arXiv subjects

Shang-Jie Jin

Publications and source records attributed to Shang-Jie Jin.

At least 19 recordsLinked to original sources

arXiv2026

Factorized neural posterior estimation for rapid and reliable inference of parameterized post-Einsteinian deviation parameters in gravitational waves

The direct detection of gravitational waves (GWs) by LIGO has confirmed general relativity (GR), but testing GR via GWs requires estimating parameterized post-Einsteinian (ppE) deviation parameters in waveform models. Traditional Bayesian inference methods like Markov chain Monte Carlo (MCMC) provide reliable estimates but suffer from prohibitive computational costs, failing to meet the real-time demands and surging data volume of future GW detectors. Here, we propose a factorized neural posterior estimation framework: we construct independent normalizing flow models for each of the nine ppE deviation parameters, and effectively integrate prior information from other source parameters via a conditional embedding network. Leveraging a hybrid neural network with a convolutional neural network and a Residual Neural Network for feature extraction, our method performs rapid and statistically reliable posterior inference directly from binary black hole signals. Compared to conventional MCMC, our approach achieves millisecond-scale inference time with a speedup factor of $9 \times 10^4$. Comprehensive validations show that the posterior estimates pass the Kolmogorov-Smirnov test and achieve empirical coverage probabilities close to theoretical targets. We apply this method to the GW150914 event and find that the resulting posterior distributions are in good agreement with existing results. This work demonstrates the great potential of deep learning for GW parameter estimation and provides a viable technical solution for real-time GR tests with next-generation detectors.

astro-ph.IM↗

arXiv2026

Three-band dark-siren cosmology with intermediate mass black hole binaries: Synergy of Taiji, LGWA, and the Einstein Telescope

Gravitational-wave (GW) dark sirens provide an independent probe of the cosmic expansion history. Their cosmological constraining power, however, depends critically on precise luminosity-distance measurements and sky localizations for cross-matching with galaxy catalogs. Multiband GW observations can track GW events across different frequency bands and thus improve both. Motivated by this, we forecast the cosmological potential of intermediate-mass black hole binaries (IMBHBs) observed by a three-band GW detector network composed of Taiji (TJ), the Lunar Gravitational-wave Antenna (LGWA), and the Einstein Telescope (ET). We simulate detectable IMBHB populations and analyze them with a hierarchical Bayesian dark-siren framework that includes galaxy-catalog completeness and redshift uncertainties. We find that the TJ-LGWA-ET network outperforms all two-detector configurations considered here. In the $Λ$CDM model, it constrains the Hubble constant and matter density to $\sim 0.12\%$ and $\sim 0.6\%$, respectively. In the $w$CDM model, a 4-year dark-siren sample alone constrains the dark-energy equation-of-state parameter $w$ to $\sim 2.7\%$. Adding baryon acoustic oscillation (BAO) and Type Ia supernova (SNe Ia) data improves the $w$ constraint to $\sim 2.1\%$, slightly better than that from the current CMB+BAO+SNe Ia combination. We also show that the final constraints remain sensitive to IMBHB population assumptions and galaxy-catalog limitations, which highlights the need for deep galaxy surveys with precise redshift measurements.

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

Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows

Transient noise artifacts, commonly referred to as glitches, pose a major challenge to parameter inference for space-based gravitational-wave (GW) observations. We develop a glitch-robust amortized inference framework for massive black hole binaries in the Taiji detector configuration by combining conditional normalizing flows, a time-frequency multimodal fusion encoder, and contrastive learning. To enable large-scale training on contaminated data, we further introduce a neural glitch generator that produces high-fidelity synthetic transients at substantially reduced computational cost. Systematic experiments show that, under glitch contamination, the proposed method yields more accurate and better-calibrated posteriors than a conventional Markov Chain Monte Carlo baseline. In ablation studies, the full time-frequency model with contrastive learning performs best overall and remains robust to variations in glitch duration and merger-relative timing. We further show that standard coverage diagnostics alone are insufficient to fully assess posterior fidelity. We therefore complement them with the continuous ranked probability score, which provides a stricter assessment of global distributional agreement in non-ideal GW data. Taken together, these results establish deep-learning-based amortized inference as a promising framework for fast and robust Bayesian parameter estimation in future space-based GW observations.

gr-qc↗

arXiv2026

Simultaneous determination of Hubble constant and cosmic baryon density: Forecasts for the synergy between FRBs and emerging probes

