arXiv subjects

Zhoujian Cao

Publications and source records attributed to Zhoujian Cao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Effect of kick velocity on gravitational wave detection of binary black holes with space- and ground-based detectors

During the coalescence of binary black holes (BBHs), asymmetric gravitational wave (GW) emission imparts a kick velocity to the remnant black hole, affecting observed waveforms and parameter estimation. In this study, we investigate the impact of this effect on GW observations using space- and ground-based detectors. By applying Lorentz transformations, we analyze waveform modifications due to kick velocities. For space-based detectors, nearly 50% of detected signals require corrections, while for ground-based detectors, this fraction is below one-third. For Q3d population model, space-based detectors could observe kick effects in over 60% of massive BBH mergers, while in pop3 model, this fraction could drop to 3$\sim$4%. Third-generation ground-based detectors may detect kick effects in up to 16% of stellar-mass BBH mergers. Our findings highlight the importance of incorporating kick velocity effects into waveform modeling, enhancing GW signal interpretation and our understanding of BBH dynamics and astrophysical implications.

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arXiv2025

Anisotropy of Nanohertz Gravitational Wave Background and Source Clustering from Supermassive Binary Black Holes Based on Cosmological Simulation

Several pulsar timing array (PTA) groups have recently claimed the detection of nanohertz gravitational wave background (GWB), but the origin of this gravitational wave (GW) signal remains unclear. Nanohertz GWs generated by supermassive binary black holes (SMBBHs) are one of the most important GW sources in the PTA band. Utilizing data from cosmological simulation, we generate multiple realizations of a mock observable universe that self-consistently incorporates the cosmic large-scale structure, enabling a robust statistical analysis of SMBBH populations and their GW signatures. We systematically investigate the merger event distributions and both the isotropic and anisotropic properties of the resulting GWB signals under different hardening timescales. Specifically, we calculate the characteristic amplitude of the GWB signal, and the angular power spectrum for both the total energy density and energy density in different frequency bins accounting for cosmic variance through different realizations. We also study the clustering pattern of the positional distribution of SMBBHs to examine whether they can reproduce the large-scale structure of galaxies. Furthermore, for the upcoming Chinese Pulsar Timing Array (CPTA) and Square Kilometre Array (SKA)-PTA, we predict the numbers and signal-to-noise ratio (SNR) distributions of resolvable individual GW sources that may be detected with SNR$>$8. We finally investigated the impact of weak lensing effects and found that their influence on the basic characteristics of the GWB signal and individual sources is rather limited.

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arXiv2025

Compact Binary Coalescence Gravitational Wave Signals Counting and Separation

As next-generation gravitational-wave (GW) observatories approach unprecedented sensitivities, the need for robust methods to analyze increasingly complex, overlapping signals becomes ever more pressing. Existing matched-filtering approaches and deep-learning techniques can typically handle only one or two concurrent signals, offering limited adaptability to more varied and intricate superimposed waveforms. To overcome these constraints, we present the UnMixFormer, an attention-based architecture that not only identifies the unknown number of concurrent compact binary coalescence GW events but also disentangles their individual waveforms through a multi-decoder architecture, even when confronted with five overlapping signals. Our UnMixFormer is capable of capturing both short- and long-range dependencies by modeling them in a dual-path manner, while also enhancing periodic feature representation by incorporating Fourier Analysis Networks. Our approach adeptly processes binary black hole, binary neutron star, and neutron star-black hole systems over extended time series data (16,384 samples). When evaluating on synthetic data with signal-to-noise ratios (SNR) ranging from 10 to 50, our method achieves 99.89% counting accuracy, a mean overlap of 0.9831 between separated waveforms and templates, and robust generalization ability to waveforms with spin precession, orbital eccentricity, and higher modes, marking a substantial advance in the precision and versatility of GW data analysis.

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arXiv2025

GWAI: Artificial Intelligence Platform for Enhanced Gravitational Wave Data Analysis

Gravitational wave (GW) astronomy has opened new frontiers in understanding the cosmos, while the integration of artificial intelligence (AI) in science promises to revolutionize data analysis methodologies. However, a significant gap exists, as there is currently no dedicated platform that enables scientists to develop, test, and evaluate AI algorithms efficiently for GW data analysis. To address this gap, we introduce GWAI, a pioneering AI-centered software platform designed for GW data analysis. GWAI contains a three-layered architecture that emphasizes simplicity, modularity, and flexibility, covering the entire analysis pipeline. GWAI aims to accelerate scientific discoveries, bridging the gap between advanced AI techniques and astrophysical research.

