arXiv subjects

Shao-Dong Zhao

Publications and source records attributed to Shao-Dong Zhao.

4 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↗

arXiv2025

A forest of gravitational waves in our Galactic Centre

At the Galactic Centre, we can expect a population of a few tens of early extreme-mass ratio inspirals (E-EMRIs) and extremely large mass ratio inspirals (XMRIs). Depending on their evolutionary stage, they can be highly eccentric, with moderate signal-to-noise ratios (SNRs) of tens or hundreds, or nearly circular, with SNRs as large as a few thousand. Their individual signals combine into a common signal, which can complicate the resolution of other types of sources. We have calculated the foreground signal of continuous E-EMRIs and XMRIs using a catalog based on the expected number of sources and a realistic phase-space distribution. The forest of E-EMRIs will cover a large portion of the LISA sensitivity curve, obscuring the signals of some massive black hole binaries, verification binaries, and harmonics of EMRIs in their polychromatic phase. The combined signal from XMRIs will be much weaker but still affect intermediate-mass black hole binaries. Due to the large SNR, this forest can be also found in other galactic nuclei, such as that of the Andromeda galaxy. Even under conservative assumptions, the forest created by E-EMRIs and XMRIs in our Galactic Centre will likely pose a challenge for resolving other types of sources, as their contribution is non-coherent and exhibits large SNRs.

astro-ph.CO↗

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↗