Two of the most pressing challenges in cosmology are the persistent discrepancy in measurements of the Hubble constant, referred to as the Hubble tension, and the deficit of baryons in the local Universe, known as the missing baryon problem. Fast radio bursts (FRBs) provide a unique probe of both the Hubble constant $H_0$ and the cosmic baryon density $Ω_{\rm b}$. However, constraints from FRBs alone suffer from a severe $H_0$-$Ω_{\rm b}$ degeneracy that prevents them from resolving either problem. We show that this degeneracy can be broken by combining FRBs with other emerging probes whose degeneracy directions differ in the $H_0$-$Ω_{\rm b}$ plane. Specifically, we quantify three multi-messenger approaches: FRBs paired with gravitational wave (GW) standard sirens, strong gravitational lensing (SGL) time delays, and 21 cm intensity mapping (IM) surveys. The combinations FRB+GW, FRB+SGL, and FRB+21 cm IM each deliver simultaneous constraints on $H_0$ and $Ω_{\rm b}$ better than (1%, 1.5%) in the $Λ$CDM model, and when dynamical dark energy is introduced, the constraining precision degrades gracefully as model complexity increases. Furthermore, within a model-independent framework, both FRB+GW and FRB+SGL constrain $H_0$ and $Ω_{\rm b}$ to precisions better than (1.5%, 3%). These precision levels are based on nominal observational expectations and would improve significantly under optimistic observational scenarios.

astro-ph.CO↗

arXiv2026

Synergy between CSST and third-generation gravitational-wave detectors: Inferring cosmological parameters using cross-correlation of dark sirens and galaxies

Gravitational-wave (GW) events are generally believed to originate in galaxies and can thus serve, like galaxies, as tracers of the universe's large-scale structure. In GW observations, waveform analysis provides direct measurements of luminosity distances; however, without relying on a specific cosmological model, the redshifts of GW sources cannot be determined due to the mass-redshift degeneracy. By cross-correlating GW events with galaxies, one can establish a correspondence between luminosity distance and redshift shells, enabling cosmological inference. In this work, we explore the scientific potential of cross-correlating GW sources detected by third-generation (3G) ground-based GW detectors with the photometric redshift survey of the China Space Station Survey Telescope (CSST). We find that the constraint precisions of the Hubble constant and the matter density parameter can reach $1.04\%$ and $2.04\%$, respectively. Additionally, we have also constrained the precision of the GW clustering bias parameter. These results highlight the significant potential of the synergy between CSST and 3G ground-based GW detectors in constraining cosmological models and probing GW source formation channels using cross-correlation of dark sirens and galaxies.

astro-ph.CO↗

arXiv2026

How Dark Sector Equations of State Govern Interaction Signatures

Using late-Universe observations, we demonstrate that freeing dark energy and dark matter equations of state (EoS) dramatically alters the inferred strength and direction of their interactions. When dark sector EoS are fixed to $w_{\mathrm{de}}=-1$ and $w_{\mathrm{dm}}=0$, the data consistently favor an energy transfer from dark energy to dark matter across various interaction forms. This apparent evidence, however, proves highly sensitive to the EoS assumptions: treating $w_{\mathrm{de}}$ as a free parameter substantially weakens the evidence for interaction, with its value converging to the quintessence regime ($w_{\mathrm{de}}>-1$). In contrast, freeing $w_{\mathrm{dm}}$ maintains a preference for interaction, revealing a correlation where positive $w_{\mathrm{dm}}$ is associated with energy transfer from dark energy to dark matter, and negative $w_{\mathrm{dm}}$ with energy transfer from dark matter to dark energy. These findings caution against the simplistic assumption of $Λ$CDM EoS values when attempting to detect a possible interaction. Despite these fundamental degeneracies, model comparison indicates that interacting dark energy scenarios are positively to strongly supported by AIC and DIC, but only inconclusively to weakly supported by Bayesian evidence against the $Λ$CDM model.

astro-ph.CO↗

arXiv2026

Assessing the robustness of amortized simulation-based inference to transient noise in gravitational-wave ringdowns

Gravitational waves (GW) emitted by binary systems allow us to perform precision tests of general relativity in the strong field regime. Ringdown signals allow for probing black hole mass and spin with high precision in GW astronomy. With improvements in current and next-generation GW detectors, developing likelihood-free parameter inference methods is crucial. This is especially important when facing challenges such as non-standard noise, partial data, or incomplete signal models that prevent the use of analytical likelihood functions. In this work, we propose an amortized simulation-based inference strategy to estimate ringdown parameters directly. Specifically, our method is based on amortized neural posterior estimation, which trains a neural density estimator of the posterior for all data segments within the prior range. The results show that our trained amortized network achieves statistically consistent parameter estimates with valid confidence coverage compared to established Markov-chain methods, while offering inference speeds that are orders of magnitude faster. Furthermore, we evaluate the robustness of the method against transient noise contamination. Our analysis reveals that the timing of glitch injection has a decisive impact on estimation bias, particularly during the tail of a signal with sparse information. Glitch strength is positively correlated with estimation error, but has limited effect at low signal-to-noise ratios. Mass and spin parameters are most sensitive to noise. This study not only provides an efficient and accurate inference framework for ringdown analysis but also lays a foundation for developing robust data-processing pipelines for future GW astronomy in realistic noise environments.

