arXiv subjects

Xue-Hao Zhang

Publications and source records attributed to Xue-Hao Zhang.

6 recordsLinked to original sources

arXiv2026

Milky Way Structure from Double White Dwarf Gravitational-Wave Sources

The millihertz gravitational-wave sky will be dominated by $\sim10^{8}$ double white dwarfs while $\sim10^{4}$ be resolvable in the Milky Way, whose three-dimensional spatial distribution traces the Galaxy's structural parameters. We present \textsc{Galena}, a hierarchical Bayesian pipeline that recovers these parameters from a double white dwarf catalogue through an inhomogeneous Poisson-process likelihood. Each source's $3\times3$ Galactic position covariance is computed from the waveform Fisher information matrix and propagated analytically through a $3\times5$ Jacobian; the resulting measurement-error convolution is factored into a pre-computed sparse weight matrix, rendering the nested-sampling inference tractable. Applied to GBSIEVER-reported LISA Data Challenge sources ($N=2151$ after quality cuts), \textsc{Galena} recovers the disk scale length $R_d=2175^{+51}_{-52}$~pc and scale height $z_d=282^{+7}_{-7}$~pc, consistent with a canonical thin disk, together with the bulge fraction $A=0.187^{+0.011}_{-0.012}$ and bulge scale radius $R_b=773^{+26}_{-25}$~pc; the bulge fraction, now recovered much closer to the literature value of $0.25$ than in our earlier exact-position fit, is measured at $\sim6\%$ statistical precision. An independent particle-swarm optimisation of the same likelihood reproduces these values to within a fraction of a percent, supporting the attribution of the improvement to the Fisher-matrix error propagation rather than to the sampler. The search-stage astrophysical prior improves per-source distance estimates but leaves the hierarchical inference essentially unchanged.

astro-ph.GA↗

arXiv2026

Improving the resolution of double white dwarf systems with spaceborne gravitational wave observatories using a robust astrophysical prior

Resolving the crowded population of double white dwarf (DWD) binaries in data from spaceborne gravitational wave (GW) observatories (e.g., LISA, Taiji) remains a major analysis challenge. Comparable performance on addressing this problem has been achieved with two main approaches: global fit, in which resolvable sources are estimated simultaneously from the data, and iterative, where sources are estimated one at a time and subtracted out from the data. While the latter is computationally efficient, methods developed under this approach have traditionally followed a frequentist framework that ignores astrophysical priors. This work incorporates a strong astrophysical prior, derived from the mass limits of detached white dwarfs and linking the GW signal frequency $f$ with its time derivative $\dot{f}$, into the iterative $\mathtt{GBSIEVER}$ pipeline. Applied to simulated LISA and LISA-Taiji network data, the method increases the number of confidently resolved sources by ${\approx}7.3\%$ (LISA-only) and ${\approx}14.6\%$ (network), respectively, and improves parameter estimation accuracy. The improvement persists across multiple realistic DWD population realizations, including in the low-frequency confusion-dominated regime, demonstrating the robustness and practical utility of astrophysically informed priors in iterative source extraction.

gr-qc↗

arXiv2023

Fast resolving Galactic binaries in LISA data and its ability to study the Milky Way

Resolving individual gravitational waves from tens of millions of double white dwarf (DWD) binaries in the Milky Way is a challenge for future space-based gravitational wave detection programs. By using previous data to define the priors for the next search, we propose an accelerated approach of searching the DWD binaries and demonstrate its efficiency based on the GBSIEVER detection pipeline. Compared to the traditional GBSIEVER method, our method can obtain $\sim 50\%$ of sources with 2.5\% of the searching time for LDC1-4 data. In addition, we find that both methods have a similar ability to detect the Milky Way structure by their confirmed sources. The relative error of distance and chirp mass is about 20\% for DWD binaries whose gravitational wave frequency is higher than $4\times10^{-3}$ Hz, even if they are close to the Galactic center. Finally, we propose a signal-to-noise ratio (SNR) threshold for LISA to confirm the detection of DWD binaries. The threshold should be 16 when the gravitational wave frequency is lower than $4\times10^{-3}$ Hz and 9 when the frequency range is from $4\times10^{-3}$ Hz to $1.5\times10^{-2}$ Hz.

astro-ph.HE↗

arXiv2023

White dwarf binary modulation can help stochastic gravitational wave background search

For the stochastic gravitational wave backgrounds (SGWBs) search centred at the milli-Hz band, the galactic foreground produced by white dwarf binaries (WDBs) within the Milky Way contaminates the extra-galactic signal severely. Because of the anisotropic distribution pattern of the WDBs and the motion of the spaceborne gravitational wave interferometer constellation, the time-domain data stream will show an annual modulation. This property is fundamentally different from those of the SGWBs. In this Letter, we propose a new filtering method for the data vector based on the annual modulation phenomenon. We apply the resulted inverse variance filter to the LISA data challenge. The result shows that for the weaker SGWB signal, such as energy density $Ω_{\rm astro}=1\times10^{-12}$, the filtering method can enhance the posterior distribution peak prominently. For the stronger signal, such as $Ω_{\rm astro}=3\times10^{-12}$, the method can improve the Bayesian evidence from `substantial' to `strong' against null hypotheses. This method is model-independent and self-contained. It does not ask for other types of information besides the gravitational wave data.

gr-qc↗

arXiv2022

Resolving Galactic binaries using a network of space-borne gravitational wave detectors

Extracting gravitational wave (GW) signals from individual Galactic binaries (GBs) against their self-generated confusion noise is a key data analysis challenge for space-borne detectors operating in the $\approx 0.1$ mHz to $\approx 10$ mHz range. Given the likely prospect that there will be multiple such detectors, namely LISA, Taiji, and Tianqin, with overlapping operational periods in the next decade, it is important to examine the extent to which the joint analysis of their data can benefit GB resolution and parameter estimation. To investigate this, we use realistic simulated LISA and Taiji data containing the set of $30\times 10^6$ GBs used in the first LISA data challenge (Radler), and an iterative source extraction method called GBSIEVER introduced in an earlier work. We find that a coherent network analysis of LISA-Taiji data boosts the number of confirmed sources by $\approx 75\%$ over that from a single detector. The residual after subtracting out the reported sources from the data of any one of the detectors is much closer to the confusion noise expected from an ideal, but infeasible, multisource resolution method that perfectly removes all sources above a given signal-to-noise ratio threshold. While parameter estimation for sources common to both the single detector and network improves broadly in line with the enhanced signal to noise ratio of GW sources in the latter, deviation from the scaling of error variance predicted by Fisher information analysis is observed for a subset of the parameters.

gr-qc↗

arXiv2021

Resolving Galactic binaries in LISA data using particle swarm optimization and cross-validation

The space-based gravitational wave (GW) detector LISA is expected to observe signals from a large population of compact object binaries, comprised predominantly of white dwarfs, in the Milky Way. Resolving individual sources from this population against its self-generated confusion noise poses a major data analysis problem. We present an iterative source estimation and subtraction method to address this problem based on the use of particle swarm optimization (PSO). In addition to PSO, a novel feature of the method is the cross-validation of sources estimated from the same data using different signal parameter search ranges. This is found to greatly reduce contamination by spurious sources and may prove to be a useful addition to any multi-source resolution method. Applied to a recent mock data challenge, the method is able to find $O(10^4)$ Galactic binaries across a signal frequency range of $[0.1,15]$ mHz, and, for frequency $\gtrsim 4$ mHz, reduces the residual data after subtracting out estimated signals to the instrumental noise level.

gr-qc↗