A Comparison of Burst Gravitational Wave

Detection Algorithms for LIGO

Amber L. Stuver

Center for Gravitational Wave Physics

Penn State University

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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

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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

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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

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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.

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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.

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SLOPE & BlockNormalSL

OP

E

BlockNormal

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Q Pipeline & SLOPEQ

Pi

pelin

e

SLOPE

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Q Pipeline & BlockNormal

Q

Pipe

line

BlockNormal

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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)

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Amplitude for 50% Detection

Blo

ckN

orm

al

Black HoleRingdown

White Noise&

Sine-Gaussians

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Amplitude for 50% Detection

SLOP

E64 Hz

16 Hz

WhiteNoise

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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

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disk for ,ddAA),A(P

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Measured Population Performances

• BlockNormal has fairly consistent performance over different signal types.

• While SLOPE’s performance can be higher, it is not as reliable.

* Population values are normalized to this

Disk (~ A-3) Isotropic (~ A-4)BlockNormal

SLOPE BlockNormal

SLOPE

White Noise 0.81 1.0* 0.41 1.0*SG 16 Hz 1.08 2.72 0.81 8.34

64 Hz 1.07 0.85 0.92 1.89

BH16 Hz 1.23 2.53 1.11 7.4964 Hz 0.97 0.44 0.49 1.59

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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.