arXiv subjects

Minghui Du

Publications and source records attributed to Minghui Du.

At least 37 records · Page 2Linked to original sources

arXiv2026

Extending the micro-Hertz detection horizons via orbital resonance effect for geocentric gravitational wave antennas

The $μ$Hz gravitational wave band holds crucial insights into coalescing supermassive black hole binaries and stochastic backgrounds but remains inaccessible due to technical challenges. We demonstrate that geocentric space-based GW detectors (e.g., TianQin, gLISA, GADFLI) can bridge this gap by considering orbital resonance effects, circumventing the need for prohibitively long baselines. When GW frequencies match with integer multiples of a satellite's orbital frequency, sustained tidal forces induce cumulative orbital deviations through resonant effects, which, combined with orbital modulation, improve detector sensitivity by 1-2 orders of magnitude in the $μ$Hz band. Consequently, geocentric missions can detect SMBHBs across significantly expanded mass-redshift parameter space. Crucially, such observations could synergize with pulsar timing array data of the same binaries at earlier inspiral stages, enabling unprecedented joint tests of strong-field gravity and binary evolution. Our findings establish geocentric antennas as a cost-effective, near-term precursor for unlocking the $μ$Hz GW astronomy.

gr-qc↗

arXiv2025

Source Confusion of Massive Black Hole Binaries for the Taiji Mission

We systematically investigate the source confusion of massive black hole binaries (MBHBs) for the Taiji space-based gravitational wave mission. Source confusion, arising from the overlap of signals in both time and frequency domains, can degrade parameter recovery accuracy. To assess this effect, we simulate three representative models MBHB populations to estimate event overlap events. Assuming 100 detections per year, only 0.31-4.2 overlaps are expected annually. Based on Fisher information matrix with the $\texttt{IMRPhenomD}$ and $\texttt{IMRPhenomHM}$ waveform models, we find that overlap significantly enlarges parameter uncertainties, while the inclusion of higher-order modes (HMs) effectively mitigates this effect. Severe confusion ($Δ\mathcal{M}_z / \mathcal{M}_z<$ 0.2%) occurs in fewer than 0.14% across the three population models. The full Bayesian analysis further corroborates the Fisher predictions, and also reveals that HMs help break key parameter degeneracies, with or without signal overlap. These findings underscore the importance of incorporating HMs for accurate inference in future space-based observations.

gr-qc↗

arXiv2025

Gravitomagnetic-Hydrodynamics and Turbulence in Early Universe

The nonlinear coupling between spacetime geometry and matter in the early Universe remains a frontier in theoretical cosmology. By introducing a novel gravitomagnetic-hydrodynamic framework, we reveal a fundamental analogy between magnetohydrodynamics and the co-evolution of spacetime geometry and relativistic plasma. We demonstrate that, in high-energy environments such as the electroweak phase transition, the (newly defined) gravitomagnetic Reynolds number becomes large, signifying a strongly coupled system where the gravitomagnetic field could be frozen into the fluid. This coupling inevitably leads to the emergence of gravitational Alfvén waves and could drive a transition to turbulence involving the dynamics of spacetime itself. Our findings suggest that gravitomagnetic-hydrodynamic turbulence may leave imprints on the stochastic gravitational wave background, offering a new window into the nonlinear dynamics of the primordial Universe.

gr-qc↗

arXiv2025

Inter-Spacecraft Tilt-to-Length Noise Reduction Algorithm for Taiji Mission

The Taiji mission for space-based gravitational wave (GW) detection employs laser interferometry to measure picometer-scale distance variations induced by GWs. The tilt-to-length (TTL) coupling noise in the inter-spacecraft interferometers, which originates from the angular jitters of the spacecrafts and the movable optical subassemblies, is predicted to be one of the main noise sources that might reduce Taiji's sensitivity to GWs. Since these angular jitters can be read out through the differential wavefront sensors, it is possible to suppress TTL noise during the data processing stage by fitting and subtracting it. This paper proposes an improved algorithm for TTL noise suppression, which addresses the issue of unknown noise floor required for optimal estimation in the practical detection scenario, and the design of this algorithm takes into account the presence of GW signals. The algorithm is validated via numerical simulation, which is built on a spacecraft dynamics simulation incorporating Taiji's drag-free and attitude control system. We also demonstrate the robustness of this algorithm by varying TTL coefficients at different levels, indicating that our algorithm is applicable to a range of payload statuses, and ultimately providing a critical advancement toward realizing Taiji's full sensitivity.

gr-qc↗

arXiv2025

Bayesian Analysis of Wave-Optics Gravitationally Lensed Massive Black Hole Binaries with Space-Based Gravitational Wave Detector

