Gravitational wave background from a cosmological population of core-collapse supernovae

Citation
V. Ferrari et al., Gravitational wave background from a cosmological population of core-collapse supernovae, M NOT R AST, 303(2), 1999, pp. 247-257
Citations number
48
Categorie Soggetti
Space Sciences
Journal title
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN journal
00358711 → ACNP
Volume
303
Issue
2
Year of publication
1999
Pages
247 - 257
Database
ISI
SICI code
0035-8711(19990221)303:2<247:GWBFAC>2.0.ZU;2-G
Abstract
We analyse the stochastic background of gravitational radiation emitted by a cosmological population of core-collapse supernovae. The supernova rate a s a function of redshift is deduced from an observation-based determination of the star formation rate density evolution. We then restrict our analysi s to the range of progenitor masses leading to black hole collapse. In this case, the main features of the gravitational wave emission spectra have be en shown to be, to some extent, independent of the initial conditions and o f the equation of state of the collapsing star, and to depend only on the b lack hole mass and angular momentum. We calculate the overall signal produc ed by the ensemble of black hole collapses throughout the Universe, assumin g a flat cosmology with a vanishing cosmological constant. Within a wide ra nge of parameter values, we find that the spectral strain amplitude has a m aximum at a few hundred Hz with an amplitude between 10(-28) and 10(-27) Hz (-1/2); the corresponding closure density, Omega(GW), has a maximum amplitu de ranging between 10(-11) and 10(-10) in the frequency interval similar to 1.5 - 2.5 kHz. Contrary to previous claims, our observation-based determin ation leads to a duty cycle of order 0.01, making our stochastic background a non-continuous one. Although the amplitude of our background is comparab le to the sensitivity that can be reached by a pair of advanced LIGO detect ors, the characteristic shot-noise structure of the predicted signal might, in principle, be exploited to design specific detection strategies.