The QPipeline is an analysis pipeline for the detection of GWBs in data from interferometric gravitational wave detectors [10]. It is based on the Qtransform [12], a multiresolution timefrequency transform that projects the data under test onto the space of Gaussian windowed complex exponentials characterized by a center time , center frequency , and quality factor .
The space of Gaussian enveloped complex exponentials is an overlapping basis of waveforms, whose duration and bandwidth , defined as the standard deviation of the squared Gaussian envelope in time and frequency, have the minimum possible timefrequency uncertainty, , where .
There is good reason to select an overlapping basis of multiresolution minimumuncertainty functions. Absent detailed knowledge of the gravitational waveform, such a basis provides the tightest possible constraints on the timefrequency area of unmodeled signals, permitting the timefrequency distribution of signal energy to be noncoherently reconstructed while incorporating as little noise energy as possible. A choice of basis that does not have minimum timefrequency uncertainty would typically include more noise than necessary, decreasing signal to noise ratio. The exception would be a restricted search for a known set of waveforms, in which a matched filter search, where the template matches the target signal, would be optimal. This type of restricted search is not the focus of this paper. Another benefit of the tighter timefrequency constraints afforded by a multiresolution sineGaussian template bank is the decreased likelihood for false coincidences, when testing for coincidence between multiple detectors.
In practice, the Q transform is evaluated only for a finite number of
basis functions, also referred to here as templates or tiles. These
templates are selected to cover a targeted region of signal space, and
are spaced such that the fractional signal energy loss
due to the mismatch , , and
between an arbitrary basis function and the nearest measurement
template,
The statistical significance of Q transform projections are given by their normalized energy , defined as the ratio of squared projection magnitude to the mean squared projection magnitude of other templates with the same central frequency and . For the case of ideal white noise, is exponentially distributed, and related to the matched filter SNR [13] by the relation
(3) 
The QPipeline consists of the following steps. The data is first whitened by zerophase linear predictive filtering [14,10]. Next, the Qtransform is applied to the whitened data, and normalized energies are computed for each measurement template. Templates with statistically significant signal content are then identified by applying a threshold on the normalized energy. Finally, since a single event may potentially produce multiple overlapping triggers due to the overlap between measurement templates, only the most significant of overlapping templates are reported as triggers. As a result, the QPipeline is effectively a templated matched filter search [13] for signals that are Gaussian enveloped sinusoids in the whitened signal space.

Figure 1 shows an example of the QPipeline applied to the inspiral phase of a simulated binary neutron star coalescence signal injected into typical single detector LIGO data with an SNR of 48.2.