gr-qc↗

arXiv2026

Gravitational wave standard sirens: A brief review of cosmological parameter estimation

Gravitational wave (GW) observations are expected to serve as a powerful and independent probe of the expansion history of the universe. By providing direct and calibration-free measurements of luminosity distances through waveform analysis, GWs provide a fundamentally different and potentially more robust approach to measuring cosmic-scale distances compared to traditional electromagnetic (EM) observations, which is known as the standard siren method. In this review, we present an overview of recent developments in GW standard siren cosmology, including up-to-date $H_0$ constraints, and prospects for constraining cosmological parameters using future GW detections. A central focus of this review is the unique ability of GW observations to break cosmological parameter degeneracies inherent in the EM observations. We also briefly highlight the impact of systematic uncertainties, such as detector calibration, weak lensing, peculiar velocities, and host-galaxy catalog completeness, and corresponding potential mitigation strategies, which currently limit the constraint precision of cosmological parameters. Looking forward, we highlight the importance of combining GW standard sirens with other emerging late-universe cosmological probes such as fast radio bursts, 21 cm intensity mapping, and strong gravitational lensing to forge a precise cosmological probe for exploring the late universe.

astro-ph.CO↗

arXiv2025

Probing the sign-changeable interaction between dark energy and dark matter with DESI baryon acoustic oscillations and DES supernovae data

There is a possibility of interaction between dark energy and dark matter, and this interaction may also undergo a sign change during the evolution of the universe. In this paper, we utilize the latest observational data to constrain models of a sign-changeable interaction. The data we employ, in addition to the cosmic microwave background data, also encompass the first-year baryon acoustic oscillation data from DESI and the type Ia supernova data of the full 5-year observation from DES. To achieve high generality, we investigate four interacting dark energy (IDE) models with different forms of the interaction term $Q$: (i) IDE1 with $Q = β(a)Hρ_{\rm de}$; (ii) IDE2 with $Q = β(a)Hρ_{\rm c}$; (iii) IDE3 with $Q = β(a)H_0ρ_{\rm de}$; (iv) IDE4 with $Q = β(a)H_0ρ_{\rm c}$. From the analysis, we observe that $β(z) > 0$ at early times and $β(z) < 0$ at late times, with the coupling $β(z)$ crossing the non-interacting line $β(z) = 0$ during cosmic evolution at the 2$σ$ confidence level for the IDE1, IDE3, and IDE4 models. However, for the IDE2 model, $β(z)$ remains consistently negative and does not cross $β(z) = 0$ at the 2$σ$ confidence level. Our findings indicate that the energy transfer is from dark matter to dark energy when dark matter dominates the universe, and from dark energy to dark matter when dark energy dominates, for the IDE1 and IDE3 models. Furthermore, Bayesian evidence suggests that the IDE1 and IDE3 models are moderately preferred over the $Λ$CDM model. The overall outcomes of this study clearly indicate that, based on current observational data, the sign-changeable IDE models are quite compelling and merit further attention.

astro-ph.CO↗

arXiv2025

Search for exotic gravitational wave signals beyond general relativity using deep learning

The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high signal-to-noise ratio events from the observational runs suggest that standard waveform templates, which assume strict adherence to GR, might overlook signals from alternative theories of gravity. Incorporating these exotic signals into traditional search algorithms is computationally infeasible due to the vast template space required. This paper introduces a proof-of-principle deep learning framework for detecting exotic GW signals, leveraging neural networks trained on GR-based templates. Through their generalization ability, neural networks learn intricate features from the data, enabling the detection of signals that deviate from GR. We present the first study evaluating the capability of deep learning to detect beyond-GR signals, including a variety of PN orders. Our model achieves rapid and accurate identification of exotic GW signals across different luminosity distances, with performance comparable to GR-based detections. In particular, applying the model to the GW150914 event demonstrates excellent performance, highlighting the potential of AI-driven methods for detecting previously overlooked signals beyond GR. This work paves the way for new discoveries in gravitational wave astronomy, enabling the detection of signals that might escape traditional search pipelines.

