Modeled Data for LIGO I

• Two Neutron Stars: you can listen to the sounds (duration: 8 seconds) for two coalescing neutrons stars (1.4 solar masses each) with no noise (131kB file) or with the binary located at 600 kpc - e.g., M31 in Andromeda (131 kB file) embedded in LIGO-I simulated noise. While you can barely discern the signal for a 0.6 Mpc source, an optimized signal processing technique for LIGO data is able to detect a source up to 15 Mpc distance (the signal amplitude goes as the inverse of the distance). The image gives you an idea of the time frequency evolution of the signal. It shows the how the signal spectrum changes (orange, blue, purple, green, brown) during the final 5 seconds of the chirp. The black curve corresponds to the case with no gravity wave signal. The spectrum has abscissa given in frequency (Hz) and ordinate in displacement amplitude spectral density (m/Sqrt[Hz]).

 

• Two 10 solar mass Black Holes: with no noise (131 kB file), and with LIGO-I simulated noise but located at 600 kpc - e.g., M31 in Andromeda (131 kB file) or located at 15 Mpc - e.g., in the Virgo Cluster of galaxies(131 kB file). Again, the two images present the signal evolution for the last 5 seconds of the chirp. Because these are massive objects, the chirp evolves more rapidly and lasts less time in the LIGO band than is the case with neutron stars. 
  For the 15 Mpc example, you can still see the signal in the spectrum although it is not possible for the ear to detect it in the sound track. The spectra have abscissa given in frequency (Hz) and ordinate in displacement amplitude spectral density (m/Sqrt[Hz]).
  
  

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        Engineering Data from Hanford, WA

• Real data from the April 2000 engineering run (161 kB file). Please note to the this engineering run was a single arm test and that the sensitivity was much lower than a full interferometer can give.

About the Images

• Power or Amplitude Spectra: Signal processing technology relies on a representation of time dependent signals in the frequency domain. A process that results in a varying or time dependent signal can be characterized by its frequency spectrum. For example a pure harmonic tone such as a sine wave signal corresponds to a frequency spectrum with a single peak at the frequency characterizing that tone. Most physical processes generate signals at many different frequencies simultaneously. These signals, when represented in the frequency domain, have complex shapes that often can serve as "fingerprints" for the process. The spectra shown above have intrinsic features such as narrow lines (e.g., at ~340 Hz, ~680 Hz, ~1020 Hz in the simulated spectra shown above) that come from instrumental and mechanical artifacts. These are not astrophysical in nature and must be accounted for in the signal processing techniques that are designed to detect weak signals of interest embedded in our detector's noise. On the other hand the components of the simulated astrophyiscal signal are easily discernible above the background when the signal strength is sufficiently high.
  Because a large dynamic range of both signal amplitude and frequency is required to describe the signals, the spectra are often plotted in "log-log" plots to compress the information. Physicists study gravitational waveforms in this manner. In the spectra above, the abscissa (x axis) corresponds to frequency in Hz while the ordinate (y axis) corresponds to the strength of the signal. The strength of a signal in the frequency domain is given by its amplitude (h[f]) or power (h^2[f]) spectral density.

Other links

LIGO Home page  NCSA Simulations of GR Processes
 
 
 

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