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The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multimessenger observations. As GWs from binary neutron star (BNS) mergers can spend up to 10–15 minutes in the frequency bands of the detectors at design sensitivity, early-warning alerts and premerger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present premerger BNS sky localization results using GW-SkyLocator, a deep-learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model’s performance on a catalog of simulated injections from Sachdev, recovered at 0–60 s before the merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for premerger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.

We present a rapid and reliable deep-learning-based method for gravitational-wave (GW) signal reconstruction from elusive, generic binary black hole mergers in LIGO data. We demonstrate that our model, AWaRe, effectively recovers GWs with parameters it has not encountered during training. This includes features like higher black hole masses, additional harmonics, eccentricity, and varied waveform systematics, which introduce complex modulations in the waveform’s amplitudes and phases. The accurate reconstructions of these unseen signal characteristics demonstrate AWaRe's ability to handle complex features in the waveforms. By directly incorporating waveform reconstruction uncertainty estimation into the AWaRe framework, we show that for real GW events, the uncertainties in AWaRe's reconstructions align closely with those achieved by benchmark algorithms like BayesWave and coherent WaveBurst. The robustness of our model to real GW events and its ability to extrapolate to unseen data open new avenues for investigations in various aspects of GW astrophysics and data analysis, including tests of general relativity and the enhancement of current GW search methodologies.

Gravitational wave (GW) observations by ground-based detectors such as LIGO and Virgo have transformed astrophysics, enabling the study of compact binary systems and their mergers. However, transient noise artifacts, or “glitches,” pose a significant challenge, often obscuring or mimicking signals and complicating their analysis. In this work, we extend the Attention-boosted Waveform Reconstruction network (AWaRe) to address glitch mitigation, demonstrating its robustness in reconstructing waveforms in the presence of real glitches from LIGO’s third observing run. Without requiring explicit training on glitches, AWaRe accurately isolates GW signals from data contaminated by glitches spanning a wide range of amplitudes and morphologies. We evaluate this capability by investigating the events GW191109 and GW200129, which exhibit strong evidence of antialigned spins and spin precession, respectively, but may be adversely affected by data quality issues. We find that, regardless of the potential presence of glitches in the data, AWaRe reconstructs both waveforms with high accuracy. Additionally, we perform a systematic study of AWaRe’s performance on a simulated catalog of injected waveforms in real LIGO glitches and obtain reliable reconstructions of the waveforms. By subtracting the AWaRe reconstructions from the data, we show that the resulting residuals closely align with the background noise that the waveforms were injected in. The robustness of AWaRe in mitigating glitches, despite being trained exclusively on GW signals and not explicitly on glitches, highlights its potential as a powerful tool for improving the reliability of searches and characterizing noise artifacts.

Gravitational-wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template-based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by nonstationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher-order waveform harmonics, focusing solely on the leading-order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate-mass black hole binaries and hierarchical mergers. Here we show, for the first time, that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher-order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run (2019–2020) achieved a high degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational-wave events, our model's reconstructions achieved between 85% and 98% overlap with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries.

The mergers of neutron star–neutron star and neutron star–black hole binaries (NSBHs) are the most promising gravitational wave (GW) events with electromagnetic (EM) counterparts. The rapid detection, localization, and simultaneous multimessenger follow-up of these sources are of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt EM counterparts during binary mergers can last less than 2 s, the timescales of existing localization methods that use Bayesian techniques, vary from seconds to days. In this paper, we propose the first deep learning–based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star–neutron star and NSBHs for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from GW searches to obtain sky direction posteriors in around 1 s using a single P100 GPU, which is several orders of magnitude faster than full Bayesian techniques.