gr-qc↗

arXiv2025

Enhancing dark siren cosmology through multi-band gravitational wave synergetic observations

Multi-band gravitational-wave (GW) standard siren observations are poised to herald a new era in the study of cosmic evolution. These observations offer higher signal-to-noise ratios and improved localizations compared to those achieved with single-band GW detection, which are crucial for the cosmological applications of dark sirens. In this work, we explore the role multi-band GW synergetic observations will play in measuring cosmological parameters, particularly in comparison with single GW observatory data. We used mock multi-band dark siren data from third-generation GW detectors and the baseline Decihertz Interferometer Gravitational-Wave Observatory to infer cosmological parameters. Our analysis shows that multi-band GW observations significantly improve sky localization accuracy by two to three orders of magnitude over single-band observations, although their impact on luminosity distance error remains limited. This results in a substantial improvement in the constraints on matter density and the Hubble constant, enhancing their constraint precision by $60\%$-$90\%$ and $52\%$-$85\%$, respectively. We conclude that the significant potential of multi-band GW synergistic observations for detecting GW signals and resolving the Hubble tension is highly promising and warrants anticipation.

astro-ph.CO↗

arXiv2024

Constraints on Interacting Dark Energy Models from the DESI Baryon Acoustic Oscillation and DES Supernovae Data

The recent results from the first year baryon acoustic oscillations (BAO) data released by the Dark Energy Spectroscopic Instrument (DESI), combined with cosmic microwave background (CMB) and type Ia supernova (SN) data, have shown a detection of significant deviation from a cosmological constant for dark energy. In this work, we utilize the latest DESI BAO data in combination with the SN data from the full five-year observations of the Dark Energy Survey and the CMB data from the Planck satellite to explore potential interactions between dark energy and dark matter. We consider four typical forms of the interaction term $Q$. Our findings suggest that interacting dark energy (IDE) models with $Q \propto ρ_{\rm de}$ support the presence of an interaction where dark energy decays into dark matter. Specifically, the deviation from $Λ$CDM for the IDE model with $Q=βH_0ρ_{\rm de}$ reaches the $3σ$ level. These models yield a lower value of Akaike information criterion than the $Λ$CDM model, indicating a preference for these IDE models based on the current observational data. For IDE models with $Q\proptoρ_{\rm c}$, the existence of interaction depends on the form of the proportionality coefficient $Γ$. The IDE model with $Q=βHρ_{\rm c}$ yields $β=0.0003\pm 0.0011$, which essentially does not support the presence of the interaction. In general, whether the observational data support the existence of interaction is closely related to the model. Our analysis helps to elucidate which type of IDE model can better explain the current observational data.

astro-ph.CO↗

arXiv2024

Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have offered an alternative and complementary approach to traditional matched filtering methods for the identification of gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial for the progress of GW physics and multi-messenger astronomy, particularly in light of the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA. In this work, we use the 2D U-Net algorithm to identify the time-frequency domain GW signals from stellar-mass binary black hole (BBH) mergers. We simulate BBH mergers with component masses from 5 to 80 $M_{\odot}$ and account for the LIGO detector noise. We find that the GW events in the first and second observation runs could all be clearly and rapidly identified. For the third observing run, about $80\%$ GW events could be identified. In particular, GW190814, currently unknown, is a special case that can be identified by the network, while other binary neutron star mergers and neutron star-black hole mergers can not be identified. Compared to the traditional convolutional neural network, the U-Net algorithm can output the time-frequency domain signal images rather than probabilities, providing a more intuitive investigation. Moreover, some of the results through U-Net can provide preliminary inference on the chirp mass information. In conclusion, the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers and potentially be helpful for future parameter inferences.

gr-qc↗

arXiv2024

Standard siren cosmology in the era of the 2.5-generation ground-based gravitational wave detectors: bright and dark sirens of LIGO Voyager and NEMO

