts a $2M_\odot$ neutron star radius below 12 km. Furthermore, a maximum mass below 2.3$M_\odot$ is obtained if the $M(R)$ slope is negative in the whole range of masses above $1M_\odot$, and a maximum mass above 2.4$M_\odot$ requires the $M(R)$ slope to be positive in some range of masses. Constraints on the mass-radius curve of neutron stars will place strong constraints on microscopic models., Comment: 10 pages, 11 figures, title changed, published version

Interactive narratives offer a novel approach to presenting business process models, making them more accessible and collaborative. These narratives create a hyper-textual environment that facilitates knowledge exchange and comprehension for ordinary individuals. However, designing such narratives is complex, as business process modelers must accurately identify and translate the graphic elements of a process model into dynamic narrative elements. This research paper introduces the Scripting Your Process (SYP) method, which provides a systematic approach to designing interactive narratives based on business process models. Following the principles of Design Science Research (DSR), a quasi-experimental study demonstrates and evaluates the SYP method. The results show that the SYP method successfully achieves its objective, contributing to the systematic design of interactive narratives derived from business process models. Consequently, individuals who are not experts in business process management can understand these processes in an engaging and gameful manner., Comment: 18 pages. Obs.: This paper has already been accepted in the International Journal of Business Information Systems (IJBIS) and it is in the journal publication schedule. This paper is the \underline{Author's Original and personal} version

We present a new inference framework for neutron star astrophysics based on conditional variational autoencoders. Once trained, the generator block of the model reconstructs the neutron star equation of state from a given set of mass-radius observations. While the pressure of dense matter is the focus of the present study, the proposed model is flexible enough to accommodate the reconstructing of any other quantity related to dense matter equation of state. Our results show robust reconstructing performance of the model, allowing to make instantaneous inference from any given observation set. The present framework contrasts with computationally expensive evaluation of the equation-of-state posterior probability distribution based on Markov chain Monte Carlo methods., Comment: 9 pages, 7 figures

We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106.065802} to detect the presence of a large (destabilising) dense matter phase transition in neutron star (NS) observations of masses and radii, and relate the feasibility of detection with parameters of the underlying mass-radius sequence, which is a functional of the dense matter equation of state. Once trained on simulated data featuring continuous $M(R)$ solutions (i.e., no phase transitions), NF is used to determine the likelihood of a first-order phase transition in a given set of $M(R)$ observations featuring a discontinuity, i.e., perform the anomaly detection. Different mock test sets, featuring two branch solutions in the $M(R)$ diagram, were parameterized by the NS mass at which the phase transition occurs, $M_c$, and the radius difference between the heaviest hadronic star and lightest hybrid star, $\Delta R$. We analyze the impact of these parameters on the NF performance in detecting the presence of a first-order phase transition. Among the results, we report that given a set of 15 stars with radius uncertainty of $0.2$ km, a detection of a two-branch solution is possible with 95\% accuracy if $\Delta R > 0.4$ km., Comment: 9 pages, 8 figures

This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the uncertainties associated with its predictions. To simulate different noise levels on the $M(R)$ observations, we create three different sets of mock data. Our results show BNNs as an accurate and reliable tool for predicting the nuclear matter properties whenever the true values are not completely outside the training dataset statistics, i.e., if the model is not heavily dependent on its extrapolating capacities. Using real mass-radius pulsar data, the model predicted, for instance, $L_{\text{sym}}=39.80\pm17.52 $ MeV and $K_{\text{sym}}=-101.67\pm62.86 $ MeV ($2\sigma$ interval). Our study provides a valuable inference framework when new NS data becomes available., Comment: 15 pages, 12 figures

The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a relativistic mean field description of nuclear matter. Different formulations, both with density dependent couplings and with non-linear mesonic terms, are considered and their predictions compared and discussed. A special attention is drawn to the effect on the neutron star properties of the inclusion of exotic degrees of freedom as hyperons. Properties such as the speed of sound, the trace anomaly, the proton fraction and the onset of direct Urca processes inside neutron stars are discussed. The knowledge of the general behavior of the hadronic equation of state and the implication it has on the neutron star properties will allow to identify signatures of a deconfinement phase transition discussed in other studies., Comment: 16 pages, 11 figures

We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS radius and tidal deformability, we leverage BNNs as effective tools for inferring the proton fraction and sound speed within NS interiors. To achieve this, several BNNs models were developed upon a dataset of $\sim$ 25K nuclear EoS within a relativistic mean-field framework, obtained through Bayesian inference that adheres to minimal low-density constraints. Unlike conventional neural networks, BNNs possess an exceptional quality: they provide a prediction uncertainty measure. To simulate the inherent imperfections present in real-world observations, we have generated four distinct training and testing datasets that replicate specific observational uncertainties. Our initial results demonstrate that BNNs successfully recover the composition with reasonable levels of uncertainty. Furthermore, using mock data prepared with the DD2, a different class of relativistic mean-field model utilized during training, the BNN model effectively retrieves the proton fraction and speed of sound for neutron star matter., Comment: 16 pages, 15 figures, published version