Within a Bayesian statistical framework, we jointly estimate the source and lens parameters and evaluate the relative evidence between the lensed and unlensed models. This work focuses on the wave optics effects induced by a point mass (PM) lens on gravitational waves (GW) from equal-mass massive binary black holes (MBHB), and assesses the capability of the space-based GW detector Taiji to detect such effects. Specifically, we investigate the impact of the redshifted lens mass MLz in the range [3e5, 3e7] solar masses, impact parameter y in [10, 50], source redshift zs in [4, 6], and total source mass Ms in [1e5, 1e7] solar masses on parameter estimation and model selection. Our results show that, for the cases we studied, larger MLz increases the waveform mismatch MM, which directly enhances the waveform difference and the corresponding signal-to-noise ratio (SNR), thereby improving the ability to discriminate between the lensed and unlensed models. In contrast, for y > 50, both MM and SNR are too small to allow effective model discrimination in these cases. Parameter estimation further indicates that for y < 50, the degeneracy between the luminosity distance and MLz can be effectively broken. Although the Bayes factor decreases as zs increases, lensing signatures remain identifiable up to zs = 6. The role of Ms depends on the overlap of the GW signal with the detector sensitive band. Overall, effective model discrimination requires MM greater than or equal to 1e-7 (corresponding to SNR greater than 5).

astro-ph.HE↗

arXiv2025

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises

The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework based on short-time Fourier transform (STFT) to enhance parameter estimation of Galactic binary and characterization of instrumental noise under non-stationarity. Segmenting data into locally stationary intervals, we derive STFT-based models for signals and noises, and implement Bayesian inference via the extended Whittle likelihood. Validated through the analysis of verification Galactic binaries and instrumental noises, our STFT approach outperforms frequency-domain methods by reducing the uncertainty and bias of estimation, successfully recovering low signal-to-noise ratio signals missed by frequency-domain analysis, and mitigating the degeneracy among noise parameters. The framework's robustness against noise drifts and computational efficiency highlight its potential for integration into future global analysis pipelines.

gr-qc↗

arXiv2024

Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network

The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the short timescale of binary mergers, it is crucial to predict the time of the merger in advance to devise detailed observational plans. The overwhelming noise and the slow accumulation of signal-to-noise ratio in the inspiral phase make this task particularly challenging. To address this issue, we propose a novel deep neural denoising network in this study, capable of denoising a 30-day inspiral phase signal. Following the denoising process, we perform the detection and merger time prediction based on the denoised signals. Our results demonstrate that for a 30-day inspiral phase data with a signal-to-noise ratio between 10 and 50 occurring no more than 10 days before the merger, our absolute prediction error for the merger time is generally within 24 hours.

gr-qc↗

arXiv2024

Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows

Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector's response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.

gr-qc↗

arXiv2024

Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations

For the LISA and Taiji missions, both transient and continuous data anomalies would pose significant challenges to the detection, estimation, and subsequent scientific interpretation of gravitational wave signals. As is indicated by the experiences of LISA PathFinder and Taiji-1, these anomalies may originate from the disturbances of the gravitational reference sensors due to routine maintenances and unexpected environmental or instrumental issues. To effectively mitigate such anomalies and thereby enhance the robustness and reliability of the scientific outputs, we suggest to employ the ``position noise suppressing'' time delay interferometry channels. Through analytical derivations and numerical simulations, we demonstrate that these time delay interferometry channels can suppress data anomalies by more than 2 orders of magnitude within the sensitive band of 0.1 mHz - 0.05 Hz, while still remaining sensitive to most of the target signals. Compared with existing researches that focus on reconstructing and subtracting data anomalies, our method does not rely on the prior knowledge about the models of anomalies. Furthermore, the potential application scenarios of these channels have also been explored.

gr-qc↗

arXiv2024

Fluctuation-dissipation relation of test masses in classical stochastic gravitational wave background

Research on pulsar timing arrays has provided preliminary evidence for the existence of a stochastic gravitational background, which, either being primordial or of astrophysical origin, will interact universally with matter distributions in our universe and affect their evolutions. This work, based on general relativity and stochastic dynamics theory, investigates the fluctuation-dissipation relation of isolated celestial bodies within a classical stochastic gravitational wave background. We employ the generalized Langevin model to analyze the fluctuating forces exerted on test masses by random spacetime and how energy dissipation occurs. Through the assumption of equilibrium, we derive the necessary conditions that should be satisfied by the stochastic gravitational wave background in the long wavelength limit, which, as we found, is a result of the back-reactions of the test mass system to the stochastic field. Additionally, as the establishment of the fluctuation-dissipation relation for such a system, certain thermodynamic quantities related to the statistical properties of the stochastic gravitational wave background could be defined and the characteristic of the diffusion process of test masses is obtained.

gr-qc↗

arXiv2024

Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions.

