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MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz +20
We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitationa…
Mock data study for next-generation ground-based detectors: The performance loss of matched filtering due to correlated confusion noise
Shichao Wu, Alexander H. Nitz
The next-generation (3G/XG) ground-based gravitational-wave (GW) detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE) will begin observing in the next decade. Due to…
Sensitivity of spin-aligned searches for neutron star-black hole systems using future detectors
Rahul Dhurkunde, Alexander H. Nitz
Current searches for gravitational waves from compact-binary objects are primarily designed to detect the dominant gravitational-wave mode and assume that the binary components hav…
Parameter Estimation with Nonstationary Noise in Gravitational-wave Data
Sumit Kumar, Alexander H. Nitz, Xisco Jiménez Forteza
The sensitivity of gravitational-wave (GW) detectors is characterized by their noise curves, which determine the detector's reach and ability to measure the parameters of astrophys…