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The article discusses use of the Interferometer Gravitational-wave Observatory (LIGO) and the Laser Interferometer Space Antenna (LISA) to detect gravitational waves and electromagnetic waves. An overview of the origin of gravitational waves, including from colliding black holes and the collision of neutron stars, is provided.

Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mismodeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multicomponent, variable dimension, parametrized model to describe the Gaussian-noise power spectrum for data from ground-based gravitational wave interferometers. Called BayesLine, the algorithm models the noise power spectral density using cubic splines for smoothly varying broadband noise and Lorentzians for narrow-band line features in the spectrum. We describe the algorithm and demonstrate its performance on data from the fifth and sixth LIGO science runs. Once fully integrated into LIGO/Virgo data analysis software, BayesLine will produce accurate spectral estimation and provide a means for marginalizing inferences drawn from the data over all plausible noise spectra. [ABSTRACT FROM AUTHOR]

The study of compact binary inspirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational waveforms them-selves, here we will begin to improve our model of the instrument noise by introducing parameters that allow us to determine the background instrumental power spectrum while simultaneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in the resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level. [ABSTRACT FROM AUTHOR]

Parameter estimation of binary black-hole merger events in gravitational-wave data relies on matched-filtering techniques which, in tum, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing nonspinning numerical-relativity waveforms. We quantify the systematic bias by using a Markov chain Monte Carlo algorithm to sample the posterior distribution function of noise-free data, and compare the offset of the maximum a priori waveform parameters (the bias) to the width of the distribution, which we refer to as the statistical error. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios. These biases grow to be comparable to the statistical errors at high ground-based-instrument signal-to-noise ratios (SNR ~ 50), but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors, but for astrophysical black hole mass estimates the absolute biases (of at most a few percent) are still fairly small. [ABSTRACT FROM AUTHOR]

The Galaxy is suspected to contain hundreds of millions of binary white dwarf systems, a large fraction of which will have sufficiently small orbital periods to emit gravitational radiation in band for space-based gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA). LISA's main science goal is the detection of cosmological events (supermassive black hole mergers, etc.) however the gravitational signal from the galaxy will be the dominant contribution to the data--including instrumental noise--over approximately two decades in frequency. The catalog of detectable binary systems will serve as an unparalleled means of studying the Galaxy. Furthermore, to maximize the scientific return from the mission, the data must be cleansed of the galactic foreground. We will present an algorithm that can accurately resolve and subtract ≥ 10 000 of these sources from simulated data supplied by the Mock LISA Data Challenge Task Force. Using the time evolution of the gravitational wave frequency, we will reconstruct the position of the recovered binaries and show how LISA will sample the entire compact binary population in the Galaxy. [ABSTRACT FROM AUTHOR]

Laser frequency noise (LFN) is the dominant source of noise expected in the Laser Interferometer Space Antenna (LISA) mission, at approximately 7 orders of magnitude greater than the typical signal expected from gravitational waves (GWs). Time-delay interferometry (TDI) suppresses LFN to an acceptable level by linearly combining measurements from individual spacecraft delayed by durations that correspond to their relative separations. Knowledge of the delay durations is crucial for TDI effectiveness. The work reported here extends upon previous studies using data-driven methods for inferring the delays during the postprocessing of raw phase meter data, also known as TDI ranging (TDIR). Our TDIR analysis uses Bayesian methods designed to ultimately be included in the LISA data model as part of a "Global Fit" analysis pipeline. Including TDIR as part of the Global Fit produces GW inferences which are marginalized over uncertainty in the spacecraft separations and allows for independent estimation of the spacecraft orbits. We demonstrate Markov chain Monte Carlo (MCMC) inferences of the six time-independent delays required in the rigidly rotating approximation of the spacecraft configuration (TDI 1.5) using simulated data. The MCMC uses fractional delay interpolation to digitally delay the raw phase meter data, and we study the sensitivity of the analysis to the filter length. Varying levels of complexity in the noise covariance matrix are also examined. Delay estimations are found to result in LFN suppression well below the level of secondary noises and constraints on the arm lengths to O(30) cm over the approximately 2.5 × 109 m baseline. [ABSTRACT FROM AUTHOR]

We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and nonstationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross-checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler. [ABSTRACT FROM AUTHOR]

Galactic ultracompact binaries are expected to be the dominant source of gravitational waves in the milli-Hertz frequency band. Of the tens of millions of Galactic binaries with periods shorter than an hour, it is estimated that a few tens of thousand will be resolved by the future Laser Interferometer Space Antenna (LISA). The unresolved remainder will be the main source of "noise" between 1 and 3 mHz. Typical Galactic binaries are millions of years from merger, and consequently their signals will persist for the duration of the LISA mission. Extracting tens of thousands of overlapping Galactic signals and characterizing the unresolved component is a central challenge in LISA data analysis, and a key contribution to arriving at a global solution that simultaneously fits for all signals in the band. Here we present an end-to-end analysis pipeline for Galactic binaries that uses transdimensional Bayesian inference to develop a time-evolving catalog of sources as data arrive from the LISA constellation. [ABSTRACT FROM AUTHOR]

