arXiv subjects

Javier M. Antelis

Publications and source records attributed to Javier M. Antelis.

17 recordsLinked to original sources

arXiv2026

Classifying the nuclear equation of state in LVK interferometric noise through core-collapse supernova gravitational-wave signatures using convolutional neural networks

This paper presents a convolutional neural network (CNN) approach to classifying the nuclear equation of state (EOS). As illustrative examples, we use five two-dimensional core-collapse supernova (CCSN) simulations that differ only in their EOS. We analyze estimates of the initial slope of the high-frequency feature (HFF) reconstructed in real interferometric data from the O3b LIGO-Virgo-KAGRA (LVK) observing run at Galactic source distances of 1, 5, and 10 kpc. The CNN classifier achieves an overall accuracy of 98.58% at 1 kpc and 52.43% at 5 kpc. At 10 kpc, its ability to distinguish among the EOS classes is effectively lost. The successful EOS classification at 1 kpc suggests that this approach may be scalable to next-generation observatories. The expected order-of-magnitude sensitivity improvements of Cosmic Explorer and the Einstein Telescope could enable comparable classification performance at approximately ten times the current distance. More detailed performance metrics, including the macro-averaged one-vs-rest (OvR) area under the curve (AUC), yield values of 0.97 and 0.98 at 1 kpc. These results indicate strong classification performance both across the complete set of EOS classes and for the individual classes.

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arXiv2026

Joint Detection and Characterization of the Standing Accretion Shock Instability for Core-Collapse Supernovae with cWB XP

The most sensitive to-date multimessenger detection of the standing accretion shock instability in real interferometric data is presented, which quantitatively identifies the presence of the SASI in core-collapse supernovae using neutrino and gravitational-wave (GW) signals. In the GW channel, the coherent WaveBurst (cWB) software on its version XP is implemented, among with real LIGO data from the O3 and O4 observing runs. With this, a more accurate estimation of parameters, such as the central frequency and signal duration, is obtained for both sets of data. The SASI identification probability versus false alarm rates is presented in the form of Receiver Operating Characteristic (ROC) curves. For O3, the new study for the combined GW and neutrino detection condition, labeled as $x + y$, shows an identification probability (previous best results from Lin et al. [1]) of 1 (1), 0.90 (0.70) and 0.37 (0.34) at 1, 5 and 10 kpc for a false identification probability of 0.10. On the other hand, using O4 shows that the GW channel by itself is sensitive enough to provide almost perfect identification probability scores, with identification probability values of 1, 0.99 and 0.97 for a false identification probability of 0.01 at 1, 5 and 10 kpc, respectively.

astro-ph.HE↗

arXiv2026

Quantification of the parameter estimation error from Rotating Core Collapse supernovae

In this paper, we perform parameter estimation with an analytical model to simulate the gravitational wave emission during the core bounce phase of a rapidly rotating core collapse supernova progenitor. This approach enables us to estimate the parameter $β$, defined as the ratio of rotational kinetic energy to gravitational potential energy in core collapse supernovae. To verify the reliability of both the analytical model and the inferred value of $β$, we use a numerical template bank constructed from Abylkairovś gravitational waveform catalog and simulate O4 noise, characterized by the interferometers power spectral density. An average fitting factor of 94\% over the interval 0.02 $< β<$ 0.14 shows that our analytical model reproduces the key characteristics of the core-bounce waveform with high accuracy, leading to only a 6\% reduction in the optimal signal to noise ratio. This provides a quantitative measure of how well the analytical model performs. Subsequently, we analyze the error in estimating $β$ using a Matched Filter method and compare it to the corresponding Cramér Rao Lower Bound. The results obtained by considering noise and waveforms at distances of 5, 10, and 50 kpc enable an assessment of how accurately the selected statistical model fits the observed data. From the asymptotic expansion of the variance, we derive a theoretical lower bound for the error that falls below $10^{-1}$ when the parameter $β$ decreases with distance.

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arXiv2026

Bayesian parameter estimation for the Core-bounce phase of Rapidly Rotating Core-Collapse Supernovae in real interferometric data

We present a novel methodology to estimate the ratio of kinetic to gravitational potential energy in core-collapse supernova progenitors and to assess the equation of state (EOS) using gravitational-wave signals from the core-bounce phase of rapidly rotating stars in real interferometric data. We extend a previous phenomenological model by introducing an additional parameter that captures the signal timescale. The agreement between our template and numerical waveform databases is evaluated through fitting factors and Bayesian model comparison, also assessing consistency across datasets. The improved model increases the median fitting factor from 88.88% to 90.83%. Parameter estimation is performed via Markov Chain Monte Carlo using real O3aL1 noise. For 452 simulated signals, the rotational parameter $β$ is recovered with a median relative error of 11.93% (95th percentile: 38.41%) and an uncertainty of $σ_β= 1.083 \times 10^{-3}$ at 10 kpc, improving over previous matched-filtering results. We further analyze the impact of prior choices and noise properties, finding that real interferometric noise introduces biases up to 11.9%, while optimized priors can reduce them to 0.6%.