The high-density behavior of nuclear matter is analyzed within a relativistic mean-field description with non-linear meson interactions. To assess the model parameters and their output, a Bayesian inference technique is used. The Bayesian setup is limited only by a few nuclear saturation properties, the neutron star maximum mass larger than 2 M$_\odot$, and the low-density pure neutron matter equation of state (EOS) produced by an accurate N$^3$LO calculation in chiral effective field theory. Depending on the strength of the non-linear scalar vector field contribution, we have found three distinct classes of EOSs, each one correlated to different star properties distributions. If the non-linear vector field contribution is absent, the gravitational maximum mass and the sound velocity at high densities are the greatest. However, it also gives the smallest speed of sound at densities below three times saturation density. On the other hand, models with the strongest non-linear vector field contribution predict the largest radii and tidal deformabilities for 1.4 M$_\odot$ stars, together with the smallest mass for the onset of the nucleonic direct Urca processes and the smallest central baryonic densities for the maximum mass configuration. These models have the largest speed of sound below three times saturation density, but the smallest at high densities, in particular, above four times saturation density the speed of sound decreases approaching approximately $\sqrt{0.4}c$ at the center of the maximum mass star. On the contrary, a weak non-linear vector contribution gives a monotonically increasing speed of sound. A 2.75 M$_\odot$ NS maximum mass was obtained in the tail of the posterior with a weak non-linear vector field interaction. We found that pQCD favors models with a large non-linear vector field contribution or hyperons., Comment: There are 17 pages, 16 figures (The typographical error in the supplemental update)

The possible existence of hybrid stars is studied using several multi-quark interaction channels. The hadronic phase consists of an equation of state (EoS) with presently accepted nuclear matter properties and the quark model is constrained by the vacuum properties of several light mesons. The dependence of several NS properties on the different quark interactions is analyzed. We show that the present constraints from neutron star observations allow for the existence of hybrid stars with large strangeness content and large quark cores., Comment: 6 pages, 5 figures; Proceedings of the XVth Quark Confinement and the Hadron Spectrum conference, August 1st - 6th, 2022, University of Stavanger, Norway

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 extraction of the nuclear matter properties from neutron star (NS) observations is nowadays an important issue, in particular, the properties that characterize the symmetry energy which are essential to describe correctly asymmetric nuclear matter. We use deep neural networks (DNNs) to map the relation between cold $\beta$-equilibrium NS matter and the nuclear matter properties. Assuming a quadratic dependence on the isospin asymmetry for the energy per particle of homogeneous nuclear matter and using a Taylor expansion up to fourth order in the iso-scalar and iso-vector contributions, we generate a dataset of different realizations of $\beta$-equilibrium NS matter and the corresponding nuclear matter properties. The DNN model was successfully trained, attaining great accuracy in the test set. Finally, a real case scenario was used to test the DNN model, where a set of 33 nuclear models, obtained within a relativistic mean field approach or a Skyrme force description, were fed into the DNN model and the corresponding nuclear matter parameters recovered with considerable accuracy, in particular, the standard deviations $\sigma(L_{\text{sym}})= 12.85$ MeV and $\sigma(K_{\text{sat}})= 41.02$ MeV were obtained, respectively, for the slope of the symmetry energy and the nuclear matter incompressibility at saturation., Comment: 10 pages, 5 figures; published version

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

The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density dependent relativistic mean field description of nuclear matter. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and the low density pure neutron matter EOS obtained from a precise next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral effective field theory ($\chi$EFT). The posterior distributions of the model parameters obtained under these minimal constraints are employed to construct the distributions of various nuclear matter properties and NS properties such as radii, tidal deformabilites, central energy densities and speeds of sound etc. We found that 90% confidence interval (CI) for allowed NS mass - radius relationship and tidal deformabilites are compatible with GW170817 and recent NICER observations, without invoking the exotic degrees of freedom. A central speed-of-sound of the order of $\sqrt{2/3}$ $c$ is obtained. The maximum neutron star mass allowed by the model is 2.5 $M_\odot$., Comment: 20 pages, 11 figures (Accepted in ApJ)

