ABSTRACT

The field of observational gravitational-wave astronomy began with a "bang" on 14 September 2015 with the detection of gravitational waves from the merger of two inspiraling and colliding black holes. This detection opened a window into the "dark side" of the universe, providing the means to observe astrophysical objects and events that would otherwise be impossible to see with standard optical telescopes. To date, approximately 100 "loud" events, from relatively nearby black holes and/or neutron stars, have been detected by several large-scale gravitational-wave detectors including NSF's LIGO. But the combined signal from the population of more distant pairs of black holes has yet to be detected. This project is designed precisely to target this signal, which (using the analogy of hearing) would sound like popcorn popping. In other words, the signal consists of weak bursts of gravitational waves of short duration (~seconds) separated by periods (~a few minutes) of relative silence. The data analysis tools developed as part of this project will explicitly take into account the popcorn-like nature of the signal, leading to a more sensitive search and a possible first detection of this type of signal within the next few years. The project will provide support and training in data analysis to one or two graduate students, thus adding to the growing community of researchers in this emerging field. The computational and data analysis skills that the students will acquire are transferable outside the field of gravitational-wave astronomy, making the students marketable in a variety of disciplines--potentially as future university professors or outside the university setting in research labs or high-tech companies.

The proposed project consists of three main activities, which increase in scope and complexity over the period of the proposal: (i) First, to provide a "proof-of-principle" demonstration of a stochastic-signal-based search for popcorn-like (intermittent) gravitational-wave signals in the context of a set of relatively simple toy models. (ii) Second, to extend the data analysis pipeline developed in part (i) to run on more realistic data sets, thus stress-testing the proposed search. (iii) Third, to run a production version of the pipeline developed in part (ii) on the Advanced LIGO-Virgo-KAGRA data taken during the 4th observation run O4, which will start near the end of 2022 / beginning of 2023. The proposed stochastic-signal-based search has the potential to advance the field of gravitational-wave astronomy by being the first search to detect the signal from mergers of pairs of stellar-mass black holes throughout the Universe. This is possible because the search takes into account the intermittent nature of the signal, which should lead to a reduced time-to-detection by increasing the signal-to-noise ratio of the recovered signal amplitude compared to the current search, which assumes that the signal is "on" all the time. In addition, by using a stochastic-signal model, the search is both more robust to the type of source and less computationally demanding than a deterministic-signal-based search, which is tuned to the specific waveforms associated with binary black hole mergers.

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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