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Peng Xu

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • gr-qc3
  • physics.comp-ph1
same name
  • Peng Xu — 19 papers, h 20
  • Peng Xu — 16 papers, h 16
  • Peng Xu — 12 papers
  • Peng Xu — 10 papers, h 44
  • Peng Xu — 8 papers
  • Peng Xu — 8 papers, h 17

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedRapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

gr-qc2025

Gravitomagnetic-Hydrodynamics and Turbulence in Early Universe

Jiaxiang Liang, Peng Xu, Minghui Du +4

The nonlinear coupling between spacetime geometry and matter in the early Universe remains a frontier in theoretical cosmology. By introducing a novel gravitomagnetic-hydrodynamic…

gr-qc2025

Inter-Spacecraft Tilt-to-Length Noise Reduction Algorithm for Taiji Mission

Qiong Deng, Leiqiao Ye, Ke An +6

The Taiji mission for space-based gravitational wave (GW) detection employs laser interferometry to measure picometer-scale distance variations induced by GWs. The tilt-to-length (…

gr-qc2025

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises

Minghui Du, Ziren Luo, Peng Xu

The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequ…

physics.comp-ph2024★ 1 cited

Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning

Bo Liang, Hong Guo, Tianyu Zhao +11

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.