arXiv subjects

Yong Yuan

Publications and source records attributed to Yong Yuan.

At least 19 recordsLinked to original sources

arXiv2026

Efficient Evaluation of Gravitational Lensing Amplification Factors: A Deep Learning Framework

Wave optics is essential for analyzing lensed gravitational waves (GWs), yet evaluating the diffraction integral $F(ω, y)$ is computationally expensive. We present a Sinusoidal Representation Networks (SIRENs) framework for the dimensionless amplification factor, demonstrating its efficacy and generalization through Point Mass Lens (PML) and Singular Isothermal Sphere (SIS) test cases. Unlike standard architectures that suffer from spectral bias, the network's periodic activation functions structurally align with the integral's oscillatory kernel, effectively resolving high-frequency spectral features. The resulting estimator achieves $\mathcal{O}(10^{-3})$ relative accuracy and a $\sim 100\times$ speedup compared to direct numerical integration. By shifting the computational burden to offline training, our framework yields a stable $\mathcal{O}(1)$ inference complexity. This guarantees constant, sub-millisecond evaluation times even in the weak-lensing diffraction tail where traditional methods stagnate. Additionally, the dimensionless formulation ensures intrinsic scale invariance, enabling direct application across astrophysical regimes from stellar-mass lenses in the ground-based LVK band to supermassive black holes in the space-based LISA band.

astro-ph.IM↗

arXiv2026

Time-Domain Deep Learning for Pairwise Identification of Strongly Lensed Gravitational-Wave Candidates

As gravitational wave (GW) catalogs continue to expand, exhaustive Bayesian comparisons of candidate event pairs become increasingly computationally expensive, which motivates the development of fast prescreening methods for strongly lensed GW searches. We formulate lensed-pair identification as a binary verification problem using two preprocessed strain segments. To address this task, we propose Physics-Inspired ResNet (PI-ResNet), a Siamese one-dimensional residual network for pairwise GW candidate classification. Unlike spectrogram-based prescreening approaches, PI-ResNet operates directly on whitened time-domain strain data and avoids an intermediate time--frequency image representation. A shared residual backbone with Squeeze-and-Excitation (SE) modules encodes the two input segments, and the paired embeddings are compared through absolute feature differences and Hadamard-product interactions. We train and evaluate the model using simulated GW signals from binary black hole mergers lensed by point-mass (PM) and singular isothermal sphere (SIS) lenses, injected into simulated LIGO and Einstein Telescope (ET) detector noise. Under ET design noise, PI-ResNet achieves accuracies of $95.60\%$ for SIS lenses and $93.80\%$ for PM lenses, while maintaining $84.03\%$ and $78.25\%$ accuracy under simulated LIGO H1--L1 Gaussian noise. These results suggest that direct learning from 1D strain data provides an efficient and physically motivated preselection statistic for candidate lensed GW pairs, while also indicating the need for detector-domain adaptation.

astro-ph.HE↗

arXiv2026

Measuring the Hubble constant with strongly lensed gravitational waves from space-based detector networks

The measurement of the Hubble constant $H_0$ plays a central role in modern cosmology. In this work, we investigate the potential of strongly lensed gravitational-wave (SLGW) signals from massive binary black hole mergers to constrain $H_0$ using future space-based detector networks. We consider two observational scenarios: one in which the source redshift is unknown, and another in which it is independently determined through electromagnetic observations. We show that meaningful constraints on $H_0$ can still be achieved without source-redshift information, provided that the lens redshift is known. For individual SLGW events, the joint Taiji+LISA analysis improves the measurement precision of $H_0$ by approximately a factor of two compared with the Taiji-only configuration. Extending the analysis to the population level, we combine five simulated SLGW events and find that the uncertainty in $H_0$, quantified by the 95\% credible interval, reaches the $1.1\times10^{-1}$ level when the source redshift is treated as unknown, and further improves to $4.2\times10^{-2}$ when the source redshift is independently measured. Our results demonstrate that joint space-based gravitational-wave observations can substantially enhance the cosmological capability of SLGW events and provide a promising avenue for precision measurements of the Hubble constant.

