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Current Progress and Future Work

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Detection of Christodoulou memory from EMRIs by LISA Signal processing Olga Petrova1, Dr. Daniel Kennefick2 1 Worcester Polytechnic Institute, Worcester, MA 2 ... – PowerPoint PPT presentation

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Title: Current Progress and Future Work


1
Detection of Christodoulou memory from EMRIs by
LISASignal processingOlga Petrova1, Dr. Daniel
Kennefick21 Worcester Polytechnic Institute,
Worcester, MA 2 University of Arkansas,
Fayetteville, AR
  • Introduction
  • The Christodoulou memory is generated by a
    gravitational wave (since gravitational waves
    carry energy, they also have mass. The flux of
    this mass generates its own gravitational wave)
  • LISA (laser interferometer space antenna) is a
    joint NASA-ESA mission that will detect
    gravitational waves in space.
  • While ground-based detectors such as LIGO are
    unlikely to detect Christodoulou memory, it has
    been proposed that LISA might be able to do so.


This is a graph of the data representing the
memory from two black holes spiraling into each
other. Positive time is the time before the
coalescence. The memory grows as the black holes
get closer, and stays constant after they
collide. Source D. Kennefick. Prospects for
detecting Christodoulou memory of gravitational
waves from a coalescing compact binary and using
it to measure neutron-star radii.
A graph of the filter function for LIGO a
ground-based laser interferometer. While this
function was derived for the LIGO noise spectrum,
we are expecting to get a similar filter for
LISA. This filter lets through only those parts
of the noisy signal which occur on the most
sensitive timescale right around the time that
the memory is changing most quickly. This
maximises the chance of distinguishing the signal
from the noise. Source D. Kennefick. Prospects
for detecting Christodoulou memory of
gravitational waves from a coalescing compact
binary and using it to measure neutron-star radii.
(due to residual effects which perturb the
spacecrafts inertial motion)
  • Current Progress and Future Work
  • We used a Fast Fourier Transform (FFT) algorithm
    to model h(f) (memory function in the frequency
    domain). Then we divided it by LISAs noise
    spectrum to get k(f) - the filter function in the
    frequency domain.
  • By the end of the program, we are planning to
    finish modeling k(f) and derive k(t) (filter
    function in the time domain) with the help of the
    inverse-FFT algorithm. This function will then be
    used for calculating the signal-to-noise ratio.

I would like to thank Dr. Daniel Kennefick for
his assistance and NASA for funding the Arkansas
Center for Space and Planetary Sciences REU
program.
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