The 2.5-generation (2.5G) ground-based gravitational wave (GW) detectors LIGO Voyager and NEMO are expected to be operational in the late 2020s and early 2030s. In this work, we explore the potential of GW standard sirens observed by the 2.5G GW detectors in measuring cosmological parameters, especially for the Hubble constant. Using GWs to measure cosmological parameters is inherently challenging, especially for 2.5G detectors, given their limited capability, which results in weaker constraints on cosmological parameters from the detected standard sirens. However, the measurement of the Hubble constant using standard siren observations from Voyager and NEMO is still promising. For example, using bright sirens from Voyager and NEMO can measure the Hubble constant with a precision of about $2\%$ and $6\%$ respectively, and using the Voyager-NEMO network can improve the precision to about $1.6\%$. Moreover, bright sirens can be used to break the degeneracy of cosmological parameters generated by CMB data, and to a certain extent, 2.5G detectors can also play a role in this aspect. Observations of dark sirens by 2.5G detectors can achieve relatively good results in measuring the Hubble constant, with a precision of within $2\%$, and if combining observations of bright and dark sirens, the precision of the Hubble constant measurement can reach about $1.4\%$. Finally, we also discussed the impact of the uncertainty in the binary neutron star merger rate on the estimation of cosmological parameters. We conclude that the magnificent prospect for solving the Hubble tension is worth expecting in the era of the 2.5G ground-based GW detectors.

astro-ph.CO↗

arXiv2024

A comprehensive forecast for cosmological parameter estimation using joint observations of gravitational waves and short $γ$-ray bursts

In the third-generation (3G) gravitational-wave (GW) detector era, the multi-messenger GW observation for binary neutron star (BNS) merger events can exert great impacts on exploring the cosmic expansion history. In this work, we comprehensively explore the potential of 3G GW standard siren observations in cosmological parameter estimations by considering the 3G GW detectors and the future short $γ$-ray burst (GRB) detector THESEUS-like telescope joint observations. Based on the 10-year observation of different detection strategies, we predict that the numbers of detectable GW-GRB events are 334--674 with the redshifts $z<3.5$ and the inclination angles $ι<15^{\circ}$. For the cosmological analysis, we consider the $Λ$CDM, $w$CDM, $w_0w_a$CDM models, and interacting dark energy (IDE) models. We find that GW can tightly constrain the Hubble constant with precisions of $0.345\%-0.065\%$, but perform not well in constraining other cosmological parameters. Fortunately, GW could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB+BAO+SN (CBS). When combining the mock GW data with the CBS data, CBS+GW can tightly constrain the equation of state of dark energy $w$ with a precision of 1.26\%, close to the standard of precision cosmology. Meanwhile, the addition of GW to CBS could improve constraints on cosmological parameters by $34.2\%-94.9\%$. In conclusion, GW standard siren observations from 3G GW detectors could play a crucial role in helping solve the Hubble tension and probe the fundamental nature of dark energy.

astro-ph.CO↗

arXiv2024

Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave (GW) signals, exerting a notable impact on the processing of GW data. The inference of GW parameters, crucial for GW astronomy research, is particularly susceptible to such interference. In this study, we pioneer the utilization of temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters, seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency domains. Remarkably, our findings indicate that the accuracy of this inference method is comparable to traditional non-glitch sampling techniques. Furthermore, our approach exhibits greater efficiency, boasting processing times on the order of milliseconds. In conclusion, the application of normalizing flow emerges as pivotal in handling GW signals affected by transient noises, offering a promising avenue for enhancing the field of GW astronomy research.

gr-qc↗

arXiv2024

Prospects for probing the interaction between dark energy and dark matter using gravitational-wave dark sirens with neutron star tidal deformation

Gravitational wave (GW) standard siren observations provide a rather useful tool to explore the evolution of the Universe. In this work, we wish to investigate whether dark sirens with neutron star (NS) deformation from third-generation GW detectors could help probe the interaction between dark energy and dark matter. We simulate the GW dark sirens of four detection strategies based on 3 yr observation and consider four phenomenological interacting dark energy (IDE) models to perform cosmological analysis. We find that GW dark sirens could provide tight constraints on $Ω_{\rm m}$ and $H_0$ in the four IDE models, but do not perform well in constraining the dimensionless coupling parameter $β$ in models of the interaction proportional to the energy density of cold dark matter. Nevertheless, the parameter degeneracy orientations of cosmic microwave background (CMB) and GW are almost orthogonal, and thus, the combination of them could effectively break cosmological parameter degeneracies, with the constraint errors of $β$ being $0.00068-0.018$. In addition, we choose three typical equations of state (EoSs) of an NS, i.e., SLy, MPA1, and MS1, to investigate the effect of an NS's EoS in cosmological analysis. The stiffer EoS could give tighter constraints than the softer EoS. Nonetheless, the combination of CMB and GW dark sirens (using different EoSs of an NS) shows basically the same constraint results of cosmological parameters. We conclude that the dark sirens from 3G GW detectors would play a crucial role in helping probe the interaction between dark energy and dark matter, and the CMB+GW results are basically not affected by the EoS of an NS.

astro-ph.CO↗