Search method for long-duration gravitational-wave transients from neutron stars

We introduce a search method for a new class of gravitational-wave signals, namely long-duration O(hours - weeks) transients from spinning neutron stars. We discuss the astrophysical motivation from glitch relaxation models and we derive a rough estimate for the maximal expected signal strength based on the superfluid excess rotational energy. The transient signal model considered here extends the traditional class of infinite-duration continuous-wave signals by a finite start-time and duration. We derive a multi-detector Bayes factor for these signals in Gaussian noise using \F-statistic amplitude priors, which simplifies the detection statistic and allows for an efficient implementation. We consider both a fully coherent statistic, which is computationally limited to directed searches for known pulsars, and a cheaper semi-coherent variant, suitable for wide parameter-space searches for transients from unknown neutron stars. We have tested our method by Monte-Carlo simulation, and we find that it outperforms orthodox maximum-likelihood approaches both in sensitivity and in parameter-estimation quality.Comment: 20 pages, 9 figures; submitted to PR

Matching matched filtering with deep networks in gravitational-wave astronomy

We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well modeled transient gravitational-wave signals is matched filtering. However, the computational cost of such searches in low latency will grow dramatically as the low frequency sensitivity of gravitational-wave detectors improves. Convolutional neural networks provide a highly computationally efficient method for signal identification in which the majority of calculations are performed prior to data taking during a training process. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same datasets when considering the sensitivity defined by Reciever-Operator characteristics.Comment: 5 pages, 3 figures, submitted to PR

Data Analysis Methods for Testing Alternative Theories of Gravity with LISA Pathfinder

In this paper we present a data analysis approach applicable to the potential saddle-point fly-by mission extension of LISA Pathfinder (LPF). At the peak of its sensitivity, LPF will sample the gravitational field in our Solar System with a precision of several $\text{fm/s}^2/\sqrt{\text{Hz}}$ at frequencies around $1\,\text{mHz}$. Such an accurate accelerometer will allow us to test alternative theories of gravity that predict deviations from Newtonian dynamics in the non-relativistic limit. As an example, we consider the case of the Tensor-Vector-Scalar theory of gravity and calculate, within the non-relativistic limit of this theory, the signals that anomalous tidal stresses generate in LPF. We study the parameter space of these signals and divide it into two subgroups, one related to the mission parameters and the other to the theory parameters that are determined by the gravity model. We investigate how the mission parameters affect the signal detectability concluding that these parameters can be determined with the sufficient precision from the navigation of the spacecraft and fixed during our analysis. Further, we apply Bayesian parameter estimation and determine the accuracy to which the gravity theory parameters may be inferred. We evaluate the portion of parameter space that may be eliminated in case of no signal detection and estimate the detectability of signals as a function of parameter space location. We also perform a first investigation of non-Gaussian "noise-glitches" that may occur in the data. The analysis we develop is universal and may be applied to anomalous tidal stress induced signals predicted by any theory of gravity

Host redshifts from gravitational-wave observations of binary neutron star mergers

Inspiralling compact binaries as standard sirens will soon become an invaluable tool for cosmology when advanced interferometric gravitational-wave detectors begin their observations in the coming years. However, a degeneracy in the information carried by gravitational waves between the total rest-frame mass $M$ and the redshift $z$ of the source implies that neither can be directly extracted from the signal, but only the combination $M(1+z)$, the redshifted mass. Recent work has shown that for binary neutron star systems, a tidal correction to the gravitational-wave phase in the late-inspiral signal that depends on the rest-frame source mass could be used to break the mass-redshift degeneracy. We propose here to use the signature encoded in the post-merger signal to deduce the redshift to the source. This will allow an accurate extraction of the intrinsic rest-frame mass of the source, in turn permitting the determination of source redshift and luminosity distance solely from gravitational-wave observations. This will herald a new era in precision cosmography and astrophysics. Using numerical simulations of binary neutron star mergers of very slightly different mass, we model gravitational-wave signals at different redshifts and use Bayesian parameter estimation to determine the accuracy with which the redshift can be extracted for a source of known mass. We find that the Einstein Telescope can determine the source redshift to $sim 10$--$20$ at redshifts of $z<0.04$.Comment: 10 pages, 4 figures; same as the version before except for acknowledgment

