Search for gravitational waves (GW) is one of the most interesting problems in modern astronomy. The large-scale GW interferometric detectors (like LIGO, VIRGO, GEO, TAMA) are already in 'Science Mode' operation and are taking data at the rate of several terabytes per week. The output of the detector consists of both signal and noise. It is of utmost importance to understand the characteristics of the instrument noise not only for diagnostics, but also because the nature of the noise is intimately tied to the efficiencies of the astrophysics searches. Detector Characterization research involves study of the detector noise for statistical characterization, modeling, diagnostics and construction of data quality flags and vetoes that feed up to the astrophysical search pipelines to make the searches more efficient. It is also research that directly provides insight to the experimentalists at the laboratory sites about the state of the instrument and its operation. Databases resulting out of detectors like LIGO, Virgo, GEO are very large and complicated because of presence of innumerable known and unknown sources of noise. This calls for advanced efficient techniques of statistical analysis that are capable of handling this data size and the data rate for meaningful scientific inference regarding the state of the detector as well as data quality relevant to astrophysical searches. Data Mining is thus central to these projects. The current and planned work of the group includes: (i) Data Mining and Classification for GW Burst triggers, (ii) Noise modeling and veto studies and (iii) Analysis of non-stationarity in gravitational wave detector output. The main theme of these projects is to identify features of the real data that affect an astrophysical search strategy, monitoring them and understanding their source and effect on the scientific interpretation of the search results. We have been following a line of investigation that involves developing robust and non-parametric statistical data analysis algorithms whose output is then analyzed for pattern using tools developed by the data mining technologists and also using innovative algorithms developed by our group. The work is highly computation-intensive and inter-disciplinary in nature that combines collective knowledge of GW experiment, signal processing and statistics and modern developments in computational sciences including grid computing. The group is a member of the LIGO Scientific Collaboration (LSC).
Some relevant web sites are:
Students working in research:
Graduate students: Robert Stone (Ph.D) | Papia Rizwan (MS Interdisciplinary Sciences (MSIS) - Physics-Computer Science)
Students who have graduated: R. Stone (MS Physics, now doctoral student at UT San Antonio (UTSA)); T. S. Weerathunga (MS Physics, now doctoral student at UTSA); T. Zhang (MSIS; CS thesis director Dr. L. R. Tang; now doctoral student at U Alabama); C. Dannangoda (MS Physics, now doctoral student at UTSA); R. Obaid (Undergraduate research, now Research Assistant at U. Chicago).