arXiv subjects

Mengfei Sun

Publications and source records attributed to Mengfei Sun.

12 recordsLinked to original sources

arXiv2026

Constraining AGN Disk Properties with Gravitational Waves from Inspiraling Stellar-Mass Binary Black Holes in Hierarchical Triple Systems

Space-based gravitational-wave detectors can observe stellar-mass binary black holes (BBHs) long before merger, allowing weak environmental perturbations to accumulate. For binaries embedded in active galactic nucleus (AGN) disks, the local gas density characterizes the environment of the supermassive black hole (SMBH) and compact-object migration. We study whether such signals can constrain this density when a stellar-mass BBH orbits a Kerr SMBH. We evolve the outer orbit with relativistic corrections and gaseous dynamical friction (DF), and construct the detector-frame waveform including BBH inspiral, de Sitter precession, DF phase correction, and moving-source effects. Using Fisher-matrix calculations for sampled systems, we estimate statistical uncertainties and systematic errors. Larger gas densities generally improve the statistical precision of several source and outer-orbit parameters, but also increase systematic errors when DF is omitted. For favorable GW190521-like systems observed by LISA for one year, the disk density can be constrained at the level of $\sigma_{\rho}\sim10^{-12}\text{--}10^{-10}\,{\rm g\,cm^{-3}}$. Such constraints would connect BBH merger environments to the gas structure of galactic nuclei and the conditions that support black hole growth. These results indicate that hierarchical BBH inspirals can probe AGN disk environments, provided that gas effects are modeled consistently.

gr-qc↗

arXiv2026

Effects of Solar Wind Plasma Noise on Stochastic Gravitational Wave Background Searches with the LISA-Taiji Network

The LISA-Taiji dual detector network improves millihertz SGWB sensitivity through cross correlation measurements. Solar wind plasma, however, can generate plasma noise correlated between detectors and bias SGWB cross correlation estimates. We use high time resolution electron density data from Wind/SWE, estimate the solar wind electron density fluctuation spectrum with the Lomb-Scargle method, and propagate the resulting plasma noise to the TDI A/E channels of the LISA-Taiji network. By including finite arm propagation, Taylor frozen flow spatial correlations, and the network overlap reduction response, we compute the SGWB parameter bias induced by interdetector plasma noise. Although the single detector plasma residual is below the reference noise, the component correlated between detectors can enter the SGWB cross correlation estimator directly. Under dual detector scale coverage, the plasma induced parameter bias for a power law SGWB can reach 12.73% of the corresponding Fisher parameter uncertainty. For M2/M3 cosmic string spectra, the bias in ln Gmu can reach 19.26% of the corresponding Fisher parameter uncertainty for the network configurations, observing times, and frequency bands considered here. These results show that the impact of solar wind plasma noise cannot be assessed from the single detector residual noise level alone. In LISA-Taiji SGWB searches, the interdetector correlated component of this noise can directly affect parameter estimation.

astro-ph.CO↗

arXiv2026

Tests of general relativity using analytic derivatives of parametrized post-Einsteinian gravitational waveforms within the Fisher-matrix framework

Testing gravity beyond general relativity (GR) is essential for probing fundamental physics in the strong-field and highly dynamical regime accessed by gravitational-wave (GW) observations. In this work, we derive analytic expressions for waveform derivatives in the Fisher-matrix formalism within the parametrized post-Einsteinian framework, using the frequency-domain inspiral waveform. These analytic derivatives enable stable and efficient Fisher-matrix calculations without relying on finite-difference schemes. We apply this method to a wide range of detector configurations, including space-based, ground-based, and multiband observations, and combine it with different binary black hole population models. Our results reveal clear and systematic trends in the constraints on non-GR effects as functions of post-Newtonian order, detector type, and source population. They also demonstrate the complementarity between space- and ground-based detectors, particularly for effects that accumulate during the low-frequency inspiral. The analytic approach substantially reduces computational cost and avoids numerical systematics associated with step-size choices, making it well suited for large-scale parameter studies. These results provide robust forecasts for the capability of future GW observations to constrain a broad class of non-GR effects and environmental influences, highlighting the scientific potential of upcoming detector networks for precision tests of gravity.

gr-qc↗

arXiv2026

Constraining Kerr supermassive black hole properties using gravitational waves from inspiraling stellar-mass binary black holes

