Gravitational wave parameter estimation is to infer the source properties of a gravitational wave event, such as the component masses and spins, and the sky location.

Parameter estimation speed has been crucial to gravitational wave astronomy and mulitimessenger astronomy. Analyses on longer signals such as binary neutron stars can take months with traditional methods.

Researchers have been seeking for new ways to reduce parameter estimation runtime. These efforts include multibanding, reduced order quadrature (such as the PyROQ package), and machine learning (currently state-of-art; such as software DINGO).

Dark matter has been an unsolved puzzle for nearly a century. A wide range of candidates have been searched for using various detectors. Though not designed for dark matter searches, the high sensitivity of gravitational wave detectors such as LIGO can be used to probe dark matter particles as they strike the test masses of the detectors and thus cause minuscule distortions in space and time.

Since the first direct detection of a gravitational wave in Sep 2015, we have detected 90 gravitational waves, most of which were from colliding black holes and a few were from colliding neutron stars with/without black holes. These detections have enabled unmatched tests in fundamental physics, nuclear physics, astrophysics, cosmology, and gravity theory itself. As the detector sensitivity keeps going up, we expect to detect over 200 such binary systems in the O4 observing run that will start in 2023.