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15 Dec 2005A. Stuver - CGWP, Penn State3 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
Citation preview
A Comparison of Burst Gravitational Wave
Detection Algorithms for LIGO
Amber L. Stuver
Center for Gravitational Wave Physics
Penn State University
15 Dec 2005 A. Stuver - CGWP, Penn State 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
15 Dec 2005 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
15 Dec 2005 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
15 Dec 2005 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.
15 Dec 2005 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.
15 Dec 2005 A. Stuver - CGWP, Penn State 7
SLOPE & BlockNormalSL
OP
E
BlockNormal
15 Dec 2005 A. Stuver - CGWP, Penn State 8
Q Pipeline & SLOPEQ
Pi
pelin
e
SLOPE
15 Dec 2005 A. Stuver - CGWP, Penn State 9
Q Pipeline & BlockNormal
Q
Pipe
line
BlockNormal
15 Dec 2005 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)
15 Dec 2005 A. Stuver - CGWP, Penn State 11
Amplitude for 50% Detection
Blo
ckN
orm
al
Black HoleRingdown
White Noise&
Sine-Gaussians
15 Dec 2005 A. Stuver - CGWP, Penn State 12
Amplitude for 50% Detection
SLOP
E64 Hz
16 Hz
WhiteNoise
15 Dec 2005 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
isotropicfor ,ddAA),A(
disk for ,ddAA),A(P
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rss3
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15 Dec 2005 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.
* 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
15 Dec 2005 A. Stuver - CGWP, Penn State 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.