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arXiv2025

Dawning of a New Era in Gravitational Wave Data Analysis: Unveiling Cosmic Mysteries via Artificial Intelligence -- A Systematic Review

Gravitational wave data analysis (GWDA) faces significant challenges due to high-dimensional parameter spaces and non-Gaussian, non-stationary artifacts in the interferometer background, which traditional methods have made significant progress in addressing but continue to face limitations. Artificial intelligence (AI), particularly deep learning (DL) algorithms, offers potential advantages, including computational efficiency, scalability, and adaptability, which may complement traditional approaches in tackling these challenges more effectively. In this review, we explore AI-driven approaches to GWDA, covering every stage of the pipeline and presenting first explorations in waveform modeling and parameter estimation. This work represents the most comprehensive review to date, integrating the latest AI advancements with practical GWDA applications. Our meta-analysis reveals insights and trends, highlighting the transformative potential of AI in revolutionizing gravitational wave research and paving the way for future discoveries.

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arXiv2024

Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning

Accurate waveform templates of binary black holes (BBHs) with eccentric orbits are essential for the detection and precise parameter estimation of gravitational waves (GWs). While SEOBNRE produces accurate time-domain waveforms for eccentric BBH systems, its generation speed remains a critical bottleneck in analyzing such systems. Accelerating template generation is crucial to data analysis improvement and valuable information extraction from observational data. We present SEOBNRE_AIq5e2, an innovative AI-based surrogate model that crafted to accelerate waveform generation for eccentric, spin-aligned BBH systems. SEOBNRE_AIq5e2 incorporates an advanced adaptive resampling technique during training, enabling the generation of eccentric BBH waveforms with mass ratios up to 5, eccentricities below 0.2, and spins $|χ_z|$ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of $1.02 \times 10^{-3}$. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.

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arXiv2024

On the transverse-traceless gauge condition when matters are presented

The transverse-traceless gauge condition is an important concept in the theory of gravitational wave. It is well known that vacuum is one of the key conditions to guarantee the existence of the transverse-traceless gauge. Although it is thin, interstellar medium is ubiquitous in the universe. Therefore, it is important to understand the concept of gravitational wave when matter is presented. Bondi-Metzner-Sachs theory has solved the gauge problem related to gravitational wave. But it does not help with the cases when gravitational wave propagates in matters. This paper discusses possible extensions of the transverse-traceless gauge condition to Minkowski perturbation with matter presented.

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arXiv2024

Extending Gibbons-Werner method to bound orbits of massive particles

The Gibbons-Werner method for the gravitational deflection angle of unbound particles in static spherically symmetric spacetimes is based on Jacobi metric and Gauss-Bonnet theorem. When it is extended to bound massive particles, there exists two difficulties: (a) Bound orbits may overlap with themselves azimuthally. To extend the definition of deflection angle for unbound orbits to bound orbits, we divide the bound orbit into multiple segments such that each segment does not overlap with itself azimuthally and can be regarded as an unbound orbit. (b) The infinite region constructed for unbound orbits in Gibbons-Werner method is invalid for bound orbits, since the Jacobi metric of bound massive particles is singular at far region. To construct a suitable region for bound orbits, we adopt the generalized Gibbons-Werner method proposed in our last work [Huang and Cao, https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.104043], so that the unphysical region in Jacobi space is avoided. What's more, taking the Schwarzschild spacetime as an example, we show the details of the calculation and obtain an analytical expression of the deflection angle between two arbitrary points on the orbit.

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arXiv2024

Finite-distance gravitational deflection of massive particles by a rotating black hole in loop quantum gravity

A rotating black hole in loop quantum gravity was constructed by Brahma, Chen, and Yeom based on a nonrotating counterpart using the revised Newman-Janis algorithm recently. For such spacetime, we investigate the weak gravitational deflection of massive particles to explore observational effects of the quantum correction. The purpose of this article is twofold. First, for Gibbons-Werner (GW) method, a geometric approach computing the deflection angle of particles in curved spacetimes, we refine its calculation and obtain a simplified formula. Second, by using GW method and our new formula, we work out the finite-distance weak deflection angle of massive particles for the rotating black hole in loop quantum gravity obtained by Brahma $et\ al.$ An analysis to our result reveals the repulsive effect of the quantum correction to particles. What's more, an observational constraint on the quantum parameter is obtained in solar system.

