Fast and accurate sensitivity estimation for continuous-gravitational-wave searches

Dreißigacker, C.; Prix, R.; Wette, K.

doi:10.1103/physrevd.98.084058

Cited by 69 publications

(107 citation statements)

References 86 publications

(219 reference statements)

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“…Following the approach of [34] we extend the upper limits calculation from the 13 trial bands to the full frequency band Hz. Indeed, as discussed in [34], the strain amplitude is proportional to S n (f ), which is the square root of the noise spectral density. For each of the 13 bands we have compute this proportionality factor, usually known as sensitivity depth, which at the end resulted almost constant over the 13 bands analyzed (up to a maximum of 15 % of error).…”

Section: Upper Limitsmentioning

confidence: 99%

Directed search for continuous gravitational-wave signals from the Galactic Center in the Advanced LIGO second observing run

et al. 2020

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In this work we present the results of a search for continuous gravitational waves from the Galactic Center using LIGO O2 data. The search uses the Band-Sampled-Data directed search pipeline, which performs a semi-coherent wide-parameter-space search, exploiting the robustness of the Frequency-Hough transform algorithm. The search targets signals emitted by isolated asymmetric spinning neutron stars, located within the few inner parsecs of the Galactic Center. The frequencies covered in this search range between 10 Hz and 710 Hz with a spin-down range from −1.8 × 10 −9 Hz/s to 3.7 × 10 −11 Hz/s. No continuous wave signal has been detected and upper limits on the gravitational wave amplitude are presented. The most stringent upper limit at 95% confidence level, for Livingston detector, is ∼ 1.4 × 10 −25 at 163 Hz. To date, this is the most sensitive directed search for continuous gravitational-wave signals from the Galactic Center and the first search of this kind using LIGO second observing run.

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Section: Upper Limitsmentioning

confidence: 99%

Directed search for continuous gravitational-wave signals from the Galactic Center in the Advanced LIGO second observing run

et al. 2020

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“…Ref. [10]): measure the detection probability p det at a chosen false-alarm level p fa for a signal population of fixed amplitude h 0 , with all other signal parameters (i.e., polarization, sky position, frequency and spindown) drawn randomly from their priors. In order to characterize the signal strength in noise, we use the sensitivity depth D [10,38], defined as…”

Section: A Benchmark Definitionsmentioning

confidence: 99%

“…By varying the injected D we can eventually find D 90% for the desired p det ¼ 90% (see e.g., Ref. [10] for more details and discussion of this standard "upper limit" procedure). By a final injection þ recovery Monte Carlo we can verify that the achieved WEAVE detection probability for the quoted thresholds and signal strengths D 90% in Table III is p det ∼ 90-91%, which is sufficiently accurate for our present purposes.…”

Section: B Weave Matched-filtering Sensitivitymentioning

confidence: 99%

Deep-learning continuous gravitational waves

et al. 2019

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We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matchedfiltering search using the WEAVE pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T ¼ 10 5 s of data, and a "harder" search using T ¼ 10 6 s. The detection probability p det is measured on a signal population for which matched filtering achieves p det ¼ 90% in Gaussian noise. In the easiest test case (T ¼ 10 5 s at 20 Hz) the DNN achieves p det ∼ 88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until p det ∼ 13% and a loss of ∼66% in sensitivity depth in the hardest case (T ¼ 10 6 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

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“…However this may not be so for searches which are compute-power limited, for example in the search for CWs or the search for gamma-ray pulsars. These computationally-limited searches often employ multiple hierarchical stages, which mix semi-coherent and coherent stages, each employing its own metric for template placement [28,39,46,69,70,[78][79][80][81][82][83][84][85][86][87][88][89][90]. Those hierarchical stages sometimes operate at substantial mismatches in the range m ∈ [0.5, 0.7], and here, the spherical approximation is an improvement on the conventional quadratic approximation.…”

Section: Why Does It Matter?mentioning

confidence: 99%

Spherical ansatz for parameter-space metrics

Allen

2019

Phys. Rev. D

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A fundamental quantity in signal analysis is the metric g ab on parameter space, which quantifies the fractional "mismatch" m between two (time-or frequency-domain) waveforms. When searching for weak gravitational-wave or electromagnetic signals from sources with unknown parameters λ (masses, sky locations, frequencies, etc.) the metric can be used to create and/or characterize "template banks". These are grids of points in parameter space; the metric is used to ensure that the points are correctly separated from one another. For small coordinate separations dλ a between two points in parameter space, the traditional ansatz for the mismatch is a quadratic form m = g ab dλ a dλ b . This is a good approximation for small separations but at large separations it diverges, whereas the actual mismatch is bounded. Here we introduce and discuss a simple "spherical" ansatz for the mismatch m = sin 2 ( g ab dλ a dλ b ). This agrees with the metric ansatz for small separations, but we show that in simple cases it provides a better (and bounded) approximation for large separations, and argue that this is also true in the generic case. This ansatz should provide a more accurate approximation of the mismatch for semi-coherent searches, and may also be of use when creating grids for hierarchical searches that (in some stages) operate at relatively large mismatch. I. MATCHED FILTERING AND THE OVERLAP BETWEEN TEMPLATESMore than two decades ago, when the first generation of interferometric gravitational wave (GW) detectors were still in the planning stages, a handful of pioneers investigated the techniques that would be needed to detect GW signals [1][2][3][4][5][6][7][8][9][10]. At that time there were three main challenges. First, the signals were weak in comparison with the noise from the detectors, so needed to be "teased out" of the data stream with optimal or nearoptimal methods. Second, the parameters describing the signals (such as the object masses in a binary system, or the rotation frequency and spindown rate of a neutron star) were not known. This required repeated searches for signals with many different parameter combinations, creating a significant computational challenge. Lastly, even if the parameters were known precisely, for some sources the waveforms could only be calculated approximately. The errors could be estimated but not sharply quantified.The solution to the first problem is to use "matched filtering" [3,[6][7][8][11][12][13][14][15][16]. In the simplest case [17] the time-dependent output S(t) of the detector is correlated with a template T (t) to produce a statistic ρ = (T, S).(1.1)If the template is normalized (T, T ) = 1 then ρ is called the signal-to-noise ratio (SNR). This is reviewed in a signal-processing context in [18,19] and in the GW context in [20] and [21]. The positive-definite inner product in Eq. (1.1) can be expressed in different ways. For example if the instrument noise is white (or the signal is confined to a narrow enough range of frequency that the noise is white in that band) then...

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Fast and accurate sensitivity estimation for continuous-gravitational-wave searches

Cited by 69 publications

References 86 publications

Directed search for continuous gravitational-wave signals from the Galactic Center in the Advanced LIGO second observing run

Directed search for continuous gravitational-wave signals from the Galactic Center in the Advanced LIGO second observing run

Deep-learning continuous gravitational waves

Spherical ansatz for parameter-space metrics

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