astro-ph.CO↗

arXiv2026

Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection

Gravitational-wave astronomy has opened a direct observational window onto compact-object dynamics, strong-field gravity, and cosmology. Among the transient sources accessible through this window, core-collapse supernovae (CCSNe) are uniquely valuable because their signals can probe the engine of stellar collapse, proto-neutron-star dynamics, and explosion asymmetries, yet their weak, stochastic, and model-dependent waveforms remain difficult to detect. In this work, we develop a contrastive self-supervised convolutional autoencoder (CS-CAE) for CCSNe gravitational-wave signal detection. The method combines a convolutional autoencoder (CAE), a noise-centered latent regularizer, and a projection head trained with a contrastive objective. This design encourages independent noisy realizations of the same CCSNe signal to be mapped to nearby latent representations, thereby reducing the influence of random noise fluctuations. CS-CAE achieves performance comparable to a supervised convolutional neural network while clearly outperforming a conventional CAE baseline, and generalizes better to unseen numerical CCSNe waveform families. Under the Einstein Telescope (ET) detector configuration, the method achieves an effective sensitive distance of approximately 120 kpc and shows improved separation of CCSNe signals from stationary noise and transient glitches in the low-false-alarm regime. These results highlight the potential of CS-CAE as a robust and less template-dependent framework for CCSNe gravitational-wave searches.

gr-qc↗

arXiv2026

Bayesian Analysis of Gravitational Wave Microlensing Effects from Galactic Double White Dwarfs

Gravitational waves (GWs) from the galactic double white dwarf (DWD) systems are one of the primary targets for upcoming space-based detectors. Due to their vast abundance and widespread distribution throughout the Galactic disk and bulge, these systems may provide a high-statistical population for probing GW microlensing effects induced by Galactic compact objects. To evaluate the detectability of such effects, in this work we simulate the four-year observation of DWD systems by Taiji, in the form of a second-generation Time Delay Interferometry (TDI) data stream. Within a Bayesian inference framework, we estimate parameters for lensed GWs from DWD systems for different values of the lens parameters, including the lens mass $M_\mathrm{L}\in [10, 10^6]$\,M$_\odot$, the effective velocity $v_\mathrm{eff}\in [50, 500]$\,km/s and the initial separation $L\in [R_\mathrm{E}, 3R_\mathrm{E}]$, and obtain the uncertainties of the corresponding parameters. These results characterize the capability of future Taiji observations to probe such systems. We further employ the Bayesian model selection framework to distinguish between lensed and unlensed scenarios, and investigate the impacts of three key physical parameters of the lens system: $M_\mathrm{L}$, $v_\mathrm{eff}$, and $L$ on distinguishing lensing events. Our results show that when $M_\mathrm{L}$ is below $10^5$\,M$_\odot$ or $L\geq3R_\mathrm{E}$, it is not possible to distinguish between lensed and unlensed models. For $v_\mathrm{eff}$, although the Bayes factor decreases as $v_\mathrm{eff}$ decreases, the lensed and unlensed models can still be distinguished within our parameter range.

astro-ph.GA↗

arXiv2026

An Opacity-Free Test of the Cosmic Distance Duality Relation Using Strongly Lensed Gravitational Wave Signals with Space-Based Detector Networks