Bayesian versus frequentist upper limits

While gravitational waves have not yet been measured directly, data analysis from detection experiments commonly includes an upper limit statement. Such upper limits may be derived via a frequentist or Bayesian approach; the theoretical implications are very different, and on the technical side, one notable difference is that one case requires maximization of the likelihood function over parameter space, while the other requires integration. Using a simple example (detection of a sinusoidal signal in white Gaussian noise), we investigate the differences in performance and interpretation, and the effect of the "trials factor", or "look-elsewhere effect".Comment: http://cdsweb.cern.ch/record/1306523/files/CERN-2011-006.pdf, http://indico.cern.ch/materialDisplay.py?contribId=52&materialId=paper&confId=10774

Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders

All-sky searches for continuous gravitational waves are generally model dependent and computationally costly to run. By contrast, SOAP is a model-agnostic search that rapidly returns candidate signal tracks in the time-frequency plane. In this work we extend the SOAP search to return broad Bayesian posteriors on the astrophysical parameters of a specific signal model. These constraints drastically reduce the volume of parameter space that any follow-up search needs to explore, so increasing the speed at which candidates can be identified and confirmed. Our method uses a machine learning technique, specifically a conditional variational auto-encoder, and delivers a rapid estimation of the posterior distribution of the four Doppler parameters of a continuous wave signal. It does so without requiring a clear definition of a likelihood function, or being shown any true Bayesian posteriors in training. We demonstrate how the Doppler parameter space volume can be reduced by a factor of $\mathcal{O}(10^{-7})$ for signals of SNR 100

A robust machine learning algorithm to search for continuous gravitational waves

Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set and at a 1% false alarm probability we showed that at 95% efficiency a fully-automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10 Hz$^{-1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention

Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach

We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a convolutional neural network can be used to detect and classify the gravitational wave signals buried in noise. For the waveforms used in the training of the convolutional neural network, our results suggest that a network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+, advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse supernovae within the Large and Small Magellanic Clouds, or a Galactic event if the explosion mechanism is the neutrino-driven mechanism. By testing the convolutional neural network with waveforms not used for training, we show that the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities are 70% and 93% respectively. All at false alarm probability equal to 10%

Cosmological inference using only gravitational wave observations of binary neutron stars

Gravitational waves emitted during the coalescence of binary neutron star systems are self-calibrating signals. As such, they can provide a direct measurement of the luminosity distance to a source without the need for a cross-calibrated cosmic distance-scale ladder. In general, however, the corresponding redshift measurement needs to be obtained via electromagnetic observations since it is totally degenerate with the total mass of the system. Nevertheless, Fisher matrix studies have shown that, if information about the equation of state of the neutron stars is available, it is possible to extract redshift information from the gravitational wave signal alone. Therefore, measuring the cosmological parameters in pure gravitational-wave fashion is possible. Furthermore, the huge number of sources potentially observable by the Einstein Telescope has led to speculations that the gravitational wave measurement is potentially competitive with traditional methods. The Einstein Telescope is a conceptual study for a third generation gravitational wave detector which is designed to yield 10^3–10^7 detections of binary neutron star systems per year. This study presents the first Bayesian investigation of the accuracy with which the cosmological parameters can be measured using information coming only from the gravitational wave observations of binary neutron star systems by the Einstein Telescope. We find, by direct simulation of 10^3 detections of binary neutron stars, that, within our simplifying assumptions, H_0, Ω_m, Ω_Λ, w_0 and w_1 can be measured at the 95% level with an accuracy of ∼8% , 65%, 39%, 80% and 90%, respectively. We also find, by extrapolation, that a measurement accuracy comparable with current measurements by Planck is possible if the number of gravitational wave events observed is O(10^(6–7)) . We conclude that, while not competitive with electromagnetic missions in terms of significant digits, gravitational waves alone are capable of providing a complementary determination of the dynamics of the Universe