Robust semicoherent searches for continuous gravitational waves with noise and signal models including hours to days long transients

The vulnerability to single-detector instrumental artifacts in standard detection methods for long-duration quasimonochromatic gravitational waves from nonaxisymmetric rotating neutron stars [continuous waves (CWs)] was addressed in past work [D. Keitel et al., Phys. Rev. D 89, 064023 (2014).] by a Bayesian approach. An explicit model of persistent single-detector disturbances led to a generalized detection statistic with improved robustness against such artifacts. Since many strong outliers in semicoherent searches of LIGO data are caused by transient disturbances that last only a few hours, we extend the noise model to cover such limited-duration disturbances, and demonstrate increased robustness in realistic simulated data. Besides long-duration CWs, neutron stars could also emit transient signals which, for a limited time, also follow the CW signal model (tCWs). As a pragmatic alternative to specialized transient searches, we demonstrate how to make standard semicoherent CW searches more sensitive to transient signals. Considering tCWs in a single segment of a semicoherent search, Bayesian model selection yields a new detection statistic that does not add significant computational cost. On simulated data, we find that it increases sensitivity towards tCWs, even of varying durations, while not sacrificing sensitivity to classical CW signals, and still being robust to transient or persistent single-detector instrumental artifacts.Comment: 16 pages, 6 figures, REVTeX4.

Line-robust statistics for continuous gravitational waves: safety in the case of unequal detector sensitivities

The multi-detector F-statistic is close to optimal for detecting continuous gravitational waves (CWs) in Gaussian noise. However, it is susceptible to false alarms from instrumental artefacts, for example quasi-monochromatic disturbances ('lines'), which resemble a CW signal more than Gaussian noise. In a recent paper [Keitel et al 2014, PRD 89 064023], a Bayesian model selection approach was used to derive line-robust detection statistics for CW signals, generalising both the F-statistic and the F-statistic consistency veto technique and yielding improved performance in line-affected data. Here we investigate a generalisation of the assumptions made in that paper: if a CW analysis uses data from two or more detectors with very different sensitivities, the line-robust statistics could be less effective. We investigate the boundaries within which they are still safe to use, in comparison with the F-statistic. Tests using synthetic draws show that the optimally-tuned version of the original line-robust statistic remains safe in most cases of practical interest. We also explore a simple idea on further improving the detection power and safety of these statistics, which we however find to be of limited practical use.Comment: 21 pages, 11 figures, updated to match published versio

The Adaptive Transient Hough method for long-duration gravitational wave transients

This paper describes a new semi-coherent method to search for transient gravitational waves of intermediate duration (hours to days). In order to search for newborn isolated neutron stars with their possibly very rapid spin-down, we model the frequency evolution as a power law. The search uses short Fourier transforms from the output of ground-based gravitational wave detectors and applies a weighted Hough transform, also taking into account the signal's amplitude evolution. We present the technical details for implementing the algorithm, its statistical properties, and a sensitivity estimate. A first example application of this method was in the search for GW170817 post-merger signals, and we verify the estimated sensitivity with simulated signals for this case.Comment: 13 pages, 14 figure

Galactic Double Neutron Star total masses and Gaussian mixture model selection

Huang et al. [arXiv:1804.03101] have analysed the population of 15 known galactic Double Neutron Stars (DNSs) regarding the total masses of these systems. They suggest the existence of two sub-populations, and report likelihood-based preference for a two-component Gaussian mixture model over a single Gaussian distribution. This note offers a cautionary perspective on model selection for this data set: Especially for such a small sample size, a pure likelihood ratio test can encourage overfitting. This can be avoided by penalising models with a higher number of free parameters. Re-examining the DNS total mass data set within the class of Gaussian mixture models, this can be achieved through several simple and well-established statistical tests, including information criteria (AICc, BIC), cross-validation, Bayesian evidence ratios and a penalised EM-test. While this re-analysis confirms the basic finding that a two-component mixture is consistent with the data, the model selection criteria consistently indicate that there is no robust preference for it over a single-component fit. Additional DNS discoveries will be needed to settle the question of sub-populations.Comment: 9 pages and 10 figures including appendices, updated version as accepted by MNRA

Improving robustness of continuous-gravitational-wave searches against signal-like instrumental artefacts and a concept for an octahedral gravitational-wave detector in space

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An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data