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arXiv2024

Gravitational Wave Mixture Separation for Future Gravitational Wave Observatories Utilizing Deep Learning

Future GW observatories, such as the Einstein Telescope (ET), are expected to detect gravitational wave signals, some of which are likely to overlap with each other. This overlap may lead to misidentification as a single GW event, potentially biasing the estimated parameters of mixture GWs. In this paper, we adapt the concept of speech separation to address this issue by applying it to signal separation of overlapping GWs. We show that deep learning models can effectively separate overlapping GW signals. The proposed method may aid in eliminating biases in parameter estimation for such signals.

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arXiv2024

Extraction of binary neutron star gravitational wave waveforms from Einstein Telescope using deep learning

In the future, the third generation (3G) gravitational wave (GW) detectors, exemplified by the Einstein Telescope (ET), will be operational. The detection rate of GW from binary neutron star (BNS) is expected to reach approximately $10^4$ per year. To address the challenges posed by BNS GW data processing for 3G GW detectors, this paper explores the extraction of BNS waveforms from ET. Drawing inspiration from SPIIR's matched filtering approach, we introduce a novel framework leveraging deep learning for BNS waveform extraction. By integrating denoised outputs of time-delayed strain, we can reconstruct the embedded BNS waveform. We have established three distinct BNS GW denoising models, each tailored to address the early inspiral, later inspiral, and merger phases of BNS GW, respectively. To further regulate the waveform shape, we propose the Amplitude Regularity Model that takes denoised output as input and regulated waveform as output. The experiments conducted on test data demonstrate the efficacy of the denoising models, the Amplitude Regularity Models, as well as the overall waveform construction method. To the best of our knowledge, this marks the first instance of deep learning being utilized for the task of BNS waveform extraction. We believe that the proposed method holds promise for early warning, searching, and localization of BNS GWs.

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arXiv2024

Dilated convolutional neural network for detecting extreme-mass-ratio inspirals

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.

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arXiv2024

Eccentricity enables the earliest warning and localization of gravitational waves with ground-based detectors

The early and precise localization of gravitational waves (GWs) is pivotal in detecting their electromagnetic (EM) counterparts, especially for binary neutron stars (BNS) and neutron star-black hole binaries (NSBH). In this letter, we pioneer the exploration of utilizing the higher harmonic modes induced by the eccentricity of compact binaries to localize GWs with ground-based detectors even before the quadrupole baseline $\ell=2$ mode enters the detector band. Our theoretical analysis marks a first in proposing a strategy for gaining the earliest possible warning and maximizing preparation time for observing pre- and/or post-merger EM counterparts. We simulate three typical binaries from GWTC-3 with eccentricities ranging from 0.05 to 0.4. Our results reveal that the third-generation (3G) detectors (low frequency cut-off $f_0=5$ Hz) can accumulate sufficient signal-to-noise ratios through higher modes before the onset of the baseline $\ell=2$ mode entry into the band. Notably, relying solely on the higher modes, the 3G detector network ET+2CE achieves an average localization on the order of $1-10^2~\rm deg^2$ around 1-1.8 hours before the merger of a GW170817-like BNS, and $10-10^3~\rm deg^2$ approximately 18-30 minutes prior to the merger of a GW200115-like NSBH. A $100~\rm deg^2$ localization is attainable even 2-4 hours prior to a BNS merger. Moreover, in the near face-on orientations which are generally more favorable for EM counterpart detection, the localization can be further improved.

