Template banks to search for compact binaries with spinning components in gravitational wave data

Abstract

Gravitational waves from coalescing compact binaries are one of the most promising sources for detectors such as LIGO, Virgo and GEO600. If the components of the binary posess significant angular momentum (spin), as is likely to be the case if one component is a black hole, spin-induced precession of a binary's orbital plane causes modulation of the gravitational-wave amplitude and phase. If the templates used in a matched-filter search do not accurately model these effects then the sensitivity, and hence the detection rate, will be reduced. We investigate the ability of several search pipelines to detect gravitational waves from compact binaries with spin. We use the postNewtonian approximation to model the inspiral phase of the signal and construct two new template banks using the phenomenological waveforms of Buonanno, Chen and Vallisneri [A. Buonanno, Y. Chen and M. Vallisneri, Phys. Rev. D67, 104025 (2003)]. We compare the performance of these template banks to that of banks constructed using the stationary phase approximation to the nonspinning post-Newtonian inspiral waveform currently used by LIGO and Virgo in the search for compact binary coalescence. We find that, at the same false alarm rate, a search pipeline using phenomenological templates is no more effective than a pipeline which uses non-spinning templates. We recommend the continued use of the non-spinning stationary phase template bank until the false alarm rate associated with templates which include spin effects can be substantially reduced.

Random template banks and relaxed lattice coverings

“…Other studies have recently started to investigate a somewhat different random template placement strategy, referred to as "stochastic" template banks [20,21,22,23]. The key difference of these methods is that they involve a "pruning stage" in the random template placement, which is aimed to remove templates that are too close to each other.…”

“…This is a natural step for searches that employ statistical detection techniques which always involve a finite false-dismissal probability. Connected to this idea are new types of template banks, commonly referred to as "stochastic", which have recently been studied and applied by various groups [20,21,22,23]. Stochastic template banks are constructed by randomly placing templates on the parameter space, accompanied by a "pruning" step in which "superfluous" templates, which are deemed to lie too close to each other, are removed.…”

Searching for gravitational waves from compact binaries with precessing spins

“…[87] was used to search for precessing signals in LIGO data [44,49]. However, it was determined that due to the increased response to the noise background, this method did not offer any improvement in sensitivity compared to a nonspinning search pipeline [51]. The basic problem was that while the increased parameter space offered an improvement in the signal power recovered for precessing signals, the large, additional, unphysical parameter space being searched greatly increased the rate of background triggers of the search.…”

“…The only search that did include spin effects in its templates [49] was a search using precessing-spin templates [50]. This search was later shown to perform, on average, no better than nonspinning searches [51], as the increased degrees of freedom in the signal space picked up extra noise, which offset the gains achieved in signal-power recovery. Several recent studies performed with aligned-spin templates have demonstrated that it is possible to pull in more signal than noise [52][53][54][55], and aligned-spin templates are currently used in searches with Advanced LIGO [56].…”

Mismodeling in gravitational-wave astronomy: The trouble with templates

“…This can greatly aid in separating signals from noise in the data, s(t), by demanding that the residuals, r(t) = s(t) − h(t), are consistent with our instrument noise model. The different classes of analyses can be classified by the strength of the priors [1], ranging from the weak signal priors used in burst searches [2][3][4][5][6][7][8] and stochastic searches [9][10][11][12][13][14][15], to the highly restrictive priors used in searches for binary mergers [16][17][18][19][20][21][22][23].…”