An information-theoretic approach to the gravitational-wave burst detection problem

The observational era of gravitational-wave astronomy began in the Fall of 2015 with the detection of GW150914. One potential type of detectable gravitational wave is short-duration gravitational-wave bursts, whose waveforms can be difficult to predict. We present the framework for a new detection algorithm for such burst events -- \textit{oLIB} -- that can be used in low-latency to identify gravitational-wave transients independently of other search algorithms. This algorithm consists of 1) an excess-power event generator based on the Q-transform -- \textit{Omicron} --, 2) coincidence of these events across a detector network, and 3) an analysis of the coincident events using a Markov chain Monte Carlo Bayesian evidence calculator -- \textit{LALInferenceBurst}. These steps compress the full data streams into a set of Bayes factors for each event; through this process, we use elements from information theory to minimize the amount of information regarding the signal-versus-noise hypothesis that is lost. We optimally extract this information using a likelihood-ratio test to estimate a detection significance for each event. Using representative archival LIGO data, we show that the algorithm can detect gravitational-wave burst events of astrophysical strength in realistic instrumental noise across different burst waveform morphologies. We also demonstrate that the combination of Bayes factors by means of a likelihood-ratio test can improve the detection efficiency of a gravitational-wave burst search. Finally, we show that oLIB's performance is robust against the choice of gravitational-wave populations used to model the likelihood-ratio test likelihoods

End-to-end algorithm for hierarchical area searches for long-duration GW sources for GEO 600

We describe a hierarchical, highly parallel computer algorithm to perform searches for unknown sources of continuous gravitational waves -- spinning neutron stars in the Galaxy -- over wide areas of the sky and wide frequency bandwidths. We optimize the algorithm for an observing period of 4 months and an available computing power of 20 Gflops, in a search for neutron stars resembling millisecond pulsars. We show that, if we restrict the search to the galactic plane, the method will detect any star whose signal is stronger than 15 times the $1\sigma$ noise level of a detector over that search period. Since on grounds of confidence the minimum identifiable signal should be about 10 times noise, our algorithm does only 50% worse than this and runs on a computer with achievable processing speed.Comment: 7 pages, for proceedings of Jan 1999 Moriond meeting "Gravitational Waves and Experimental Gravity

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

An efficient Matched Filtering Algorithm for the Detection of Continuous Gravitational Wave Signals

We describe an efficient method of matched filtering over long (greater than 1 day) time baselines starting from Fourier transforms of short durations (roughly 30 minutes) of the data stream. This method plays a crucial role in the search algorithm developed by Schutz and Papa for the detection of continuous gravitational waves from pulsars. Also, we discuss the computational cost--saving approximations used in this method, and the resultant performance of the search algorithm.Comment: 4 pages, text only, accepted for publication in the proceedings of the 3rd Amaldi conference on gravitational wave

Reducing the number of templates for aligned-spin compact binary coalescence gravitational wave searches using metric-agnostic template nudging

Efficient multi-dimensional template placement is crucial in computationally intensive matched-filtering searches for Gravitational Waves (GWs). Here, we implement the Neighboring Cell Algorithm (NCA) to improve the detection volume of an existing Compact Binary Coalescence (CBC) template bank. This algorithm has already been successfully applied for a binary millisecond pulsar search in data from the Fermi satellite. It repositions templates from over-dense regions to under-dense regions and reduces the number of templates that would have been required by a stochastic method to achieve the same detection volume. Our method is readily generalizable to other CBC parameter spaces. Here we apply this method to the aligned--single-spin neutron-star--black-hole binary coalescence inspiral-merger-ringdown gravitational wave parameter space. We show that the template nudging algorithm can attain the equivalent effectualness of the stochastic method with 12% fewer templates

Search algorithm for a gravitational wave signal in association with Gamma Ray Burst GRB030329 using the LIGO detectors

One of the brightest Gamma Ray Burst ever recorded, GRB030329, occurred during the second science run of the LIGO detectors. At that time, both interferometers at the Hanford, WA LIGO site were in lock and acquiring data. The data collected from the two Hanford detectors was analyzed for the presence of a gravitational wave signal associated with this GRB. This paper presents a detailed description of the search algorithm implemented in the current analysis.Comment: To appear in the Proceedings of 8th Gravitational Wave Data Analysis Workshop (Milwaukee, WI) (Class. Quantum Grav.

Application of the Hilbert-Huang Transform to the Search for Gravitational Waves

We present the application of a novel method of time-series analysis, the Hilbert-Huang Transform, to the search for gravitational waves. This algorithm is adaptive and does not impose a basis set on the data, and thus the time-frequency decomposition it provides is not limited by time-frequency uncertainty spreading. Because of its high time-frequency resolution it has important applications to both signal detection and instrumental characterization. Applications to the data analysis of the ground and space based gravitational wave detectors, LIGO and LISA, are described

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure