8 papers
Coherent End-to-End Search for Generic Extreme-Mass-Ratio Inspirals
Xiaobo Zou, Xingyu Zhong, Wen-Biao Han +1
Extreme-mass-ratio inspirals (EMRIs) encode more than strong-field orbital cycles and are key targets for space-borne gravitational-wave interferometers, yet coherent recove…
Red noise and evolving signals: a complete frequentist approach to supermassive black hole binary searches with pulsar timing array
Xuan Tao, Boris Goncharov, Yiqian Qian +2
Searches for gravitational waves (GWs) from isolated supermassive black hole binaries (SMBHBs) in pulsar timing array (PTA) data require simultaneous estimation of signal and noise…
Improving the resolution of double white dwarf systems with spaceborne gravitational wave observatories using a robust astrophysical prior
Shao-Dong Zhao, Xue-Hao Zhang, Soumya D. Mohanty +1
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 cha…
Scalable continuous gravitational wave detection in PTA data with non-parametric red noise suppression and optimal pulsar selection
Yi-Qian Qian, Yan Wang, Soumya D. Mohanty +1
Bayesian methods for the detection of continuous gravitational waves (CGWs) in Pulsar Timing Array (PTA) data incur substantial computational costs that grow rapidly due to the num…
Constraining the Deviation of Kerr Metric via Bumpy Parameterization and Particle Swarm Optimization in Extreme Mass-Ratio Inspirals
Xiaobo Zou, Xingyu Zhong, Wen-Biao Han +1
Measurement of deviations in the Kerr metric using gravitational wave (GW) observations will provide a clear signal of new Physics. Previous studies have developed multiple paramet…
Using normal to find abnormal: AI-based anomaly detection in gravitational wave data
Yi-Yang Guo, Soumya D. Mohanty, Xie Qunying +1
The detection and classification of anomalies in gravitational wave data plays a critical role in improving the sensitivity of searches for signals of astrophysical origins. We pre…