astro-ph.HE↗

arXiv2025

Residual neural networks to classify the high frequency emission in core-collapse supernova gravitational waves

We present a new methodology to explore the morphology of the High Frequency Feature (HFF), i.e., the dominant, rising-frequency GW emission from a proto-neutron star in core-collapse supernovae (CCSNe). We used a residual neural network (ResNet50) to perform multi-class classification of image samples constructed from time-frequency Morlet wavelet scalograms. We defined a three-class problem by categorizing the HFF slope as Steep, Moderate, or Low, according to physically informed ranges. The ResNet50 model was optimized with phenomenological waveforms injected into real noise from the LIGO-Virgo O3b observing run and then tested with numerically simulated CCSN waveforms embedded in the same real noise. 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. For further distances, we observed declines in test accuracy until 0.8000 with H1 and L1 data at 10 kpc and until 0.5933 with V1 data at 10 kpc, which we attribute to limitations in the input datasets. Our methodology is sufficiently general to enable early-stage characterization of the HFF in real interferometric data.

astro-ph.HE↗

arXiv2024

On the detectability of gravitational waves emitted from head-on collisions of $\ell$-boson stars

In this work, we investigate head-on collisions of $\ell$-boson stars, potential candidates for dark matter compact objects. We begin with a review of the general properties and features of these stars, leveraging results from prior studies to analyze the gravitational wave signals generated by such collisions. Considering a maximum distance of 100 Mpc for potential events, we identify the range of masses and scalar field frequencies for these stars that would render the gravitational waves detectable by current gravitational wave observatories. Additionally, we process the resulting signals to generate simulated observatory images, highlighting their similarities and differences compared to those produced by black hole collisions.

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arXiv2024

Dependence of the Reconstructed Core-Collapse Supernova Gravitational Wave High-Frequency Feature on the Nuclear Equation of State, in Real Interferometric Data

We present an analysis of gravitational wave (GW) predictions from five two-dimensional Core Collapse Supernova (CCSN) simulations that varied only in the Equation of State (EOS) implemented. The GW signals from these simulations are used to produce spectrograms in the absence of noise, and the emergent high-frequency feature (HFF) is found to differ quantitatively between simulations. Below 1 kHz, the HFF is well approximated by a first-order polynomial in time. The resulting slope was found to vary between 10-50% across all models. Further, using real interferometric noise we investigated the current capabilities of GW detectors to resolve these differences in HFF slope for a Galactic CCSN. We find that for distances up to 1 kpc, current detectors can resolve HFF slopes that vary by at least 30%. For further Galactic distances, current detectors are capable of distinguishing the upper and lower bounds of the HFF slope for groupings of our models that varied in EOS. With the higher sensitivity of future GW detectors, and with improved analysis of the HFF, our ability to resolve properties of the HFF will improve for all Galactic distances. This study shows the potential of using the HFF of CCSN produced GWs to provide insight into the physical processes occurring deep within CCSN during collapse, and in particular its potential to further constrain the EOS through GW detection.

astro-ph.HE↗

arXiv2024

Parameter estimation from the core-bounce phase of rotating core collapse supernovae in real interferometer noise

In this work we propose an analytical model that reproduces the core-bounds phase of gravitational waves (GW) of Rapidly Rotating (RR) from Core Collapse Supernovae (CCSNe), as a function of three parameters, the arrival time $τ$, the ratio of the kinetic and potential energy $β$ and a phenomenological parameter $α$ related to rotation and equation of state (EOS). To validate the model we use 126 waveforms from the Richers catalog \cite{Richers_2017} selected with the criteria of exploring a range of rotation profiles, and involving EOS. To quantify the degree of accuracy of the proposed model, with a particular focus on the rotation parameter $β$, we show that the average Fitting Factor (FF) between the simulated waveforms with the templates is 94.4\%. In order to estimate the parameters we propose a frequentist matched filtering approach in real interferometric noise which does not require assigning any priors. We use the Matched Filter (MF) technique, where we inject a bank of templates considering simulated colored Gaussian noise and the real noise of O3L1. For example for A300w6.00\_BHBLP at 10Kpc we obtain a standar deviation of $σ= 3.34\times 10^{-3}$ for simulated colored Gaussian noise and $σ= 1.46\times 10^{-2}$ for real noise. On the other hand, from the asymptotic expansion of the variance we obtain the theoretical minimum error for $\hatβ$ at 10 kpc and optimal orientation. The estimation error in this case is from $10^{-2}$ to $10^{-3}$ as $β$ increases. We show that the results of the estimation error of $β$ for the 3-parameter space (3D) is consistent with the single-parameter space (1D), which allows us to conclude that $β$ is decoupled from the others two parameters.

