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A Comparison of Burst Gravitational Wave Detection Algorithms for LIGO

A Comparison of Burst Gravitational Wave Detection Algorithms for LIGO. Amber L. Stuver Center for Gravitational Wave Physics Penn State University. Overview. Burst Data Analysis Algorithms Strongest False Alarm Events Do the algorithms see the data in the same way?

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A Comparison of Burst Gravitational Wave Detection Algorithms for LIGO

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  1. A Comparison of Burst Gravitational Wave Detection Algorithms for LIGO Amber L. Stuver Center for Gravitational Wave Physics Penn State University

  2. Overview • Burst Data Analysis Algorithms • Strongest False Alarm Events • Do the algorithms see the data in the same way? • Simulated Signal Performance • How do the algorithms differ with different signals? • Population Performance • What is the relative performance of each algorithm given a population? • Conclusions A. Stuver - CGWP, Penn State

  3. Data Analysis Algorithms (ETG) • BlockNormal • searches data for statistical “change points” and divides the data into blocks of data with consistent mean and variance. A block is reported if it differs by a significant amount from the statistics of the larger data set. • SLOPE • finds the best-fit straight line through intervals of the timeseries and if the slope is sufficiently improbable, the interval is reported • Q Pipeline • a multi-resolution time-frequency search for excess power. The data are projected onto bases that are logarithmically spaced in frequency and Q, and linearly in time and the most significant set of non-overlapping tiles are reported A. Stuver - CGWP, Penn State

  4. Strongest False Alarm Events • If each ETG “looks” at the data in a fundamentally equivalent way, then they should identify the same strongest false events. • Strongest defined as the largest magnitude of whatever quantity each ETG identifies • Data set is a subset of LIGO science data (S3) without any signal injections and assumed to be noise only A. Stuver - CGWP, Penn State

  5. View of All Strongest False Events • Locations of the 10 strongest BlockNormal, SLOPE and Q Pipeline events from a contiguous stretch of 600 seconds of data. • The most significant events identified by the three ETG’s are different. • Upon inspection, the events themselves do not appear, by eye, to have obvious differences. A. Stuver - CGWP, Penn State

  6. Events All ETGs “Saw” • Of the events that all 3 ETGs identify, do they rank the strength in the same way? • If the ETGs are equivalent on this level, a scatter plot of the rank of an event in one ETG to the rank in other will cluster along the diagonal… • They don’t. A. Stuver - CGWP, Penn State

  7. SLOPE & BlockNormal SLOPE BlockNormal A. Stuver - CGWP, Penn State

  8. Q Pipeline & SLOPE Q Pipeline SLOPE A. Stuver - CGWP, Penn State

  9. Q Pipeline & BlockNormal Q Pipeline BlockNormal A. Stuver - CGWP, Penn State

  10. Simulated Signal Performance • ETGs are not fundamentally equivalent and signal properties that ETG was sensitive to was not initially obvious • What, then, are the signal properties that each ETG favor? • To determine specific signal sensitivities: • Simulate signals of different lengths and amplitudes and inject into a white noise background (zero mean and unit variance) A. Stuver - CGWP, Penn State

  11. Amplitude for 50% Detection White Noise & Sine- Gaussians BlockNormal Black Hole Ringdown A. Stuver - CGWP, Penn State

  12. Amplitude for 50% Detection 64 Hz SLOPE White Noise 16 Hz A. Stuver - CGWP, Penn State

  13. Population Performance • Convolve the detection efficiency surface with a population • The integral of this gives a measure of an ETG’s performance WRT a population A. Stuver - CGWP, Penn State

  14. Measured Population Performances • BlockNormal has fairly consistent performance over different signal types. • While SLOPE’s performance can be higher, it is not as reliable. A. Stuver - CGWP, Penn State * Population values are normalized to this

  15. Conclusions • BlockNormal, SLOPE and Q Pipeline do not detect the same strongest events. • Among the events that are coincident, the significance of the event, as identified by the ETG’s, is uncorrelated. • There are signal properties that distinguish the preferences of each ETG. • SLOPE has a strong frequency dependence while BlockNormal favors impulsive events. • The overall shape of the detection fraction surface is meaningful for describing an ETG’s performance. • BlockNormal has a consistent performance over different signal types while SLOPE varies depending on the signal frequency. A. Stuver - CGWP, Penn State

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