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NOBEL PRIZE 2026

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NSFDirectorate for Mathematical and Physical Sciences

Extending the Teach of Gravitational-Wave Detectors through Low Latency Data Products, Unmodeled Searches, and Detector Characterization

Completed
Institution
University of Minnesota-Twin Cities, MN
Principal investigator
Michael W Coughlin
Funding
$300K
Duration
2023–2026
Open portfolio

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Abstract

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Multi-messenger gravitational-wave astronomy has arrived with the detection of the coalescence of compact binary systems. With Advanced LIGO, Advanced Virgo and KAGRA pushing towards design sensitivity, continued and new efforts to characterize the detectors and subtract known physical effects from the data is required. This work enables the possibility of detecting more compact binaries, as well as discoveries of gravitational waves from other astrophysical sources that may/may not be associated with electromagnetic signals. With the goal of significantly increasing the science reach of the advanced detectors, this award will enable the detection of yet-to-be-detected, unmodeled gravitational waves and the development of software for low-latency data products. It will also support an outreach program covering elementary through high school outreach activities with a focus on gravitational waves within Minneapolis and St. Paul. Along with the ongoing advances in instrumentation, the realization of gravitational-wave astronomy depends critically on continuing advances in data analysis capabilities, both for second and third generation instruments. This award focuses on three areas: (i) techniques and searches for unmodeled gravitational-wave transients on O4 and O5 data, (ii) software for low-latency data products, including estimates of source properties such as inclination and ejecta predictions and apply them to future detected events in O4 and O5, and (iii) techniques to mitigate non-linear noise sources using auxiliary sensor data to understand and remove contributions of linear and non-linear noise sources in gravitational-wave strain to significantly improve their sensitivity. The outreach activities funded by this award will help students understand what LIGO is and how gravity works. 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.

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Directorate for Mathematical and Physical SciencesArtificial Intelligence (AI)OFFICE OF MULTIDISCIPLINARY ACWindows on the Universe (WoU)GVF - Global Venture FundGERMANY (F.R.G.)Gravity Exp. & Data AnalysisWoU-Windows on the Universe: Tgravitational waveslow-latencyligonon-lineardata productsgravitational-wavedetectorsgravitational

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