The LIGO–Virgo–KAGRA Collaboration recently reported GW231123, a black hole merger with total mass of around 190–265 M⊙. This event adds to the growing evidence of “lite” intermediate mass black hole (IMBH) discoveries of postmerger black holes ≳100 M⊙. GW231123 posed several data analysis challenges owing to waveform-model systematics and the presence of noise artifacts called glitches. We present the first comprehensive machine learning analysis to further validate this event, strengthen its astrophysical inference, and characterize instrumental noise in its vicinity. Our approach uses a combination of tools tailored for specific analyses: GW-Whisper, an adaptation of OpenAI’s audio transformer; ArchGEM, a Gaussian mixture model–based soft clustering and density approximation software; and AWaRe, a convolutional autoencoder. We identify the data segment containing the merger with >70% confidence in both detectors and verify its astrophysical origin. We then characterize the scattered light glitch around the event, providing the first physically interpretable parameters for the glitch. We also reconstruct the real waveforms from the data with slightly better agreement to model-agnostic reconstructions than to quasi-circular models, hinting at possible astrophysics beyond current waveform families (such as noncircular orbits or environmental imprints). Finally, by demonstrating high-fidelity waveform reconstructions for simulated mergers with total masses between 100 and 1000 M⊙, we show that our method can confidently probe the IMBH regime. Our integrated framework offers a powerful complementary tool to traditional pipelines for rapid, robust analysis of massive, glitch-contaminated events.

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.

Keyword extraction is a major step to extract plenty of valuable and meaningful information from the rich source of World Wide Web (W.W.W.). Different keyword extraction algorithms are proposed with their own advantages and disadvantages. Vector Space Model (VSM) algorithms prove quite effective for keyword extraction, but do not emphasize on the class label information of classified data. Supervised Term Weighting (STW) algorithms address this problem, but suffer from high dimensionality. Besides, they do not incorporate semantic relationship between terms. To address these problems, Graph Based Models (GBM) are introduced. However, they also use unsupervised learning. Hence, this paper proposes a Keyword Extraction using Supervised Cumulative TextRank (KESCT) technique that explores the benefits of both VSM and GBM techniques. The proposed algorithm modifies TextRank by incorporating a novel Unique Statistical Supervised Weight (USSW) to include class label information of classified data. To emphasize on the relatedness between terms, the mutual information between terms is also included. The proposed algorithm is validated using four review datasets and results are compared with traditional TextRank and its variants using Support Vector Machine (SVM) classifier, Naïve-Bayes (NB) classifier and an ensemble classifier. Experimental results mark the efficacy of the proposed algorithm over existing algorithms.

Recent LIGO-Virgo-KAGRA (LVK) analyses have revealed complex structure in the binary black hole (BBH) population, including distinct features in the primary mass spectrum and nontrivial spin-mass correlations. However, the phenomenological models used to capture these features often lack analytic transparency, making it difficult to isolate robust physical laws from modeling artifacts. To address this, symbolic regression is applied to the posterior inference products of the GWTC-4 catalog, discovering compact, closed-form analytic expressions for four key population relationships: (i) the merger-rate evolution with redshift; (ii) the mass-ratio dependence of the effective-spin distribution; (iii) the redshift evolution of the effective-spin distribution; and (iv) the conditional mass-ratio distributions associated with the 10 solar mass and 35 solar mass primary mass peaks. This framework successfully compresses both rigid and highly flexible models into differentiable phenomenological laws, dynamically recovering a consistent low-redshift merger-rate slope without assuming an a priori power-law form. The exact analytic derivatives provided by symbolic regression show that the mass ratio--effective spin and redshift--effective spin correlations are robustly driven by broadening of the posterior widths rather than shifts in the mean. Furthermore, qualitatively distinct functional forms for the mass-ratio distributions conditioned on the 10 solar mass and 35 solar mass primary mass peaks are identified. These closed-form expressions enable exact analytic gradient diagnostics and compact surrogate summaries, particularly for flexible numerical posteriors that are not otherwise available in low-dimensional analytic form. They also facilitate rapid downstream calculations for rate forecasting, formation channel comparison, and stochastic background estimation.

The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multi-messenger observations. Since GWs from binary neutron star (BNS) mergers can spend up to 10-15 mins in the frequency bands of the detectors at design sensitivity, early warning alerts and pre-merger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present pre-merger BNS sky localization results using CBC-SkyNet, a deep learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model's performance on a catalog of simulated injections from Sachdev et al. (2020), recovered at 0-60 secs before merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for rapid pre-merger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.