Gravitational-Wave Data Science with a LIGO at Caltech

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This award supports research in gravitational wave detector commissioning and characterization, as well as data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational-wave (GW) astronomy has entered a new era, when detected events (so far, from the merger of compact binary systems: binary black holes and binary neutron stars) will become frequent. The Advanced LIGO detectors are continuously improving their sensitivity to GWs; this will result in unprecedented rates of discoveries of compact object mergers, and the potential for many discoveries of GWs from other astrophysical sources. Whole new classes of sources may be discovered. These observations will enable a wealth of studies and new scientific results in astronomy, cosmology, and fundamental physics. Observations of binary black hole mergers are used to understand stellar evolution and the formation, evolution and death of binaries. They enable uniquely powerful tests of general relativity as a theory of gravity in the strong-field, highly-relativistic regime where detectable GWs are produced. Observations of binary neutron star mergers enable constraints on the nuclear equation of state, unique measurements of the local Hubble-Lemaitre parameter, studies of gamma-ray burst physics, and insights into the origins of the heavy elements in the universe (including gold, platinum and uranium). All of these studies are made possible through the construction and operation of the incredibly sensitive LIGO and Virgo detectors. The Caltech group, working with the LIGO Laboratory and LIGO Scientific Collaboration (LSC), continuously improves the detectors' sensitivity, precisely calibrates the strain data, characterizes detector behavior to evaluate and improve data quality, and releases the data to the public. These studies also depend crucially on the ability to identify weak GW signals from astrophysical sources in the noisy data, using highly optimized search pipelines. We also use the scientific computing required to analyze LIGO data as a tool to engage high school juniors and seniors in the local area in gravitational-wave physics and astronomy, introducing programming and more broadly applying the scientific method through hands-on GW data science projects.Our work is devoted to discovering and analyzing gravitational-wave (GW) signals in the LIGO, Virgo and KAGRA detectors during LIGO's observing runs O3 and O4. The proposed work focuses on: (a) Identifying GW signals from compact binary mergers in data from LIGO, Virgo, and KAGRA, using both of the LSC ``flagship'' search pipelines, PyCBC and gstlal; (b) using the Hidden Markov Model Viterbi algorithm to search for long-duration burst GWs from compact binary mergers, continuous GWs (CGWs) from newly-formed, rapidly spinning neutron stars in our galaxy, and other sources that produce narrow-band signals with potentially wandering central frequency; and (c) validation of candidate events (including CGW candidates) found by these pipelines, with special attention to data quality, calibration, and search pipeline specifics. We will build on our existing expertise and experience in all of these efforts, and extend them to the era of frequent detections by automating routine tasks. We will exploit our expertise in low-latency data handling, strain calibration, detector characterization and data quality evaluation, and search pipelines. The results of these efforts will make it possible to make confident detections of GW events as they become frequent, and make the information obtained available to the LSC, to the astronomical community, and to the public, promptly, so that they can be fully exploited by those communities to advance gravitational-wave astronomy.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.