Výsledky vyhledávání

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Detection and Prediction of Future Massive Black Hole Mergers with Machine Learning and Truncated Waveforms

Autor: Houba, Niklas, Strub, Stefan H., Ferraioli, Luigi, Giardini, Domenico

We present a novel machine learning framework tailored to detect massive black hole binaries observed by spaceborne gravitational wave detectors like the Laser Interferometer Space Antenna (LISA) and predict their future merger times. The detection i

Externí odkaz: http://arxiv.org/abs/2405.11340

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Detection and Mitigation of Glitches in LISA Data: A Machine Learning Approach

Autor: Houba, Niklas, Ferraioli, Luigi, Giardini, Domenico

The proposed Laser Interferometer Space Antenna (LISA) mission is tasked with the detection and characterization of gravitational waves from various sources in the universe. This endeavor is challenged by transient displacement and acceleration noise

Externí odkaz: http://arxiv.org/abs/2401.00846

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Compensation of front-end and modulation delays in phase and ranging measurements for time-delay interferometry

Autor: Euringer, Philipp, Houba, Niklas, Hechenblaikner, Gerald, Mandel, Oliver, Soualle, Francis, Fichter, Walter

In the context of the Laser Interferometer Space Antenna (LISA), the laser subsystems exhibit frequency fluctuations that introduce significant levels of noise into the measurements, surpassing the gravitational wave signal by several orders of magni

Externí odkaz: http://arxiv.org/abs/2312.02888

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LISA Point-Ahead Angle Control for Optimal Tilt-to-Length Noise Estimation

Autor: Houba, Niklas, Delchambre, Simon, Ziegler, Tobias, Hechenblaikner, Gerald, Fichter, Walter

The Laser Interferometer Space Antenna (LISA) mission features a three-spacecraft long-arm constellation intended to detect gravitational wave sources in the low-frequency band up to 1 Hz via laser interferometry. The paper presents an open-loop cont

Externí odkaz: http://arxiv.org/abs/2208.11033

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