We assess the detection prospects of a gravitational wave background associated with sub-luminous gamma-ray bursts (SL-GRBs). We assume that the central engines of a significant proportion of these bursts are provided by newly born magnetars and cons
ider two plausible GW emission mechanisms. Firstly, the deformation-induced triaxial GW emission from a newly born magnetar. Secondly, the onset of a secular bar-mode instability, associated with the long lived plateau observed in the X-ray afterglows of many gamma-ray bursts (Corsi & Meszaros 2009a). With regards to detectability, we find that the onset of a secular instability is the most optimistic scenario: under the hypothesis that SL-GRBs associated with secularly unstable magnetars occur at a rate of (48; 80)Gpc^{-3}yr^{-1} or greater, cross-correlation of data from two Einstein Telescopes (ETs) could detect the GW background associated to this signal with a signal-to-noise ratio of 3 or greater after 1 year of observation. Assuming neutron star spindown results purely from triaxial GW emissions, we find that rates of around (130;350)Gpc^{-3}yr^{-1} will be required by ET to detect the resulting GW background. We show that a background signal from secular instabilities could potentially mask a primordial GW background signal in the frequency range where ET is most sen- sitive. Finally, we show how accounting for cosmic metallicity evolution can increase the predicted signal-to-noise ratio for background signals associated with SL-GRBs.
The brightest events in a time series of cosmological transients obey an observation time dependence which is often overlooked. This dependence can be exploited to probe the global properties of electromagnetic and gravitational wave transients (Howe
ll et al. 2007a, Coward & Burman 2005). We describe a new relation based on a peak flux--observation time distribution and show that it is invariant to the luminosity distribution of the sources (Howell et al. 2007b). Applying this relation, in combination with a new data analysis filter, to emph{Swift} gamma-ray burst data, we demonstrate that it can constrain their rate density.
It has been shown that the observed temporal distribution of transient events in the cosmos can be used to constrain their rate density. Here we show that the peak flux--observation time relation takes the form of a power law that is invariant to the
luminosity distribution of the sources, and that the method can be greatly improved by invoking time reversal invariance and the temporal cosmological principle. We demonstrate how the method can be used to constrain distributions of transient events, by applying it to Swift gamma-ray burst data and show that the peak flux--observation time relation is in good agreement with recent estimates of source parameters. We additionally show that the intrinsic time dependence allows the method to be used as a predictive tool. Within the next year of Swift observation, we find a 50% chance of obtaining a peak flux greater than that of GRB 060017 -- the highest Swift peak flux to date -- and the same probability of detecting a burst with peak flux > 100 photons s^{-1} cm^{-2} within 6 years.
The temporal evolution of the gravitational wave background signal resulting from stellar-mass binary black hole (BBH) inspirals has a unique statistical signature. We describe the application of a new filter, based on the `probability event horizon
(PEH) concept, that utilizes both the temporal and spatial source distribution to constrain the local rate density, $r_{0}$, of BBH inspiral events in the nearby Universe. Assuming Advanced LIGO sensitivities and an upper rate of Galactic BBH inspirals of $ 30hspace{1mm}mathrm{Myr}^{-1}$, we simulate GW data and apply a fitting procedure to the PEH filtered data. To determine the accuracy of the PEH filter in constraining $r_{0}$, a comparison is made with a fit to the brightness distribution of events. We apply both methods to a data stream containing a background of Gaussian distributed false alarms. We find that the brightness distribution yields lower standard errors, but is biased by the false alarms. In comparison the PEH method is less prone to errors resulting from false alarms but has a lower resolution as fewer events contribute to the data. Used in combination, the PEH and brightness distribution methods provide an improved estimate of the rate density.
