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Register a new userSearching for continuous gravitational wave signals: the hierarchical Hough transform algorithm
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It is well known that matched filtering techniques cannot be applied for searching extensive parameter space volumes for continuous gravitational wave signals. This is the reason why alternative strategies are being pursued. Hierarchical strategies are best at investigating a large parameter space when there exist computational power constraints. Algorithms of this kind are being implemented by all the groups that are developing software for analyzing the data of the gravitational wave detectors that will come online in the next years. In this talk we will report about the hierarchical Hough transform method that the GEO 600 data analysis team at the Albert Einstein Institute is developing. The three step hierarchical algorithm has been described elsewhere. In this talk we will focus on some of the implementational aspects we are currently concerned with.
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The direct detection of continuous gravitational waves from pulsars is a much anticipated discovery in the emerging field of multi-messenger gravitational wave (GW) astronomy. Because putative pulsar signals are exceedingly weak large amounts of data need to be integrated to achieve desired sensitivity. Contemporary searches use ingenious ad-hoc methods to reduce computational complexity. In this paper we provide analytical expressions for the Fourier transform of realistic pulsar signals. This provides description of the manifold of pulsar signals in the Fourier domain, used by many search methods. We analyze the shape of the Fourier transform and provide explicit formulas for location and size of peaks resulting from stationary frequencies. We apply our formulas to analysis of recently identified outlier at 1891.76 Hz.
Searches for continuous gravitational waves from unknown sources attempt to detect long-lasting gravitational radiation by identifying Doppler-modulated signatures in the data. Semicoherent methods allow for wide parameter space surveys, identifying interesting regions to be followed up using more sensitive (and computationally expensive) tools. Thus, it is required to properly understand the parameter space structure under study, as failing to do so could significantly affect the effectiveness of said strategies. We introduce a new measure for distances in parameter space suited for semicoherent continuous wave searches. This novel approach, based on comparing time-frequency tracks, can be applied to any kind of quasi-monochromatic continuous wave signals and adapts itself to the underlying structure of the parameter space under study. In a first application to the post-processing stage of an all-sky search for continuous waves from neutron stars in binary systems, we demonstrate a search sensitivity improvement by solely replacing previous ad hoc distance measures in the candidate clustering procedure by the new proposal.
Continuous gravitational wave signals, like those expected by asymmetric spinning neutron stars, are among the most promising targets for LIGO and Virgo detectors. The development of fast and robust data analysis methods is crucial to increase the chances of a detection. We have developed a new and flexible general data analysis framework for the search of this kind of signals, which allows to reduce the computational cost of the analysis by about two orders of magnitude with respect to current procedures. This can correspond, at fixed computing cost, to a sensitivity gain of up to 10%-20%, depending on the search parameter space. Some possible applications are discussed, with a particular focus on a directed search for sources in the Galactic center. Validation through the injection of artificial signals in the data of Advanced LIGO first observational science run is also shown.
We report on an all-sky search for periodic gravitational waves in the frequency range $mathrm{50-1000 Hz}$ with the first derivative of frequency in the range $-8.9 times 10^{-10}$ Hz/s to zero in two years of data collected during LIGOs fifth science run. Our results employ a Hough transform technique, introducing a $chi^2$ test and analysis of coincidences between the signal levels in years 1 and 2 of observations that offers a significant improvement in the product of strain sensitivity with compute cycles per data sample compared to previously published searches. Since our search yields no surviving candidates, we present results taking the form of frequency dependent, 95$%$ confidence upper limits on the strain amplitude $h_0$. The most stringent upper limit from year 1 is $1.0times 10^{-24}$ in the $mathrm{158.00-158.25 Hz}$ band. In year 2, the most stringent upper limit is $mathrm{8.9times10^{-25}}$ in the $mathrm{146.50-146.75 Hz}$ band. This improved detection pipeline, which is computationally efficient by at least two orders of magnitude better than our flagship Einstein$@$Home search, will be important for quick-look searches in the Advanced LIGO and Virgo detector era.
We present the first application of a hierarchical Markov Chain Monte Carlo (MCMC) follow-up on continuous gravitational-wave candidates from real-data searches. The follow-up uses an MCMC sampler to draw parameter-space points from the posterior distribution, constructed using the matched-filter as a log-likelihood. As outliers are narrowed down, coherence time increases, imposing more restrictive phase-evolution templates. We introduce a novel Bayes factor to compare results from different stages: The signal hypothesis is derived from first principles, while the noise hypothesis uses extreme value theory to derive a background model. The effectiveness of our proposal is evaluated on fake Gaussian data and applied to a set of 30 outliers produced by different continuous wave searches on O2 Advanced LIGO data. The results of our analysis suggest all but five outliers are inconsistent with an astrophysical origin under the standard continuous wave signal model. We successfully ascribe four of the surviving outliers to instrumental artifacts and a strong hardware injection present in the data. The behavior of the fifth outlier suggests an instrumental origin as well, but we could not relate it to any known instrumental cause.
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