First community-wide machine learning gravitational wave search challenge with 6 algorithms tested on progressively realistic data including real O3a noise. Top ML methods achieve 95% of matched filtering sensitivity for Gaussian noise and 70% for real noise.
Ensemble deep learning model combining multiple CNNs successfully detects all O1/O2 BBH events (except GW170818) with zero false alarms on one month of O2 data, demonstrating real-time GW analysis capability.
Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for …
The exploration of the universe has recently entered a new era thanks to the multi-messenger paradigm, characterized by a continuous increase in the quantity and quality of experimental data that is obtained by the detection of the various cosmic …
The exploration of the universe has recently entered a new era thanks to the multi-messenger paradigm, characterized by a continuous increase in the quantity and quality of experimental data that is obtained by the detection of the various cosmic …
Since the early stages of operation of ground-based gravitational-wave interferometers, careful monitoring of these detectors has been an important component of their successful operation and observations. Characterization of gravitational-wave …