gr-qc↗

arXiv2024

Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning

Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes particularly challenging due to non-local parameter degeneracies, arising from multiple local maxima, as well as flat regions and ridges inherent in the likelihood function. These factors lead to exceptionally high time complexity for parameter analysis while employing traditional matched filtering and random sampling methods. To address these challenges, the present study applies machine learning to Bayesian posterior estimation of EMRI signals, leveraging the recently developed flow matching technique based on ODE neural networks. Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter space, involving up to seventeen parameters, associated with EMRI signals. Furthermore, to our knowledge, this is the first instance of applying machine learning, specifically the Continuous Normalizing Flows (CNFs), to EMRI signal analysis. Our findings highlight the promising potential of machine learning in EMRI waveform analysis, offering new perspectives for the advancement of space-based GW detection and GW astronomy.

physics.comp-ph↗

arXiv2024

Exploring Time Delay Interferometry Ranging as a Practical Ranging Approach in the Bayesian Framework

Time Delay Interferometry (TDI) is an indispensable step in the whole data processing procedure of space-based gravitational wave detection, as it mitigates the overwhelming laser frequency noise, which would otherwise completely bury the gravitational wave signals. Knowledge on the inter-spacecraft optical paths (i.e. delays) is one of the key elements of TDI. Conventional method for inter-spacecraft ranging mainly relies on the pseudo-random noise (PRN) code signal modulated onto the lasers. To ensure the reliability and robustness of this ranging information, it would be highly beneficial to develop other methods which could serve as cross-validations or backups. This paper explores the practical implementation of an alternative data-driven approach - time delay interferometry ranging (TDIR) - as a ranging technique independent of the PRN signal. Distinguished from previous research, our TDIR algorithm significantly relaxes the stringent requirement for clock synchronization imposed by traditional TDI procedure. By framing TDIR as a Bayesian parameter estimation problem and employing a general polynomial parametrization, we demonstrate our algorithm with simulated data based on the numerical orbit of Taiji. In the presence of laser frequency noise and secondary noises, the estimated median values of delays are only 5.28 ns away from the ground truths, capable of suppressing laser frequency noise to the desired level. Additionally, we have also analysed the requirements of mitigating optical bench noise and clock noise on TDIR, and presented an illustrative example for the impact of laser locking.

astro-ph.IM↗

arXiv2024

GravitoMagneto-Hydrodynamics and Spacetime Turbulence in Early Universe

Based on the gravitoelectromagnetic formalism and inspired by the rich analogies between electrodynamics and general relativity, we try one step further along this line and suggest a new counterpart in the gravitoelectromagnetic world analogue to the electromagnetic physics. A counterpart model of the MagnetoHydroDynamics that could help us to understand the possible new physics in tightly bounded spacetime-matter systems such as the case of extremely relativistic fluids in the early Universe. This new viewpoint also suggests a possible new form of spacetime-matter turbulence which may be tested through gravitational wave observations.

gr-qc↗

arXiv2024

Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows

Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.

astro-ph.IM↗

arXiv2023

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .

gr-qc↗

arXiv2023

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.

gr-qc↗

arXiv2023

A Measurement of Hubble Constant Using Cosmographic Approach from Fast Radio Bursts and SNe Ia

The Hubble constant ${H}_0$ is a crucial parameter in cosmology. However, different cosmic observations have resulted in varying posterior results for ${H}_0$, leading to what is known as the ${H}_0$ tension. In order to address this issue, it is beneficial to use other dataset to constrain ${H}_0$. In this paper, via the cosmographic approach based on the Friedman-Lemaitre-Robertson-Walker (FLRW) metric to the dispersion measure of the intergalactic medium ${\rm{DM}}_{\rm{IGM}}(z)$ of Fast Radio Bursts (FRBs), we obtain the Taylor expansion of $\langle{\rm{DM}}_{\rm{IGM}}(z)\rangle$ in terms redshift $z$. The result for Hubble constant $H_0=65.5^{+6.4}_{-5.4}$ ${\rm{km~s^{-1}~Mpc^{-1}}}$ $(68$$\%$ ${\rm{C.L.}}) $, cosmological deceleration parameter $q_0=-0.50\pm 0.20 $ and the jerk parameter $j_0=-0.1^{+2.0}_{-2.5}$ using uncalibrated Supernova Ia (SNe Ia) Pantheon dataset combined with 18 localized FRBs are obtained. To demonstrate the impact of parameter degeneracies on our analysis methods, we compare the results using three different forms of $f_{\rm{IGM}}(z)$ and two different prior distributions for $Ω_{\rm{b,0}}$. Then we find that the uncertainty in $H_0$ is not significantly affected by the prior range of $f_{\rm{IGM}}(z)$ and $Ω_{\rm{b,0}}$, but the mean value is influenced by the priors for $f_{\rm{IGM}}(z)$ and $Ω_{\rm{b,0}}$ due to parameter degeneracies with $H_0$. Employing $f_{\rm{IGM}}(z)$ that evolves with redshift, we obtain the constraints for $H_0=69.0^{+6.7}_{-5.7}$ ${\rm{km~s^{-1}~Mpc^{-1}}}$. Furthermore, the mock analyses give a posterior estimation of $H_0$ with an accuracy of 4.6\% and higher precision for $q_0$ and $j_0$ in the near future.

astro-ph.CO↗