English

Testing the no-hair theorem with black hole ringdowns using TIGER

General Relativity and Quantum Cosmology 2015-06-19 v2

Abstract

The Einstein Telescope (ET), a proposed third-generation gravitational wave observatory, would enable tests of the no-hair theorem by looking at the characteristic frequencies and damping times of black hole ringdown signals. In previous work it was shown that with a single 5001000M500 - 1000\,M_\odot black hole at distance 6\lesssim 6 Gpc (or redshift z1z \lesssim 1), deviations of a few percent in the frequencies and damping times of dominant and sub-dominant modes would be within the range of detectability. Given that such sources may be relatively rare, it is of interest to see how well the no-hair theorem can be tested with events at much larger distances and with smaller signal-to-noise ratios, thus accessing a far bigger volume of space and a larger number of sources. We employ a model selection scheme called TIGER (Test Infrastructure for GEneral Relativity), which was originally developed to test general relativity with weak binary coalescence signals that will be seen in second-generation detectors such as Advanced LIGO and Advanced Virgo. TIGER is well-suited for the regime of low signal-to-noise ratio, and information from a population of sources can be combined so as to arrive at a stronger test. By performing a range of simulations using the expected noise power spectral density of Einstein Telescope, we show that with TIGER, similar deviations from the no-hair theorem as considered in previous work will be detectable with great confidence using O(10)\mathcal{O}(10) sources distributed uniformly in co-moving volume out to 50 Gpc (z5z \lesssim 5).

Cite

@article{arxiv.1406.3201,
  title  = {Testing the no-hair theorem with black hole ringdowns using TIGER},
  author = {J. Meidam and M. Agathos and C. Van Den Broeck and J. Veitch and B. S. Sathyaprakash},
  journal= {arXiv preprint arXiv:1406.3201},
  year   = {2015}
}

Comments

11 pages, 20 figures. Matches version in PRD

R2 v1 2026-06-22T04:36:58.647Z