We study the capability of future space-based gravitational-wave (GW) detectors to constrain supermassive black hole (SMBH) properties through observations of inspiraling stellar-mass binary black holes (BBHs) orbiting them. Focusing on stable hierarchical triple systems, we model the BBH motion in Kerr spacetime and compute the modulated GW signals using the post Newtonian waveform combined with moving-source transformation. Based on the LISA configuration and second-generation time delay interferometry technology, we estimate parameter uncertainties with the Fisher information matrix. Our results show that the outer semimajor axis has the strongest influence on parameter precision, while the SMBH spin and eccentricity mainly affect their own uncertainties. For high-SNR signals, the SMBH mass and orbital parameters can be measured with relative uncertainties on the order of $10^{-5}$, while the spin magnitude and its orientation can be constrained to within a few percentages. Applying the method to an M87*-like system, GW observations provide more precise measurements of the SMBH mass and spin compared with current electromagnetic observations, highlighting the potential of space-based GW astronomy to probe SMBH properties with high accuracy.

gr-qc↗

arXiv2026

Identifiability of $g$ mode Resonances in Eccentric Binary Neutron Stars with Multidetector Observations

$g$ mode resonances in eccentric binary neutron star systems are potential probes of internal stratification, superfluidity, composition gradients, and the equation of state. Although such weak dynamical tidal signatures are unlikely to be resolved with current detector sensitivities, third generation observations may make them accessible, in which case identifying the weak resonant phase shift would provide information beyond the bulk adiabatic tidal deformability. We build a four class dataset in an eccentric harmonic framework, containing point particle, adiabatic tide, resonant $g$ mode, and pure noise samples, and use Einstein Telescope (ET) and Cosmic Explorer (CE) detector data to test whether this weak resonant phase signature can be identified from noisy time domain strain. The ET, CE, and ET+CE deep learning models reach accuracies of $0.655$, $0.815$, and $0.897$, respectively. On the same simulated samples, the matched filtering method reaches lower accuracies of $0.514$, $0.677$, and $0.689$. This result arises from the fact that the resonant correction manifests as a weak phase morphology difference superimposed on the adiabatic tidal background, whereas matched filtering is sensitive only to the overall similarity. Hence, in the presence of weak phase differences, the neural classifier employed in deep learning is better able to learn these local phase and morphology features from the complete time domain strain segment. The results indicate that joint third generation observations improve the identifiability of weak internal mode phase information.

astro-ph.HE↗

arXiv2025

Detection of Multiband Lensed Gravitational Waves from Dark Matter Halos with Deep Learning

Lensed gravitational waves acquire amplitude and phase modulations when propagating through the gravitational potential of dark matter halos, producing interference structures in the observed waveform. However, these features are often difficult to identify in detector noise. In this work, we develop a deep learning framework for the automatic classification of lensed gravitational wave signals under multiband observations. We simulate binary neutron star signals observed by the space based detector DECIGO and the ground based Einstein Telescope, and construct five classes of data including pure noise, unlensed signals, and three lensed cases generated by the SIS, CIS, and NFW dark matter halo models. By comparing single detector and joint detector configurations, we evaluate the classification performance under different observational settings. The results show that multiband observations significantly improve the identification of lensed signals and reduce confusion among different lens models. This approach provides an efficient method for automated recognition of lensed gravitational waves in future multiband gravitational wave observations.

astro-ph.IM↗

arXiv2025

Probing globular clusters using modulated gravitational waves from binary black holes

Globular clusters (GCs) are crucial for studying stellar dynamics and galactic structure, yet precise measurements of their distances and masses are often limited by uncertainties in electromagnetic (EM) observations. We present a novel method that leverages gravitational waves (GWs) from stellar-mass binary black holes (BBHs) orbiting within GCs to enhance the precision of GC parameter measurements. The BBH's orbital motion imprints characteristic modulations on the GW waveform, encoding information about the host GC. Using post-Newtonian waveforms and Lorentz transformations, we simulate modulated GW signals and evaluate the resulting parameter constraints via a Fisher information matrix analysis. Our results show that incorporating GW observations can significantly reduce the uncertainties in GC distance and mass measurements, in many cases achieving improvements by an order of magnitude. These findings demonstrate the value of BBHs as dynamical probes and highlight the power of GWs to advance GC studies beyond the limits of traditional EM methods.

gr-qc↗

arXiv2025

Conditional Autoencoder for Generating Binary Neutron Star Waveforms with Tidal and Precession Effects