The cosmic distance duality relation (CDDR), expressed as $d_L(z) = (1+z)^2 D_A(z)$, is a fundamental relation in modern cosmology. In this work, we apply a method to test the CDDR using simulated strongly lensed gravitational-wave (SLGW) signals from massive binary black holes (MBBH) as observed by proposed space-based detector networks. Our analysis is conducted under the point-mass lens model, considering the strong lensing scenario that produces two images. We generate 90 days of simulated SLGW data for 10 events based on the Population III stellar formation model, with source redshifts in the range $z_s \in [2,6]$ and lens redshifts in $z_L \in [0.2,1]$. The deviation of CDDR is parameterized by $η_1(z) = 1 + η_0 z$ and $η_2(z) = 1 + η_0 z/(1+z)$, and we incorporate the deviation parameter $η_0$ directly into the waveform model. Parameter estimation is performed within a Bayesian statistical framework, combining simulated data from both Taiji and LISA. For a single lensed event, the joint Taiji+LISA analysis improves the measurement precision of $η_0$ by roughly a factor of two compared with Taiji-only observations. By combining 10 simulated events, the population-level constraints on $η_0$, quantified by the half width of the $95\%$ credible interval, reach approximately $2.61\times10^{-4}$ ($1.72\times10^{-4}$) for the $η_1(z)$ parameterization and $1.22\times10^{-3}$ ($6.86\times10^{-4}$) for $η_2(z)$ in the Taiji-only (Taiji+LISA) scenario, respectively. The inferred values of $η_0$ remain consistent with $η_0 = 0$ within the estimated uncertainties, with no statistically significant evidence for deviations from the CDDR at the achieved precision. These results demonstrate the significant advantage of joint space-based observations for high-precision tests of the CDDR.

astro-ph.CO↗

arXiv2026

Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Based Gravitational Wave Antenna

Taiji, a Chinese space-based gravitational wave (GW) detection project, aims to explore the millihertz GW universe with unprecedented sensitivity. By observing astrophysical and cosmological sources, including Galactic binaries, massive black hole binaries, extreme mass-ratio inspirals, and stochastic gravitational wave backgrounds, etc., Taiji is expected to deliver transformative insights into astrophysics, cosmology, and fundamental physics. However, Taiji's data analysis faces unique challenges compared to ground-based detectors like LIGO-Virgo-KAGRA, such as the overlap of numerous signals, extended data durations, more rigorous accuracy requirements for the waveform templates, incompletely characterized noise spectra, non-stationary noises, and various data anomalies. Taking Taiji as a representative example, this paper reviews the data characteristics and data analysis challenges of space-based GW detection, and introduces the second round of Taiji Data Challenge, a collection of simulation datasets designed as a shared platform for resolving these critical issues. This platform distinguishes itself from previous works by the systematic integration of orbital dynamics based on a full drag-free and attitude control simulation, extended noise sources, more complicated and overlapping GW signals, second-generation time-delay interferometry, and the coupling effect of time-varying arm-lengths, etc. Concurrently released is the open-source toolkit Triangle, which offers the capabilities for customized simulation of signals, noises, and other instrumental effects. By taking a step further towards realistic detection, Taiji Data Challenge II and Triangle altogether serve as a new testbed, supporting the development of Taiji's global analysis and end-to-end pipelines, and ultimately bridging the gaps between observation and scientific objectives.

gr-qc↗

arXiv2026

Classifying Core-Collapse Supernova Gravitational Waves using Supervised Contrastive Learning

The detection and reconstruction of gravitational waves from core-collapse supernovae (CCSN) present significant challenges due to the highly stochastic nature of the signals and the complexity of detector noise. In this work, we introduce a deep learning framework utilizing a ResNet-50 encoder pre-trained via supervised contrastive learning to classify CCSN signals and distinguish them from instrumental noise artifacts. Our approach explicitly optimizes the feature space to maximize intra-class compactness and inter-class separability. Using a simulated four-detector network (LIGO Hanford, LIGO Livingston, Virgo, and KAGRA) and realistic datasets injecting magnetorotational and neutrino-driven waveforms, we demonstrate that the contrastive learning paradigm establishes a superior metric structure within the embedding space, significantly enhancing detection efficiency. At a false positive rate of $10^{-4}$, our method achieves a true positive rate (TPR) of nearly $100\%$ for both rotational and neutrino-driven signals within a distance range of $10$--$200$~kpc, while maintaining a TPR of approximately $80\%$ at $1200$~kpc. In contrast, traditional end-to-end methods yield a TPR below $20\%$ for rotational signals at distances $\geq 200$~kpc, and fail to exceed $60\%$ for neutrino-driven signals even at a close proximity of $10$~kpc.

