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We present the results of a detailed study on the detectability of the High Frequency Feature (HFF) in core-collapse supernova (CCSN) gravitational wave (GW) signals. We applied Residual Neural Networks (ResNet50), one of the state-of-the-art deep learning architectures in computer vision, to perform a multi-class classification of image samples built from time-frequency Morlet wavelet scalograms of LIGO-Virgo noise plus CCSN GW signals. We consider three target labels for three consecutive and mutually exclusive intervals in which the (first-order) slope of the HFF can be located. We optimized, trained, and tested the ResNet50 model with phenomenological waveforms. Next, we tested the optimized ResNet50 model with GW signals from CCSN simulations. At galactic distances of $1$Kpc and $5$Kpc with H1 and L1 data and $1$Kpc with V1 data, we obtained highly accurate results (test accuracies from $0.8933$ to $0.9867$), which show the feasibility of our methodology. In the case of further distances, we observed declines in test performance until $0.8000$ with H1 and L1 data at $10$Kpc and until $0.5933$ with V1 data at $10$Kpc. Without assuming the continuity and/or discontinuity of the HFF slope values, our methodology is general enough to address, at an early stage, the characterization of the HFF., Comment: 40 pages, 12 figures, 5 tables

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting., Comment: 24 pages, 15 figures, accepted by PRD

We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events using as features a set of reconstruction parameters provided by cWB. Detected noise events are discarded yielding to a reduction of the false alarm rate (FAR) and of the false alarm probability (FAP) thus enhancing of the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is learned using a dataset of noise and signal events, and then it is used to identify and discard noise events in cWB analyses. Noise and signal reduction levels were examined in single detector networks (L1 and H1) and two detector network (L1H1). The FAR was reduced by a factor of $\sim10$ to $\sim100$, there was an enhancement in the statistical significance of $\sim1$ to $\sim2\sigma$, while there was no impact in detection efficiencies.

In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. As novel contribution, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. Resampling is performed by repeated $k$-fold cross-validation experiments, and for a white-box approach, behavior of CNNs is mathematically described in detail. Through a Morlet wavelet transform, strain time series are converted to time-frequency images, which in turn are reduced before generating input datasets. Moreover, to reproduce more realistic experimental conditions, we worked only with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand. After hyperparameter adjustments, we found that resampling smooths stochasticity of mini-batch stochastic gradient descend by reducing mean accuracy perturbations in a factor of $3.6$. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers. However, applying a post-analysis, we found that for GW signals of SNR $\geq 21.80$ with H1 data and SNR $\geq 26.80$ with L1 data, CNNs could remain as tentative alternatives for detecting GW signals. Besides, with receiving operating characteristic curves we found that CNNs show much better performances than those of Naive Bayes and Support Vector Machines models and, with a significance level of $5\%$, we estimated that predictions of CNNs are significant different from those of a random classifier. Finally, we elucidated that performance of CNNs is highly class dependent because of the distribution of probabilistic scores outputted by the softmax layer., Comment: 29 pages, 28 figures

Motivated by the goal for high accuracy modeling of gravitational radiation emitted by isolated systems, recently, there has been renewed interest in the numerical solution of the hyperboloidal initial value problem for Einstein's field equations in which the outer boundary of the numerical grid is placed at null infinity. In this article, we numerically implement the tetrad-based approach presented in [J.M. Bardeen, O. Sarbach, and L.T. Buchman, Phys. Rev. D 83, 104045 (2011)] for a spherically symmetric, minimally coupled, self-gravitating scalar field. When this field is massless, the evolution system reduces to a regular, first-order symmetric hyperbolic system of equations for the conformally rescaled scalar field which is coupled to a set of singular elliptic constraints for the metric coefficients. We show how to solve this system based on a numerical finite-difference approximation, obtaining stable numerical evolutions for initial black hole configurations which are surrounded by a spherical shell of scalar field, part of which disperses to infinity and part of which is accreted by the black hole. As a non-trivial test, we study the tail decay of the scalar field along different curves, including one along the marginally trapped tube, one describing the world line of a timelike observer at a finite radius outside the horizon, and one corresponding to a generator of null infinity. Our results are in perfect agreement with the usual power-law decay discussed in previous work. This article also contains a detailed analysis for the asymptotic behavior and regularity of the lapse, conformal factor, extrinsic curvature and the Misner-Sharp mass function along constant mean curvature slices., Comment: Updated version which includes a discussion of the Newman-Penrose scalar and which has some typos corrected. 33 pages, 19 figures, to appear in Physical Review D

We study spherical and nonspherical linear acoustic perturbations of the Michel flow, which describes the steady radial accretion of a perfect fluid into a nonrotating black hole. The dynamics of such perturbations are governed by a scalar wave equation on an effective curved background geometry determined by the acoustic metric, which is constructed from the spacetime metric and the particle density and four-velocity of the fluid. For the problem under consideration in this article the acoustic metric has the same qualitative features as an asymptotically flat, static and spherically symmetric black hole, and thus it represents a natural astrophysical analogue black hole. As for the case of a scalar field propagating on a Schwarzschild background, we show that acoustic perturbations of the Michel flow exhibit quasi-normal oscillations. Based on a new numerical method for determining the solutions of the radial mode equation, we compute the associated frequencies and analyze their dependency on the radii of the event and sonic horizons and the angular momentum number. Our results for the fundamental frequencies are compared to those obtained from an independent numerical Cauchy evolution, finding good agreement between the two approaches. When the radius of the sonic horizon is large compared to the event horizon radius, we find that the quasi-normal frequencies scale approximately like the surface gravity associated with the sonic horizon., Comment: 20 pages, 12 figures. Typos corrected. New subsection V.C discussing the eikonal limit. Matches published version