WoU-MMA: Gravitational Wave Data Analysis and Tools for Multi-Messenger Astrophysics

University Of Maryland, College Park, College Park MD

Investigators

Abstract

NSF’s LIGO observatories have produced outstanding scientific discoveries, beginning with the first direct detection of gravitational waves in 2015. To date, over 90 gravitational-wave signals have been detected by the LIGO Scientific Collaboration together with its international partners. One of the most exciting discoveries so far was the event named GW170817, which was produced when two neutron stars merged and was followed by a gamma-ray burst along with distinctive X-ray, visible-light, infrared, and radio emissions across the whole electromagnetic spectrum used by astronomers. That “multi-messenger” event revealed new information about the astrophysics of gamma-ray bursts, the physical properties of super-dense neutron stars, the speed of gravitational waves versus light, and the source of heavy elements like gold – a remarkable success of NSF’s “Windows on the Universe” initiative. The Advanced LIGO detectors, along with the European Virgo detector and Japanese KAGRA detector, are currently being prepared for their fourth observing run. This award is supporting research to improve the data analysis methods in order to increase the number of gravitational-wave signals that can be identified in the data, by using machine learning techniques to make use of measures of data quality, to enable even better studies of astrophysics and fundamental physics using gravitational waves. This award also supports real-time data analysis operations and rapid checks so that reliable candidate events can quickly be communicated to astronomers, to enable rapid follow-up observations leading to the next multi-messenger breakthroughs. The award supports the training of a graduate-student scientist and outreach activities to share LIGO science results and general science concepts with the public. The research supported by this award will advance the science program of NSF's LIGO and the project's international partners in two main areas: improving the detection of compact binary coalescence (CBC) events in the upcoming O4 observing run, and ensuring readiness to detect and study the next great multi-messenger event – or the next few, as nature provides. The PyCBC data analysis pipeline will be improved by incorporating data quality information into the detection statistic using machine learning methods, an approach which has been demonstrated (in preliminary studies) to improve the net sensitivity of searches. The upgraded PyCBC pipeline will be used both for low-latency searches and for the final production analysis feeding into the next LIGO-Virgo-KAGRA (LVK) transient event catalog, furthering LVK collaboration goals while developing the scientific skills and experience of a graduate student researcher. This award will also support the validation and monitoring of other data analysis pipelines, and tools and procedures to coordinate rapid response activities by LVK scientists when a promising candidate has been found. This award also will support the maintenance of collaboration tools to plan, track, publish and disseminate the LVK’s science results from the O4 observing run. Finally, it will provide some funds for supplies for the University of Maryland Physics Department’s STEM education/outreach programming for young students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.