Gravitational waves from binary neutron star mergers provide critical insights into dense matter physics and strong-field gravity, yet accurate waveform modeling remains computationally intensive. We present a deep generative model for gravitational waveforms from binary neutron star mergers that captures the late inspiral, merger, and ringdown phases while incorporating spin precession and tidal effects. Using a conditional autoencoder architecture, the model efficiently produces high-fidelity waveforms across a broad parameter space, including component masses (m1, m2), spin components (S1x, S1y, S1z, S2x, S2y, S2z), and tidal deformabilities (Lambda1, Lambda2). Trained on 1*10^6 waveforms generated by the IMRPhenomXP_NRTidalv2 model, our network achieves a mean mismatch of 2.13*10^-3. The generation time for a single waveform is 0.12 s, compared to 0.66 s for IMRPhenomXP_NRTidalv2, representing a speedup of about fivefold. When generating 1000 waveforms, the model completes the task in 0.75 s, roughly ten times faster than the baseline. This significant acceleration facilitates rapid parameter estimation and real-time gravitational-wave searches. With improved precision and efficiency, the model can support low-latency detection and broader applications in multi-messenger astrophysics.

astro-ph.GA↗

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.

gr-qc↗

arXiv2025

Identification of Stochastic Gravitational Wave Backgrounds from Cosmic String Using Machine Learning

Cosmic strings play a crucial role in enhancing our understanding of the fundamental structure and evolution of the universe, unifying our knowledge of cosmology, and potentially unveiling new physical laws and phenomena. The advent and operation of space-based detectors provide an important opportunity for detecting stochastic gravitational wave backgrounds (SGWB) generated by cosmic strings. However, the intricate nature of SGWB poses a formidable challenge in distinguishing its signal from the complex noise by some traditional methods. Therefore, we attempt to identify SGWB based on machine learning. Our findings show that the joint detection of LISA and Taiji significantly outperforms individual detectors, and even in the presence of numerous low signal-to-noise ratio(SNR) signals, the identification accuracy remains exceptionally high with 95%. Although our discussion is based solely on simulated data, the relevant methods can provide data-driven analytical capabilities for future observations of SGWB.

gr-qc↗

arXiv2024

Constraints and detection capabilities of GW polarizations with space-based detectors in different TDI combinations

TDI is essential in space-based GW detectors, effectively reducing laser noise and improving detection precision. Space-based GW detectors provide a unique opportunity to probe GW polarizations, including possible additional modes that may signal deviations from general relativity and alternative gravity theories. In this study, we examine the impacts of second-generation TDI combinations on GW polarization detection by simulating LISA, Taiji, and TianQin, including realistic orbital effects such as link length and angle variations. Detector performance is assessed using sensitivity and power-law integrated curves, as well as the SNR of BBHs and phase transitions (PTs). For massive BBHs, the A and E channels typically offer the best sensitivity, while the X channel in TianQin is most effective for detecting additional polarizations. For stellar-mass BBHs, the $\alpha$ channel provides the highest SNR for vector modes in LISA and Taiji specifically for lower-mass systems, while the A and E channels are optimal for higher masses or other polarizations. For PT signals, the X channel generally delivers the optimal performance, except in the low-peak-frequency regime below 1 mHz, where the U channel in TianQin becomes more sensitive. When considering additional polarizations, the X channel emerges as the most robust choice for TianQin, in contrast to LISA and Taiji, where the A and E channels provide strong capabilities for GW polarization tests. This distinction between LISA, Taiji, and TianQin represents a key result of the present work and has not been explicitly emphasized in previous studies. Our findings emphasize the importance of selecting high-sensitivity TDI combinations to enhance detection capabilities across different polarizations, deepening our insight into GW sources and the fundamental nature of spacetime.

gr-qc↗

arXiv2023

Parameter Estimation for Intermediate-Mass Binary Black Holes through Gravitational Waves Observed by DECIGO

With the anticipated launch of space-based gravitational wave detectors, including LISA, TaiJi, TianQin, and DECIGO, expected around 2030, the detection of gravitational waves generated by intermediate-mass black hole binaries (IMBBHs) becomes a tangible prospect. However, due to the detector's reception of a substantial amount of non-Gaussian, non-stationary data, employing traditional Bayesian inference methods for parameter estimation would result in significant resource demands and limitations in the waveform template library. Therefore, in this paper, we simulated foreground noise induced by stellar-origin binary black holes (SOBBHs), which is non-Gaussian and non-stationary, and we explore the use of Gaussian process regression (GPR) and deep learning for parameter estimation of Intermediate Mass Binary Black Holes (IMBBHs) in the presence of such non-Gaussian, non-stationary background noise. By comparing these results from deep learning and GPR, we demonstrate that deep learning can offer improved precision in parameter estimation compared to traditional GPR. Furthermore, compared to GPR, deep learning can provide posterior distributions of the sample parameters faster.

gr-qc↗