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arXiv2024

An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the Third Observing Run of Advanced LIGO and Advanced Virgo

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.

astro-ph.HE↗

arXiv2024

Generating tailored high frequency features in core collapse supernova gravitational wave signals applicable in LIGO interferometric studies

In this article, we introduce a methodology based on an analytical model of a damped harmonic oscillator subject to random forcing to generate transient gravitational wave signals. Such a model incorporates a simulated linear high-frequency component that mirrors the growing characteristic frequency over time observed in numerical simulations of core-collapse supernova gravitational wave signals. Unlike traditional numerical simulations, the method proposed in this study requires minimal computational resources, which makes it particularly advantageous for tasks such as data analysis, detection, and reconstruction of gravitational wave transients. To verify the physical accuracy of the generated signals, they are compared against the amplitude spectral of current LIGO interferometers and a 3D numerical simulation of a core-collapse supernova gravitational wave signal from the Andresen et al. 2017 model s15.nr. The results indicate that this approach is effective in generating scalable signals that align with LIGO interferometric data, offering potential utility in various gravitational wave transient investigations.

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arXiv2023

Characterizing the gravitational wave temporal evolution of the gmode fundamental resonant frequency for a core collapse supernova: A neural network approach

We present a methodology based on the implementation of a fully connected neural network to estimate the gravitational wave (GW) temporal evolution of the gmode fundamental resonant frequency for a Core Collapse Supernova (CCSN). To perform the estimation, we construct a training data set, using synthetic waveforms, that serves to train the ML algorithm, and then use several CCSN waveforms to test the model. According to the results obtained from the implementation of our model, we provide numerical evidence to support the classification of progenitors according to their degree of rotation. The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within $13\%$ for the tested candidates included in this study. This method of classification does not require priors or templates, it is based on physical modelling, and can be combined with studies that classify the progenitor with other physical features.

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arXiv2023

A search for distinctive footprints of compact binary coalescence within alternatives theories of gravity

In this review we examine the amplitude intensity associated to tensorial and non-tensorial polarization modes generated by binary systems at their inspiral stage, within the alternative theories of gravity of Brans Dicke, Rosen, and Lightman Lee. This study is performed without making an explicit use of the Transverse Traceless gauge of the General Relativity approach, and at the Newtonian limit. Consequently such additional polarization modes appear (non-tensorial) due to additional degrees of freedom in modified theories of gravitation. We model and compare the different polarization modes and strain signals for each scheme varying the sky location. Our analysis allows us to identify the locations where these modes are more intense, and, therefore susceptible to being identified for the future interferometer detector network. This gives rise to a framework in which the amplitude and the intensity of all polarization modes of general relativity and alternative hypotheses can be compared.

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arXiv2021

Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae

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 $\sim2σ$, while there was no impact in detection efficiencies.

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arXiv2020

Deep learning for gravitational-wave data analysis: A resampling white-box approach

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.

astro-ph.IM↗

arXiv2019

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

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arXiv2018

An independent search of gravitational waves in the first observation run of advanced LIGO using cross-correlation

This work describes a template-free method to search gravitational waves (GW) using data from the LIGO observatories simultaneously. The basic idea of this method is that a GW signal is present in a short-duration data segment if the maximum correlation-coefficient between the strain signals is higher than a significant threshold and its time difference is lower than the 10 ms of inter-observatory light propagation time. Hence, this method can be used to carry out blind searches of any types of GW irrespective of the waveform and of the source type and sky location. An independent search of injected and real GW signals from compact binary coalescences (CBC) contained in the first observation run (O1) of advanced LIGO was carried out to asses its performance. On the basis of the results, the proposed method was able to detect GW produced by binary systems without making any assumption about them.

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arXiv2017

Obtaining Gravitational Waves from Inspiral Binary Systems using LIGO data

The discovery of the astrophysical events GW150926 and GW151226 has experimentally confirmed the existence of gravitational waves (GW) and has demonstrated the existence of binary stellar-mass black hole systems. This finding marks the beginning of a new era that will reveal unexpected features of our universe. This work presents a basic insight to the fundamental theory of GW emitted by inspiral binary systems and describes the scientific and technological efforts developed to measure these waves using the interferometer-based detector called LIGO. Subsequently, the work presents a comprehensive data analysis methodology based on the matched filter algorithm, which aims to recovery GW signals emitted by inspiral binary systems of astrophysical sources. This algorithm was evaluated with freely available LIGO data containing injected GW waveforms. Results of the experiments performed to assess detection accuracy showed the recovery of 85% of the injected GW.

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