astro-ph.HE↗

arXiv2025

Constrain magnetar parameters by taking into account the evolutionary effects of radius and moment of inertia with \emph{Swift}/XRT data

A newly born millisecond magnetar has been proposed as one possible central engine of some GRBs with X-ray plateau emission. In this work, we systematically analyzed the Swift/XRT data of long GRBs with plateau emission that were detected before 2023 December, and estimated the physical parameters by considering the $R/I$ evolutionary effects. We found that neglecting the $R/I$ evolutionary effects can lead to systematic overestimation or underestimation of magnetar parameters such as $B_p$, $P_0$, and $ε$ from 20\% to 50\%. We also found that some tight correlations, which can be approximately expressed as $ε\propto P_0^{1.57\pm0.22}$, $ε\propto B_p^{0.97\pm0.13}$, $B_p\propto P_0^{1.30\pm0.16}$, $E_{\rm wind}\propto E_{\rm jet,iso}^{0.83\pm0.07}(E_{\rm jet}^{0.76\pm0.06})$, $P_0\propto E_{\rm jet,iso}^{-0.29\pm0.03}(E_{\rm jet}^{-0.26\pm0.02})$, $B_p\propto E_{\rm jet,iso}^{-0.58\pm0.06}(E_{\rm jet}^{-0.55\pm0.05})$, and $ε\propto E_{\rm jet,iso}^{-0.55\pm0.07}(E_{\rm jet}^{-0.52\pm0.06})$ for our selected EoSs. The universal correlations suggest that a nascent magnetar with the faster $P_0$, lower $B_p$, and lower $ε$ are more inclined to power a more energetic GRB jet, and the $ε$ and $P_0$ of newborn magnetar are likely to originate from the magnetically induced distortion and correspond to the equilibrium spin period as a result of interaction between the magnetar and its accretion disk, respectively. Finally, we found that the GW signals from the remnants of those GW-dominated GRBs with redshift measurements cannot reach aLIGO sensitivity threshold, and only two cases (GRBs 150323A and 170607A) can reach ET sensitivity threshold. Future GW observations could not only offer the first smoking gun that a protomagnetar can serve as the central engine of GRBs but also play a crucial role in precisely constraining the neutron star EoS.

astro-ph.HE↗

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

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification

Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled synthetic data under controlled conditions, reducing the dependency on costly and time-consuming experimental labeling. However, neural networks trained solely on synthetic data often suffer from degraded performance when applied to experimental data due to domain shifts. To bridge this domain gap, the proposed framework integrates domain adversarial training, minimum class confusion loss, and the Bootstrap Your Own Latent (BYOL) strategy. These components work jointly to facilitate effective knowledge transfer from the labeled simulation domain to the unlabeled experimental domain, achieving accurate and reliable damage classification in concrete. Extensive experiments demonstrate that the proposed method achieves notable performance improvements, reaching an accuracy of 0.7762 and a macro F1 score of 0.7713, outperforming both the plain 1D CNN baseline and six representative domain adaptation techniques. Moreover, the method exhibits high robustness across training runs and introduces only minimal additional computational cost. These findings highlight the practical potential of the proposed simulation-driven and label-efficient framework for real-world applications in structural health monitoring.

cs.CE↗

arXiv2024

Waveform Reconstruction of Core-Collapse Supernova Gravitational Waves with Improved Multisynchrosqueezing Transform

Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a means to probe the internal physical properties of supernovae. However, due to their complex time-frequency structure, effectively searching for and extracting GW signals from CCSNe remains an unsolved challenge. In this paper, we apply the improved multisynchrosqueezing transform (IMSST) method to reconstruct simulated GW data based on the advanced LIGO (aLIGO) and Einstein Telescope (ET) detectors. These data are generated by the magnetorotational and neutrino-driven mechanisms, and we use the match score as the criterion for evaluating the quality of the reconstruction. To assess whether the reconstructed waveforms correspond to true GW signals, we calculate the false alarm probability of reconstruction (FAPR). For GW sources located at 10 kpc and datasets where the waveform amplitudes are normalized to $5 \times 10^{-21}$ observed by aLIGO, FAPR are $2.1 \times 10^{-2}$ and $6.2 \times 10^{-3}$, respectively. For GW sources at 100 kpc and with waveform amplitudes normalized to $5 \times 10^{-21}$ observed by ET, FAPR are $1.3 \times 10^{-1}$ and $1.5 \times 10^{-2}$, respectively. When the gravitational wave strain reaches $7 \times 10^{-21}$ and the match score threshold is set to 0.75, the IMSST method achieves maximum reconstruction distances of approximately 37 kpc and 317 kpc for aLIGO and ET, respectively. Finally, we compared the performance of IMSST and STFT in waveform reconstruction based on the ET. The results show that the maximum reconstructable distance using STFT is 186 kpc.

astro-ph.HE↗

arXiv2024

The Progenitor and Central Engine of short-duration GRB 201006A associated with a coherent radio flash

Recently, the detection of a coherent radio flash associated with short-duration GRB 201006A, occurring 76.6 minutes after the burst, has attracted great attention. However, the physical origin of the coherent radio flash remains under debate. By reanalyzing its data observed by Fermi and Swift, we find that an early radio afterglow as the physical origin of the radio flash can be ruled out, but the coherent radio emission seems to be consistent with the hypothesis of a supramassive magnetar as the central engine collapsing into a black hole. Within this scenario, the derived magnetar surface magnetic field ($B_{\rm p}$) and the initial spin period ($P_{\rm 0}$) fall into a reasonable range but require a preferable low value of $η_{\rm R} = 10^{-7}$ or $10^{-6}$. Moreover, the calculated low-$\varepsilon$ value and $E_{\rm γ,iso}-E_{\rm p}$ correlation of GRB 201006A also supports the progenitor which is from the merger of compact stars. We also discuss the non-detected kilonova emission associated with GRB 201006A, and then compare with its upper limits of optical observations.

astro-ph.HE↗

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

Waveform Reconstruction of Core-Collapse Supernovae Gravitational-Waves with Ensemble Empirical Mode Decomposition

The gravitational waves (GW) from core-collapse supernovae (CCSN) have been proposed as a probe to investigate physical properties inside of the supernova. However, how to search and extract the GW signals from core-collapse supernovae remains an open question due to its complicated time-frequency structure. In this paper, we apply the Ensemble Empirical Mode Decomposition (EEMD) method to decompose and reconstruct simulated GW data generated by magnetorotational mechanism and neutrino-driven mechanism within the advanced LIGO, using the match score as the criterion for assessing the quality of the reconstruction. The results indicate that by decomposing the data, the sum of the first six intrinsic mode functions (IMFs) can be used as the reconstructed waveform. To determine the probability that our reconstructed waveform corresponds to a real GW waveform, we calculate the false alarm probability of reconstruction (FAPR). By setting the threshold of the match score to be 0.75, we obtain FAPR of GW sources at a distance of 5 kpc and 10 kpc to be $6\times10^{-3}$ and $1\times10^{-2}$ respectively. If we normalize the maximum amplitude of the GW signal to $5\times10^{-21}$, the FAPR at this threshold is $4\times10^{-3}$. Furthermore, in our study, the reconstruction distance is not equivalent to the detection distance. When the strain of GW reaches $7 \times 10^{-21}$, and the match score threshold is set at 0.75, we can reconstruct GW waveform up to approximately 36 kpc.

astro-ph.HE↗

arXiv2024

Ejecta-circumstellar medium interaction in high-density environment contribution to kilonova emission: Application to GRB 191019A