We analyze the uncertainties introduced in the determination of the neutron star matter proton fraction, in a range of densities close to the saturation density, if the cold $\beta$-equilibrium neutron star matter equation of state (EoS) is known. In particular, we discuss the effect of neglecting the muon contribution and of considering that the energy density of nuclear matter is well described by taking only terms until second order in the proton-neutron asymmetry. It is shown that two types of uncertainties may be associated with the extraction of the symmetry energy from the $\beta$-equilibrium equation of state: an overestimation if terms above the parabolic approximation on the asymmetry parameter are neglected, or an underestimation if the muon contribution is not considered. The effect of the uncertainty on the symmetric nuclear matter EoS on the determination of the proton fraction is discussed. It could be shown that the neutron star mass-radius curve is sensitive to the parabolic approximation on the asymmetry parameter., Comment: 13 pages, 8 figures, published version

We determine, within a meta-model, the properties of the nuclear matter equation of state (EoS) that allow for a phase transition to deconfinement matter. It is shown that the properties that define the isoscalar channel are the ones that are affected, in particular, a phase transition implies much larger values of the skewness and kurtosis. The effect of multi-quark interaction channels in the description of the quark phase in hybrid stars is also studied. NS properties, such as the mass and radius of the quark core, show an interplay dependence between the 8-quark vector and the 4-quark isovector-vector interactions. We show that low mass NS, $M\sim 1.4 M_\odot$, may already contain a quark core, and satisfy all existing NS observational constraints. We discuss the strangeness content of the quark core and its influence on the speed of sound., Comment: 8 pages, 5 figures, Contribution to the Virtual Tribute to Quark Confinement and the Hadron Spectrum 2021 conference proceedings (vConf21)

The low density nuclear matter equation of state is strongly constrained by nuclear properties, however, for constraining the high density equation of state it is necessary to resort to indirect information obtained from the observation of neutron stars, compact objects that may have a central density several times nuclear matter saturation density, $n_0$. Taking a meta-modelling approach to generate a huge set of equation of state that satisfy nuclear matter properties close to $n_0$ and that do not contain a first order phase transition, the possibility of constraining the high density equation of state was investigated. The entire information obtained from the GW170817 event for the probability distribution of $\tilde{\Lambda}$ was used to make a probabilistic inference of the EOS, which goes beyond the constraints imposed by nuclear matter properties. Nuclear matter properties close to saturation, below $2n_0$, do not allow us to distinguish between equations of state that predict different neutron star (NS) maximum masses. This is, however, not true if the equation of state is constrained at low densities by the tidal deformability of the NS merger associated to GW170817. Above $3n_0$, differences may be large, for both approaches, and, in particular, the pressure and speed of sound of the sets studied do not overlap, showing that the knowledge of the NS maximum mass may give important information on the high density EOS. Narrowing the maximum mass uncertainty interval will have a sizeable effect on constraining the high density EOS., Comment: 11 pages, 6 figures

The effect of temperature on the crust-core transition of a magnetar is studied. The thermodynamical spinodals are used to calculate the transition region within a relativistic mean-field approach for the equation of state. Magnetic fields with intensities $5\times 10^{16}$ G and $5\times 10 ^{17}$ G are considered. It is shown that the effect on the extension of the crust-core transition is washed away for temperatures above $10^{9}$ K for magnetic field intensities $ \lesssim 5\times 10^{16}$ G but may still persist if a magnetic field as high as $5\times 10 ^{17}$G is considered. For temperatures below that value, the effect of the magnetic field on crust-core transition is noticeable and grows as the temperature decreases and, in particular, it is interesting to identify the existence of disconnected non-homogeneous matter above the $B=0$ crust core transition density. Models with different symmetry energy slopes at saturation show quite different behaviors. In particular, a model with a large slope, as suggested by the recent results of PREX-2, predicts the existence of up to four disconnected regions of non-homogeneous matter above the zero magnetic field crust-core transition density., Comment: 9 pages, 5 figures, published version; Topical Issue "The QCD Phase Diagram in Strong Magnetic Fields"

We analyze the impact of several multi-quark interaction channels on the properties of hybrid stars. Four and eight vector interactions were included in the $SU(3)$ NJL model to describe the quark matter, and all vector like interactions were investigated. The hybrid stars were built starting from an hadronic EoS that satisfy presently accepted nuclear matter properties and considering the quark model constrained by the vacuum properties of several light mesons. The interplay between the 8-quark vector interaction and the 4-quark isovector-vector interaction determine the size of the quark core and its strangeness content. The inclusion of a isovector-vector interaction is essential to obtain large $s$-quark contents and a smaller central speed of sound, although this interaction pushes the onset of quarks to larger densities. It is observed that low mass stars, with a mass below 1.4$M_\odot$, may contain a quark core but with small strangeness content. It is shown that the presently existing mass and tidal deformability constraints from NS observations allow for the existence of hybrid stars with a large strangeness content and large quark cores for binary mass ratios $q=M_2/M_1\gtrsim 0.85$., Comment: 12 pages, 8 figures; matches the published version