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Register a new userEnabling high confidence detections of gravitational-wave bursts
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With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration un-modeled gravitational wave signals have been hampered by transient noise artifacts, or glitches, in the detectors. In some cases, even high signal-to-noise simulated astrophysical signals have proven difficult to distinguish from glitches, so that essentially any plausible signal could be detected with at most 2-3 $sigma$ level confidence. We have put forth the BayesWave algorithm to differentiate between generic gravitational wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWaves capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement.
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We present a method for assigning a statistical significance to detection candidates in targeted searches for continuous gravitational waves from known pulsars, without assuming the detector noise is Gaussian and stationary. We take advantage of the expected Doppler phase modulation of the signal induced by Earths orbital motion, as well as the amplitude modulation induced by Earths spin, to effectively blind the search to real astrophysical signals from a given location in the sky. We use this sky-shifting to produce a large number of noise-only data realizations to empirically estimate the background of a search and assign detection significances, in a similar fashion to the use of timeslides in searches for compact binaries. We demonstrate the potential of this approach by means of simulated signals, as well as hardware injections into real detector data. In a study of simulated signals in non-Gaussian noise, we find that our method outperforms another common strategy for evaluating detection significance. We thus demonstrate that this and similar techniques have the potential to enable a first confident detection of continuous gravitational waves.
We show that for detections of gravitational-wave transients, constraints can be given on physical parameters of the source without using any specific astrophysical models. Relying only on fundamental principles of general relativity, we can set upper limits on the size, mass, and distance of the source solely from characteristics of the observed waveform. If the distance of the source is known from independent (e.g. electromagnetic) observations, we can also set lower limits on the mass and size. As a demonstration, we tested these constraints on binary black hole signals observed by the LIGO and Virgo detectors during their first and second observing runs, as well as on simulated binary black hole and core-collapse supernova signals reconstructed from simulated detector data. We have found that our constraints are valid for all analyzed source types, but their efficiency (namely, how far they are from the true parameter values) strongly depends on the source type, ranging from being in the same order of magnitude to a several orders of magnitude difference. In cases when a gravitational-wave signal is reconstructed without waveform templates and no astrophysical model on the source is available, these constraints provide the only quantitative characterization of the source that can guide the astrophysical modeling process.
The observed distributions of the source properties from gravitational-wave detections are biased due to the selection effects and detection criteria in the detections, analogous to the Malmquist bias. In this work, this observation bias is investigated through its fundamental statistical and physical origins. An efficient semi-analytical formulation for its estimation is derived which is as accurate as the standard method of numerical simulations, with only a millionth of the computational cost. Then, the estimated bias is used for model independent inferences on the binary black hole population. These inferences show additional structures, specifically two potential mass gaps in the joint mass distribution, which were not found via modelled inferences. Example ready-to-use scripts and some produced datasets for this method are shared in an online repository.
A central challenge in Gravitational Wave Astronomy is identifying weak signals in the presence of non-stationary and non-Gaussian noise. The separation of gravitational wave signals from noise requires good models for both. When accurate signal models are available, such as for binary Neutron star systems, it is possible to make robust detection statements even when the noise is poorly understood. In contrast, searches for un-modeled transient signals are strongly impacted by the methods used to characterize the noise. Here we take a Bayesian approach and introduce a multi-component, variable dimension, parameterized noise model that explicitly accounts for non-stationarity and non-Gaussianity in data from interferometric gravitational wave detectors. Instrumental transients (glitches) and burst sources of gravitational waves are modeled using a Morlet-Gabor continuous wavelet frame. The number and placement of the wavelets is determined by a trans-dimensional Reversible Jump Markov Chain Monte Carlo algorithm. The Gaussian component of the noise and sharp line features in the noise spectrum are modeled using the BayesLine algorithm, which operates in concert with the wavelet model.
Gravitational wave astronomy has established its role in measuring the equation of state governing cold supranuclear matter. To date and in the near future, gravitational wave measurements from neutron star binaries are likely to be restricted to the inspiral. However, future upgrades and the next generation of gravitational wave detectors will enable us to detect the gravitational wave signatures emitted after the merger of two stars, at times when densities beyond those in single neutron stars are reached. Therefore, the postmerger gravitational wave signal enables studies of supranuclear matter at its extreme limit. To support this line of research, we present new and updated phenomenological relations between the binary properties and characteristic features of the postmerger evolution. Most notably, we derive an updated relation connecting the mass-weighted tidal deformability and the maximum neutron star mass to the dominant emission frequency of the postmerger spectrum. With the help of a configuration-independent Bayesian analysis using simplified Lorentzian model functions, we find that the main emission frequency of the postmerger remnant, for signal-to-noise ratios of $8$ and above, can be extracted within a 1-sigma uncertainty of about 100 Hz for Advanced LIGO and Advanced Virgos design sensitivities. In some cases, even a postmerger signal-to-noise ratio of $4$ can be sufficient to determine the main emission frequency. This will enable us to measure binary and equation-of-state properties from the postmerger, to perform a consistency check between different parts of the binary neutron star coalescence, and to put our physical interpretation of neutron star mergers to the test.
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