Continuous gravitational waves (CW) are expected from spinning neutron stars with non-axisymmetric deformations. A network of interferometric detectors (LIGO, Virgo and GEO600) is looking for these signals. They are predicted to be very weak and retrievable only by integration over long observation times. One of the standard methods of CW data analysis is the multi-detector F-statistic. In a typical search, the F-statistic is computed over a range in frequency, spin-down and sky position, and the candidates with highest F values are kept for further analysis. However, this detection statistic is susceptible to a class of noise artifacts, strong monochromatic lines in a single detector. By assuming an extended noise model - standard Gaussian noise plus single-detector lines - we can use a Bayesian odds ratio to derive a generalized detection statistic, the line veto (LV-) statistic. In the absence of lines, it behaves similarly to the F-statistic, but it is more robust against line artifacts. In the past, ad-hoc post-processing vetoes have been implemented in searches to remove these artifacts. Here we provide a systematic framework to develop and benchmark this class of vetoes. We present our results from testing this LV-statistic on simulated data.Comment: 2 pages, 1 figure, to be published in Proceedings of Statistical Challenges in Modern Astronomy V, Springer 201

Faster search for long gravitational-wave transients: GPU implementation of the transient F-statistic

The F-statistic is an established method to search for continuous gravitational waves from spinning neutron stars. Prix et al. (2011) introduced a variant for transient quasi-monochromatic signals. Possible astrophysical scenarios for such transients include glitching pulsars, newborn neutron stars and accreting systems. Here we present a new implementation of the transient F-statistic, using pyCUDA to leverage the power of modern graphics processing units (GPUs). The obtained speedup allows efficient searches over much wider parameter spaces, especially when using more realistic transient signal models including time-varying (e.g. exponentially decaying) amplitudes. Hence, it can enable comprehensive coverage of glitches in known nearby pulsars, improve the follow-up of outliers from continuous-wave searches, and might be an important ingredient for future blind all-sky searches for unknown neutron stars.Comment: 13 pages, 3 figures; v2: updated reference to 1710.02327 and its erratu

Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method

Gravitational lensing has been extensively observed for electromagnetic signals, but not yet for gravitational waves (GWs). Detecting lensed GWs will have many astrophysical and cosmological applications, and becomes more feasible as the sensitivity of the LIGO-Virgo-KAGRA detectors improves. One of the missing ingredients to robustly identify lensed GWs is to ensure that the statistical tests used are robust under the choice of underlying waveform models. We present the first systematic study of possible waveform systematics in identifying candidates for strongly lensed GW event pairs, focusing on the posterior overlap method. To this end, we compare Bayes factors from all posteriors using different waveforms included in GWTC data releases from the first three observing runs (O1-O3). We find that waveform choice yields a wide spread of Bayes factors in some cases. However, it is likely that no event pairs from O1 to O3 were missed due to waveform choice. We also perform parameter estimation with additional waveforms for interesting cases, to understand the observed differences. We also briefly explore if computing the overlap from different runs for the same event can be a useful metric for waveform systematics or sampler issues, independent of the lensing scenario.Comment: 29 pages, 5 figures, comments welcom

Matched-filter study and energy budget suggest no detectable gravitational-wave 'extended emission' from GW170817

Van Putten & Della Valle (2018) have reported a possible detection of gravitational-wave 'extended emission' from a neutron star remnant of GW170817. Starting from the time-frequency evolution and total emitted energy of their reported candidate, we show that such an emission is not compatible with the current understanding of neutron stars. We explore the additional required physical assumptions to make a full waveform model, for example, taking the optimistic emission from a spining-down neutron star with fixed quadrupolar deformation, and study whether even an ideal single-template matched-filter analysis could detect an ideal, fully phase-coherent signal. We find that even in the most optimistic case an increase in energy and extreme parameters would be required for a confident detection with LIGO sensitivity as of 2018-08-17. The argument also holds for other waveform models following a similar time-frequency track and overall energy budget. Single-template matched filtering on the LIGO data around GW170817, and on data with added simulated signals, verifies the expected sensitivity scaling and the overall statistical expectation.Comment: 9 pages, 6 figures, updated version as accepted by MNRA

Convolutional neural network search for long-duration transient gravitational waves from glitching pulsars

Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Current methods to search for such signals rely on matched filtering and a brute-force grid search over possible signal durations, which is sensitive but can become very computationally expensive. We develop a method to search for post-glitch signals on combining matched filtering with convolutional neural networks, which reaches similar sensitivities to the standard method at false-alarm probabilities relevant for practical searches, while being significantly faster. We specialize to the Vela glitch during the LIGO-Virgo O2 run, and set upper limits on the gravitational-wave strain amplitude from the data of the two LIGO detectors for both constant-amplitude and exponentially decaying signals.Comment: 19 pages, 9 figures. Comments welcom