Support for LIGO Data Analysis and Instrumentation Research at the University of Texas Rio Grande Valley

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This award supports research in LIGO instrumentation and data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational waves (GW) have been directly detected in 2015. So far, two different types of sources have been detected, viz. binary black holes and binary neutron stars. However, there is a wide repertoire of potential sources yet to be detected. The third generation of GW detectors are also in the offing. To address these new challenges in the next decade, this award supports experimental innovations and novel detector characterization and data analysis techniques to further enhance probability of detection of new sources and further extend the GW visibility field. Core collapse supernovae (CCSN) are one of such highly anticipated yet equally challenging sources. The science payoffs from such a detection will be huge, but it dares to elude us because of the low occurrence rates and weak signal strengths. The award will implement a new technique that, based on recent studies, is expected to enhance the detection sensitivity of CCSN. At the same time, further data quality studies will be conducted to study and mitigate noise generated by turbulent airflow. On the instrumental side, research will be conducted to calculate the length response from the advanced LIGO detectors to better understand the high frequency response. While this research will reflect on fundamental understanding of a wide variety of issues, it will also be a great opportunity to train the undergraduate and graduate students in GW research and strengthen STEM workforce. The algorithms and numerical models that will be developed during this study will have a broader application beyond the GW data analysis. With the upcoming O3 run of the LIGO detectors, it is anticipated the detection of other types of sources and even unknown ones. With the goal of significantly increasing the science reach of the advanced detectors, the UTRGV team will work on projects in the following major areas. 1. Noise characterization: the development of a numerical model to generate realistic finely-sampled temperature fields and run a full hydrodynamic simulation, to determine the frequency distribution of turbulent vortices, and to see how turbulent airflow acts back on the temperature field. 2. Instrumentation research: studies of the aLIGO interferometer configuration in the interferometer model, and evaluating the residual uncertainties at high frequencies. 3. Efficient methods for GW emission from core collapse supernovae: development and application of innovative data analysis algorithms geared towards enhancement of efficiency in detecting weak unmodeled GW signals from core collapse supernovae burst sources. A data pre-processing method (called "TSD"), derived from the Harmonic Regeneration Noise Reduction (HRNR) technique, will be integrated with existing network analysis pipelines to boost their sensitivity to post-core-bounce-phase supernova signals, followed by characterization of performance enhancement and waveform reconstruction for such signals injected in observation-run data.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.