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

Localization of short duration gravitational-wave transients with the early advanced LIGO and Virgo detectors

The Laser Interferometer Gravitational wave Observatory (LIGO) and Virgo, advanced ground-based gravitational-wave detectors, will begin collecting science data in 2015. With first detections expected to follow, it is important to quantify how well generic gravitational-wave transients can be localized on the sky. This is crucial for correctly identifying electromagnetic counterparts as well as understanding gravitational-wave physics and source populations. We present a study of sky localization capabilities for two search and parameter estimation algorithms: \emph{coherent WaveBurst}, a constrained likelihood algorithm operating in close to real-time, and \emph{LALInferenceBurst}, a Markov chain Monte Carlo parameter estimation algorithm developed to recover generic transient signals with latency of a few hours. Furthermore, we focus on the first few years of the advanced detector era, when we expect to only have two (2015) and later three (2016) operational detectors, all below design sensitivity. These detector configurations can produce significantly different sky localizations, which we quantify in detail. We observe a clear improvement in localization of the average detected signal when progressing from two-detector to three-detector networks, as expected. Although localization depends on the waveform morphology, approximately 50% of detected signals would be imaged after observing 100-200 deg$^2$ in 2015 and 60-110 deg$^2$ in 2016, although knowledge of the waveform can reduce this to as little as 22 deg$^2$. This is the first comprehensive study on sky localization capabilities for generic transients of the early network of advanced LIGO and Virgo detectors, including the early LIGO-only two-detector configuration.Comment: 18 pages, 8 figure

A Coincidence Null Test for Poisson-Distributed Events

When transient events are observed with multiple sensors, it is often necessary to establish the significance of coincident events. We derive a universal null test for an arbitrary number of sensors motivated by the archetypal detection problem for independent Poisson-distributed events in gravitational-wave detectors such as LIGO and Virgo. In these detectors, transient events may be witnessed by myriad channels that record interferometric signals and the surrounding physical environment. We apply our null test to a broad set of simulated gravitational-wave events as well as to a real gravitational-wave detection to determine which auxiliary channels do and do not witness real gravitational waves, and therefore which are safe to use when constructing vetoes. We also describe how our approach can be used to study detector artifacts and their origin, as well as to quantify the statistical independence of candidate GW signals from noise artifacts observed in auxiliary channels.Comment: 14 pages, 7 Figure

Noise Reduction in Gravitational-wave Data via Deep Learning

With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by ∼21.6% and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors

Observational implications of lowering the LIGO-Virgo alert threshold

The recent detection of the binary-neutron-star merger associated with GW170817 by both the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo and the network of electromagnetic-spectrum observing facilities around the world has made the multi-messenger detection of gravitational-wave (GW) events a reality. These joint detections allow us to probe GW sources in greater detail and provide us with the possibility of confidently establishing events that would not have been detected in GW data alone. In this Letter, we explore the prospects of using the electromagnetic (EM) follow-up of low-significance GW event candidates to increase the sample of confident detections with EM counterparts. We find that the GW-alert threshold change that would roughly double the number of detectable astrophysical events would increase the false-alarm rate (FAR) by more than five orders of magnitude from 1 per 100 years to more than 1000 per year. We find that the localization costs of following up low-significance candidates are marginal, as the same changes to FAR only increase distance/area localizations by less than a factor of 2 and increase volume localization by less than a factor of 4. We argue that EM follow-up thresholds for low-significance candidates should be set on the basis of alert purity (P_(astro)) and not FAR. Ideally, such estimates of P_(astro) would be provided by LIGO-Virgo, but in their absence we provide estimates of the average purity of the GW candidate alerts issued by LIGO-Virgo as a function of FAR for various LIGO-Virgo observing epochs

Noise Reduction in Gravitational-wave Data via Deep Learning

With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by $\sim 21.6\%$ and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors.Comment: 12 pages, 7 figure

Searching for Gravitational-Wave Counterparts using the Transiting Exoplanet Survey Satellite

In 2017, the LIGO and Virgo gravitational wave (GW) detectors, in conjunction with electromagnetic (EM) astronomers, observed the first GW multi-messenger astrophysical event, the binary neutron star (BNS) merger GW170817. This marked the beginning of a new era in multi-messenger astrophysics. To discover further GW multi-messenger events, we explore the synergies between the Transiting Exoplanet Survey Satellite (TESS) and GW observations triggered by the LIGO-Virgo-KAGRA Collaboration (LVK) detector network. TESS's extremely wide field of view of ~2300 deg^2 means that it could overlap with large swaths of GW localizations, which can often span hundreds of deg^2 or more. In this work, we use a recently developed transient detection pipeline to search TESS data collected during the LVK's third observing run, O3, for any EM counterparts. We find no obvious counterparts brighter than about 17th magnitude in the TESS bandpass. Additionally, we present end-to-end simulations of BNS mergers, including their detection in GWs and simulations of light curves, to identify TESS's kilonova discovery potential for the LVK's next observing run (O4). In the most optimistic case, TESS will observe up to one GW-found BNS merger counterpart per year. However, TESS may also find up to five kilonovae which did not trigger the LVK network, emphasizing that EM-triggered GW searches may play a key role in future kilonova detections. We also discuss how TESS can help place limits on EM emission from binary black hole mergers, and rapidly exclude large sky areas for poorly localized GW events.Comment: 16 pages, 7 figures, 2 tables. Submitted to AAS Journal