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arXiv2024

Comparison and application of different post-Newtonian models for inspiralling stellar-mass binary black holes with space-based GW detectors

Space-based gravitational wave (GW) detectors are expected to detect the stellar-mass binary black holes (SBBHs) inspiralling in the low-frequency band, which exist in several years before the merger. Accurate GW waveforms in the inspiral phase are crucial for the detection and analysis of those SBBHs. In our study, based on post-Newtonian (PN) models, we investigate the differences in the detection, accuracy requirement, and parameter estimation of SBBHs in the cases of LISA, Taiji, and their joint detection. We find that low-order PN models are sufficient for simulating low-mass ($\le 50\ \mathrm{M}_\odot$) SBBHs population. Moreover, for detectable SBBHs in space-based GW detectors, over 90% of the GW signals from low-order PN models meet accuracy requirement. Additionally, different PN models do not exhibit significant differences in Bayesian inference. Our research provides a comprehensive reference for balancing computational resources and the desired accuracy of GW waveform generation. It highlights the suitability of low-order PN models for simulating SBBHs and emphasizes their potential in the detection and parameter estimation of SBBHs.

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arXiv2024

Testing an exact diffraction formula with gravitational wave source lensed by a supermassive black hole in binary systems

When it comes to long-wavelength gravitational waves (GWs), diffraction effect becomes significant when these waves are lensed by celestial bodies. Typically, the traditional diffraction integral formula neglects large-angle diffraction, which is often adequate for most of cases. Nonetheless, there are specific scenarios, such as when a GW source is lensed by a supermassive black hole in a binary system, where the lens and source are in close proximity, where large-angle diffraction can play a crucial role. In our prior research, we have introduced an exact, general diffraction integral formula that accounts for large-angle diffraction as well. This paper explores the disparities between this exact diffraction formula and the traditional, approximate one under various special conditions. Our findings indicate that, under specific parameters, such as a lens-source distance of $D_{\rm LS}=0.1$\,AU and a lens mass of $M_{\rm L}=4\times10^{6}M_\odot$, the amplification factor for the exact diffraction formula is notably smaller than that of the approximate formula, differing by a factor of approximately $r_F\simeq0.806$. This difference is substantial enough to be detectable. Furthermore, our study reveals that the proportionality factor $r_F$ gradually increases from 0.5 to 1 as $D_{\rm LS}$ increases, and decreases as $M_{\rm L}$ increases. Significant differences between the exact and approximate formulas are observable when $D_{\rm LS}\lesssim0.2$ AU (assuming $M_{\rm L}=4\times10^{6}M_\odot$) or when $M_{\rm L}\gtrsim2\times10^6M_\odot$ (assuming $D_{\rm LS}=0.1$ AU). These findings suggest that there is potential to validate our general diffraction formula through future GW detections.

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arXiv2024

Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer

Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems. At present, deep learning methods have not been widely explored for GW waveform generation and extrapolation. To solve the data processing difficulty and the increasing waveform complexity caused by the detector's response and second-generation time-delay interferometry (TDI 2.0), an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer) is proposed. For compact binary system waveforms, three models were trained to predict the waveforms of massive black hole binaries (MBHB), extreme mass-ratio inspirals (EMRIs), and galactic binaries (GB), achieving prediction accuracies of at most 99%, 91%, and 99%, respectively. The CBS-GPT model exhibits notable generalization and interpretability, with its hidden parameters effectively capturing the intricate information of waveforms, even with the complex instrument response and a wide parameter range. Our research demonstrates the potential of large models in the GW realm, opening up new opportunities and guidance for future researches such as complex waveforms generation, gap completion, and deep learning model design for GW science.

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arXiv2024

Influence of dark matter equation of state on the axial gravitational ringing of supermassive black holes

In this work, we explore the effects of surrounding dark matter featuring different equations of state on the axial gravitational quasinormal modes of supermassive black holes situated at the center of galaxies. Our attention primarily rests on dark matter exhibiting a spike structure, originating from relativistic Bondi accretion through an adiabatic process, which diminishes at a certain distance from the black hole. We analyze how varying the equation of state of the dark matter influences the properties of the spacetime in the black hole's vicinity. Our findings reveal that different states of dark matter spikes correspondingly affect the black hole's quasinormal modes. In particular, we identify deviations in both the ringing frequency and damping time, reaching magnitudes of up to $10^{-3}$ for certain parameter values. These variations can potentially be detected by upcoming space-borne detectors. Our findings thus indicate the feasibility of discerning and limiting the essential properties of dark matter surrounding supermassive black holes using future gravitational wave detections, particularly in the case of extreme mass ratio inspiral systems.

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arXiv2024

WaveFormer: transformer-based denoising method for gravitational-wave data

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole (BBH) events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in gravitational wave data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network (IGWN) in future observational runs.

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