Seth Timpano Louis Rubbo Neil Cornish Characterizing the Gravitational Wave Background using LISA.

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Presentation transcript:

Seth Timpano Louis Rubbo Neil Cornish Characterizing the Gravitational Wave Background using LISA

Outline Motivation Galactic Sources of Gravitational Waves Modeling a Source LISA and Detector Simulations The Full Modulated Signal Bright Sources Confusion Background Tests of Normality

Galactic Sources of Gravitational Radiation Binaries have time varying quadrupole moments Large number of binaries Galactic Sources –Unevolved Binaries: 7  –Catacylsmics: 1.8  10 6 –WUMa: 3  10 7 –Neutron Star Binaries: 1  10 6 –Neutron Star/Black Hole: 5  10 5 –Close White Dwarfs: 3   10 7 ??? D. Hils, P. Bender, and R.F. Webbink, Astrophys. J. 360, 75, 1990

Modeling an Individual Source General Gravitational Wave Polarization Coefficients Amplitudes

Galactic Model Galactic Disk Sun-Centered Ecliptic Coordinates

Barycenter Combine all source types to arrive at a total barycenter background.

Source Number Density Number of sources per Frequency bin versus frequency.

LISA NASA/ESA mission 2014 Orbital Configuration –1 AU –60 degree inclination –3 spacecraft –5e6 km arm-length Sensitive to both + & x Frequency Response –10 -5 to 10 0 Hz Sources –Galactic Binaries –SMBH Mergers –EMRIs

Signal Modulation –Frequency Modulation Doppler Effect –Amplitude Modulation Time Varying Antenna Patterns –Phase Modulation +,x sensitivity

Extended Low Frequency Approximation Arbitrary Observation Time: Frequency Evolution: Arm Response Functions: Total Response:

Correlations Low Frequency ApproximationRigid Adiabatic Approximation Extended Low Frequency Approximation

The Accelerated LISA Simulator Low Frequency Approximation Extended Low Frequency Approximation Rigid Adiabatic Approximation f < 3mHzf < 7mHzf < 100mHz

The Simulated Background

The Barycenter Background

The Simulated Background

Noise Co-added to Signal

Outlier Removal Exact Removal Removal Procedure –Determine initial Confusion Background –Remove all sources with SNR > 5 –Update Confusion Background –Remove all sources with SNR > 5 –Repeat 4 more times

Confusion Background Definition of the Confusion Background Estimate of the Confusion Background

Outlier Properties Source Number and Type Distance versus Frequency Source Density

Gaussian?...No Are the Fourier coefficients of the power spectrum normally distributed? Fails to be Gaussian due to outliers in the tails of the distribution. Central Limit Theorem?

Gaussian?...Yes What happens when we remove all the bright sources? The Confusion background is Gaussian.

Galactic Model of Gravitational Radiation Detector Simulation Identification of Outliers and Source Removal Distinguish Background from Noise Summary