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The second generation of gravitational-wave detectors are being built and tuned all over the world. The detection of signals from binary black holes is beginning to fulfill the promise of gravitational-wave astronomy. In this work, we examine several possible configurations for third-generation laser interferometers in existing km-scale facilities. We propose a set of astrophysically motivated metrics to evaluate detector performance. We measure the impact of detector design choices against these metrics, providing a quantitative cost-benefit analyses of the resulting scientific payoffs.
Future ground-based gravitational-wave detectors are slated to detect black hole and neutron star collisions from the entire stellar history of the universe. To achieve the designed detector sensitivities, frequency noise from the laser source must b
Detuning the signal-recycling cavity length from a cavity resonance significantly improves the quantum noise beyond the standard quantum limit, while there is no km-scale gravitational-wave detector successfully implemented the technique. The detunin
Next-generation observations will revolutionize our understanding of binary black holes and will detect new sources, such as intermediate-mass black holes. Primary science goals include: Discover binary black holes throughout the observable Universe;
The second-generation of gravitational-wave detectors are just starting operation, and have already yielding their first detections. Research is now concentrated on how to maximize the scientific potential of gravitational-wave astronomy. To support
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques