Support of LIGO Data Analysis Activities at the University of Texas at Brownsville

The University Of Texas Rio Grande Valley, Edinburg TX

Investigators

Abstract

The General Theory of Relativity discovered by Einstein tells us that the familiar, everyday force of gravity is a manifestation of something much stranger: the bending of the geometry of space-time by matter. Among the key predictions of the theory, which includes the expanding Universe and the existence of black holes, is the existence of gravitational waves (GW): ripples moving at the speed of light in the geometry of space-time caused by the fast motion of large masses. Although well tested in terms of their indirect effects on binary systems of compact stars, the direct detection of gravitational waves incident on Earth poses an outstanding challenge. The scientific rewards from achieving this ability would be enormous - ranging from probing the extreme dynamics of exploding stars to gleaning information about the state of the Universe almost at the moment of the Big Bang itself. The effort to enable this new window on the universe has occupied several decades of experimental and technological developments that have pushed the boundaries across diverse fields in the physical sciences. The year 2015 will mark a highly-anticipated watershed moment for gravitational-wave physics: The two advanced Laser Interferomenter Gravitational Wave Observatory (aLIGO) detectors will start their initial data taking runs, followed by the commissioning of the advanced Virgo gravitational wave observatory in Europe. The sensitivity of the aLIGO detector will be ramped up to become about ten times better than that of the first-generation detectors, opening up a spatial volume for observing GW sources that will be 1000 times larger than before. Along with these tremendous advances in instrumentation, it has been known from the very inception of the gravitational wave physics effort that the envelope of statistical data analysis techniques must also be pushed further to enable the detection of weak and rare signals embedded in noise. It is in this area that the research funded by this grant will make significant contributions. By supporting graduate students, this grant will help to grow the community of researchers in this field. Since the University of Texas at Brownsville (UTB) is an Hispanic serving institution, the research activities will expose students who are traditionally under-represented in STEM areas to forefront science. Ongoing major education and outreach activities at UTB will leverage these activities to create awareness among high-school students about exciting projects such as LIGO. This grant supports research projects in the following major data analysis areas. (i) An algorithm will be developed and implemented that allows the construction of sky maps of an anisotropic background of stochastic gravitational waves with minimal prior assumptions. Using methods originally developed in the context of pulsar-timing-based gravitational-wave searches, the new approach will vastly extend the capabilities of existing model-dependent searches. A Bayesian alternative to the standard frequentist stochastic search will also be developed that will leverage existing code for Bayesian inference in the context of detector characterization and noise estimation. (ii) A smoothness-regularization-based coherent network analysis method will be implemented that significantly improves the detection and characterization of burst gravitational wave signals having non-compact time-frequency (TF) signatures. Such signals are expected to arise in a variety of astrophysical scenarios but are known to pose a significant challenge to existing methods. (iii) A new method, derived from the Harmonic Regeneration Noise Reduction (HRNR) technique developed in acoustical signal processing, will be integrated with existing network analysis pipelines to boost their sensitivity to post-core-bounce-phase supernova signals. By improving both signal waveform and sky location estimation, this will lead to better multi-messenger follow up of such a rare but dramatic event. (iv) The characterization of detector noise and non-gravitational wave signals ("glitches") in the data is necessary for improving detection confidence for genuine gravitational-wave signals. In this context, the Self Organizing Map (SOM) method will be used for instrumental glitch classification, and glitch characteristics relevant to aLIGO will be catalogued. An independent approach will use the BayesWave Bayesian inference pipeline to identify and study glitch signals.