C. Chatterjee

TL;DR: In this paper, a review of machine learning techniques for the analysis of ground-based gravitational-wave detector data is presented, including techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational wave searches, methods for fast measurements of the astrophysical parameters of gravitational wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise.

TL;DR: In this article, the authors present the design concept and science case for a neutron star extreme matter observatory (NEMO): a gravitational-wave interferometer optimized to study nuclear physics with merging neutron stars.

TL;DR: The Neutron Star Extreme Matter Observatory (NEMO) as discussed by the authors uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves.

TL;DR: Applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo searches, methods for fast measurements of the astrophysical parameters of gravitational- wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise.

TL;DR: The second GWTC-2.1 catalog as mentioned in this paper reports on a deeper list of candidate events observed over the same period, which employ three matched-filter search pipelines for candidate identification, and estimate the probability of astrophysical origin for each candidate event.