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Transcript of Improving Parameter Estimation Efficiency for Advanced Detector Data Analysis of Compact Binary...
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IntroductionParallelization
Variable ResolutionSummary
Improving Parameter Estimation Efficiencyfor Advanced Detector Data Analysis
of Compact Binary Coalescence
J. M. Bell1 2 J. Veitch2 3
1Millsaps College
2Gravitational PhysicsNIKHEF
3Department of PhysicsUniversity of Birmingham Physics
University of Florida IREU in Gravitational Physics
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 1 / 20
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IntroductionParallelization
Variable ResolutionSummary
Gravitational WavesParameter EstimationNested SamplingMotivation
Gravitational Waves
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 2 / 20
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IntroductionParallelization
Variable ResolutionSummary
Gravitational WavesParameter EstimationNested SamplingMotivation
Gravitational WavesFrom Compact Binary Coalescence
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 3 / 20
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IntroductionParallelization
Variable ResolutionSummary
Gravitational WavesParameter EstimationNested SamplingMotivation
Parameter EstimationDoing the Physics!
I 2 MassesI TimeI Sky positionI DistanceI 2 Orientation AnglesI 6 Spin Components
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 4 / 20
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IntroductionParallelization
Variable ResolutionSummary
Gravitational WavesParameter EstimationNested SamplingMotivation
Parameter EstimationA Bayesian Algorithm
Nested Sampling
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 5 / 20
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IntroductionParallelization
Variable ResolutionSummary
Gravitational WavesParameter EstimationNested SamplingMotivation
Motivation
I The Problem:Data analysis via Nested Sampling takes time
I The Solution:Improving the efficiency of Nested Sampling
I ParallelizationI Multiple Bandwidth Analysis
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 6 / 20
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Parallelization
I Nested sampling converges on the maximum likelihoodI faster with the use of fewer live pointsI more accurately with the use of more live points
I Goals:I to reduce overall computational time while maintaining
sufficient accuracyI to optimize this procedure by finding the most effective
range of live points.
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
ParallelizationMethod
1 Run multiple instances in parallel with different NliveI 1 @ 1024I 2 @ 512I 4 @ 256
...I 64 @ 16
2 Recombine the results weighted by their parameterestimates
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Parallelization ResultsChirp Mass Cumulative Distributions
Factors of 1024 Factors of 256
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Parallelization ResultsAccuracy and Efficiency
Posterior Samples
Nlive
Computational Time (s)
Nlive
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
ParallelizationConclusions
I Parallelization can reduce computational time arbitrarilyI Reducing Nlive by 50% returns 75% of the posterior
samples
I The optimal range for Nlive is 200 to 256I The total Nlive across instances should be over 1000
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Switching Gears
Time Domain to Frequency DomainF vs. T function
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Multi-bandwidth Analysis
I Resolution is related to the number of samples in aninterval
I high resolution is redundantI low resolution is efficient
I Plan:I to downsample the frequency domain waveform according
to an optimized function based on the Nyquist timeI Goals:
I to exploit the monochromatic, low frequency nature of theearly waveform
I to focus computational resources on the more complexregion near the merger
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Variable Resolution MethodDetermining the Sampling Rate
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 14 / 20
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Variable Resolution MethodA Broken Waveform
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Variable Resolution ResultsAccuracy
Bands % Match1 99.99972 99.95413 99.75894 99.53515 99.3865
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Variable Resolution ResultsEfficiency
Bands d+hh:mm:ss1 ≈ 4+09:00:002 3+16:28:543 2+23:22:124 2+18:19:395 2+17:30:19
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IntroductionParallelization
Variable ResolutionSummary
OverviewMethodResultsConclusions
Variable Resolution Conclusions
I Variable Resolution analyses are feasible for parameterestimation
I A computation requiring roughly 50% of the time retainsover 99% of the accuracy
I Other methods of interpolation could lead to greateraccuracy
I Further work needed to eliminate remaining issues
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IntroductionParallelization
Variable ResolutionSummary
Summary and Outlook
Summary and Outlook
I Parallelization and Variable Resolution are viable means ofreducing computational time
I What lies ahead?I Optimization of the multiband algorithmI Simultaneous testing of both approachesI Implementation in the time domain
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IntroductionParallelization
Variable ResolutionSummary
Summary and Outlook
J.M. Bell, J. Veitch Improving CBC Parameter Estimation Efficiency 20 / 20