The nearby long-duration GRB 191019A recently detected by Swift lacks an associated supernova and belongs to a host galaxy with little star formation activity, suggesting that the origin of this burst is the result of a merger of two compact objects with dynamical interactions in a high-density medium of an active galactic nucleus. Given the potential motivation of this event, and given that it occurs in such a high-density environment, the ejecta-circumstellar medium (CSM) interaction cannot be ignored as possibly contributing to the kilonova emission. Here, we theoretically calculate the kilonova emission by considering the contribution of the ejecta-CSM interaction in a high-density environment. We find that the contribution to the kilonova emission from the ejecta-CSM interaction will dominate at a later time, and a smaller ejecta mass will have a stronger kilonova emission from the ejecta-CSM interaction. Moreover, we try to apply it to GRB 191019A, but we find that it is difficult to identify the possible kilonova emission from the observations, due to the contribution of the bright host galaxy. On the other hand, less injected mass (less than $M_{\rm ej}=2\times10^{-5}M_{\odot}$) will be required if one can detect the kilonova emission associated with a GRB 191019A-like event in the future. The {\em r}-process-powered and spin energy contributions from the magnetar are also discussed.

astro-ph.HE↗

arXiv2024

The progenitor and central engine of a peculiar GRB 230307A

Recently, a lack of supernova-associated long-duration gamma-ray burst (GRB 230307A) at such a low redshift $z=0.065$, but associated with a possible kilonova emission, has attracted great attention. Its heavy element nucleosynthesis and the characteristic of soft X-ray emission suggests that the central engine of GRB 230307A is magnetar which is originated from a binary compact star merger. The calculated lower value of $\varepsilon \sim 0.05$ suggests that the GRB 230307A seems to be with ambiguous progenitor. The lower value of $f_{\rm eff}=1.23$ implies that the GRB 230307A is not likely to be from the effect of "tip of iceberg". We adopt the magnetar central engine model to fit the observed soft X-ray emission with a varying efficiency and find that the parameters constraints of magnetar fall into a reasonable range, i.e., $B<9.4\times10^{15}$ G and $P<2.5$ ms for $Γ_{\rm sat} = 10^3$, and $B<3.6\times10^{15}$ G and $P<1.05$ ms for $Γ_{\rm sat} = 10^4$. Whether the progenitor of GBR 230307A is from the mergers of neutron star - white dwarf (NS - WD) or neutron star - neutron star (NS - NS) remains unknown.

astro-ph.HE↗

arXiv2023

On the possibility to detect gravitational waves from post-merger super-massive neutron stars with a kilohertz detector

The detection of a secular post-merger gravitational wave (GW) signal in a binary neutron star (BNS) merger serves as strong evidence for the formation of a long-lived post-merger neutron star (NS), which can help constrain the maximum mass of NSs and differentiate NS equation of states. We specifically focus on the detection of GW emissions from rigidly rotating NSs formed through BNS mergers, using several kilohertz GW detectors that have been designed. We simulate the BNS mergers within the detecting limit of LIGO-Virgo-KARGA O4 and attempt to find out on what fraction the simulated sources may have a detectable secular post-merger GW signal. For kilohertz detectors designed in the same configuration of LIGO A+, we find that the design with peak sensitivity at approximately $2{\rm kHz}$ is most appropriate for such signals. The fraction of sources that have a detectable secular post-merger GW signal would be approximately $0.94\% - 11\%$ when the spindowns of the post-merger rigidly rotating NSs are dominated by GW radiation, while be approximately $0.46\% - 1.6\%$ when the contribution of electromagnetic (EM) radiation to the spin-down processes is non-negligible. We also estimate this fraction based on other well-known proposed kilohertz GW detectors and find that, with advanced design, it can reach approximately $12\% - 45\%$ for the GW-dominated spindown case and $4.7\% - 16\%$ when both the GW and EM radiations are considered.

gr-qc↗