arXiv subjects

Jian-Dong Liu

Publications and source records attributed to Jian-Dong Liu.

3 recordsLinked to original sources

arXiv2026

Probing Active Galactic Nuclei and Measuring the Hubble constant with Extreme-Mass-Ratio Inspirals

Extreme-mass-ratio inspirals (EMRIs) carry valuable information about their surrounding astrophysical environments. Over the course of their long-term evolution, interactions between the secondary object and the accretion disk can produce observable effects on both the orbital evolution and the emitted gravitational waveform. Based on the modifications to the companion's orbital evolution induced by the accretion disk environment, we investigate the feasibility of identifying the presence of accretion disk environmental effects in EMRI systems using gravitational wave signals. Within a Bayesian framework, we analyze the capability of EMRI systems with multiple parameter configurations to distinguish accretion disk environmental effects. Our results show that, under the $α$-disk model, all injected events can successfully identify the environment in which the EMRIs reside. Furthermore, we examined the improvement in the precision of Hubble constant measurements using the dark siren method after correctly identifying the accretion disk environment and constraining the relevant disk parameters. Constraining these environmental parameters may further deepen our understanding of the host environment, thereby enabling a more reliable inference of the physical properties of the accretion disk and its associated luminosity and ultimately improving the measurement of cosmological parameters. We find that the measurement precision for a single event can improve by as much as $20\%$. This work highlights the necessity of incorporating environmental effects into future EMRI data analysis. Proper modeling of such effects not only helps identify EMRI systems embedded in accretion disk environments but also further improves the precision of gravitational wave cosmological parameter inference.

gr-qc↗

arXiv2024

The Effect of Higher Harmonics On Gravitational Wave Dark Sirens

The gravitational wave (GW) signal from the merger of two black holes can serve as a standard sirens for cosmological inference. However, a degeneracy exists between the luminosity distance and the inclination angle between the binary system's orbital angular momentum and the observer's line of sight, limiting the precise measurement of the luminosity distance. In this study, we investigate how higher harmonics affect luminosity distance estimation for third-generation (3G) GW detectors in binary black hole mergers. Our findings demonstrate that considering higher harmonics significantly enhances distance inference results compared with using only the (2,2) mode. This improved accuracy in distance estimates also strengthens constraints on host galaxies, enabling more precise measurements of the Hubble constant. These results highlight the significant influence of higher harmonics on the range estimation accuracy of 3G ground-based GW detectors.

gr-qc↗

arXiv2024

Beimingwu: A Learnware Dock System

The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with the hope of solving new user tasks even beyond models' original purposes. In this paradigm, developers worldwide can submit their high-performing models spontaneously to the learnware dock system (formerly known as learnware market) without revealing their training data. Once the dock system accepts the model, it assigns a specification and accommodates the model. This specification allows the model to be adequately identified and assembled to reuse according to future users' needs, even if they have no prior knowledge of the model. This paradigm greatly differs from the current big model direction and it is expected that a learnware dock system housing millions or more high-performing models could offer excellent capabilities for both planned tasks where big models are applicable; and unplanned, specialized, data-sensitive scenarios where big models are not present or applicable. This paper describes Beimingwu, the first open-source learnware dock system providing foundational support for future research of learnware paradigm.The system significantly streamlines the model development for new user tasks, thanks to its integrated architecture and engine design, extensive engineering implementations and optimizations, and the integration of various algorithms for learnware identification and reuse. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security. Beimingwu supports the entire process of learnware paradigm. The system lays the foundation for future research in learnware-related algorithms and systems, and prepares the ground for hosting a vast array of learnwares and establishing a learnware ecosystem.

cs.SE↗