In the mHz gravitational-wave band, galactic ultracompact binaries (UCBs) are continuous sources emitting at near-constant frequency. The signals from many of these galactic binaries will be sufficiently strong to be detectable by the Laser Interferometer Space Antenna (LISA) after ∼Ϭ(1  week) of observing. In addition to their astrophysical value, these UCBs can be used to monitor the data quality of the observatory. This paper demonstrates the capabilities of galactic UCBs to be used as calibration sources for LISA by demanding signal coherence between adjacent week-long data segments separated by a gap in time of a priori unknown duration. A parameter for the gap duration is added to the UCB waveform model and used in a Markov-chain Monte Carlo algorithm simultaneously fitting for the astrophysical source parameters. Results from measurements of several UCBs are combined to produce a joint posterior on the gap duration. The measurement accuracy's dependence on how much is known about the UCBs through prior observing, and seasonal variations due to the LISA orbital motion, is quantified. The duration of data gaps in a two-week segment of data can be constrained to within ∼0.2  s using Ϭ(10) UCBs after one month of observing. The timing accuracy from UCBs improves to ≲0.1 s after 1 year of mission operations. These results are robust to within a factor of ∼2 when taking into account seasonal variations. [ABSTRACT FROM AUTHOR]

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 unmodeled gravitational-wave signals have been hampered by transient noise artifacts, or "glitches," in the detectors. 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 BayesWave's 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. [ABSTRACT FROM AUTHOR]

The study of compact binary inspirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational waveforms themselves, here we will begin to improve our model of the instrument noise by introducing parameters that allow us to determine the background instrumental power spectrum while simultaneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in the resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level. [ABSTRACT FROM AUTHOR]

Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve ~5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%. [ABSTRACT FROM AUTHOR]

Estimating the parameters of gravitational wave signals detected by ground-based detectors requires an understanding of the properties of the detectors' noise. In particular, the most commonly used likelihood function for gravitational wave data analysis assumes that the noise is Gaussian, stationary, and of known frequency-dependent variance. The variance of the colored Gaussian noise is used as a whitening filter on the data before computation of the likelihood function. In practice the noise variance is not known and it evolves over timescales of dozens of seconds to minutes. We study two methods for estimating this whitening filter for ground-based gravitational wave detectors with the goal of performing parameter estimation studies. The first method uses large amounts of data separated from the specific segment we wish to analyze and computes the power spectral density of the noise through the mean-median Welch method. The second method uses the same data segment as the parameter estimation analysis, which potentially includes a gravitational wave signal, and obtains the whitening filter through a fit of the power spectrum of the data in terms of a sum of splines and Lorentzians. We compare these two methods and conclude that the latter is a more effective spectral estimation method as it is quantitatively consistent with the statistics of the data used for gravitational wave parameter estimation while the former is not. We demonstrate the effect of the two methods by finding quantitative differences in the inferences made about the physical properties of simulated gravitational wave sources added to LIGO-Virgo data. [ABSTRACT FROM AUTHOR]

The LIGO-Virgo Collaboration uses a variety of techniques to detect and characterize gravitational waves. One approach is to use templates--models for the signals derived from Einstein's equations. Another approach is to extract the signals directly from the coherent response of the detectors in the LIGO-Virgo network. Both approaches played an important role in the first gravitational wave detections. Here we extend the BayesWave analysis algorithm, which reconstructs gravitational wave signals using a collection of continuous wavelets, to use a generalized wavelet family, known as chirplets, that have time-evolving frequency content. Since generic gravitational wave signals have frequency content that evolves in time, a collection of chirplets provides a more compact representation of the signal, resulting in more accurate waveform reconstructions, especially for low signal-to-noise events, and events that occupy a large time-frequency volume. [ABSTRACT FROM AUTHOR]

Advanced ground-based gravitational-wave detectors are capable of measuring tidal influences in binary neutron-star systems. In this work, we report on the statistical uncertainties in measuring tidal deformability with a full Bayesian parameter estimation implementation. We show how simultaneous measurements of chirp mass and tidal deformability can be used to constrain the neutron-star equation of state. We also study the effects of waveform modeling bias and individual instances of detector noise on these measurements. We notably find that systematic error between post-Newtonian waveform families can significantly bias the estimation of tidal parameters, thus motivating the continued development of waveform models that are more reliable at high frequencies. [ABSTRACT FROM AUTHOR]

Gravitational waves in general relativity contain two polarization degrees of freedom, commonly labeled plus and cross. Besides those two tensor modes, generic theories of gravity predict up to four additional polarization modes: two scalar and two vector. Detection of nontensorial modes in gravitational wave data would constitute a clean signature of physics beyond general relativity. Previous measurements have pointed to the unambiguous presence of tensor modes in gravitational waves, but the presence of additional generic nontensorial modes has not been directly tested. We propose a model-independent analysis capable of detecting and characterizing mixed tensor and nontensor components in transient gravitational wave signals, including those from compact binary coalescences. This infrastructure can constrain the presence of scalar or vector polarization modes on top of the tensor modes predicted by general relativity. Our analysis is morphology-independent (as it does not rely on a waveform templates), phase-coherent, and agnostic about the source sky location. We apply our analysis to data from GW190521 and simulated data and demonstrate that it is capable of placing upper limits on the strength of nontensorial modes when none are present, or characterizing their morphology in the case of a positive detection. Tests of the polarization content of a transient gravitational wave signal hinge on an extended detector network, wherein each detector observes a different linear combination of polarization modes. We therefore anticipate that our analysis will yield precise polarization constraints in the coming years, as the current ground-based detectors LIGO Hanford, LIGO Livingston, and Virgo are joined by KAGRA and LIGO India. [ABSTRACT FROM AUTHOR]

The Laser Interferometer Space Antenna (LISA) mission, scheduled for launch in the early 2030s, is a gravitational wave observatory in space designed to detect sources emitting in the millihertz band. In contrast to the present ground-based detectors, the LISA data are expected to be a signal dominated, with strong and weak gravitational wave signals overlapping in time and in frequency. Astrophysical population models predict a sufficient number of signals in the LISA band to blend together and form an irresolvable foreground noise. In this work, we present a generic method for characterizing the foreground signals originating from a given astrophysical population of coalescing compact binaries. Assuming idealized detector conditions and a perfect data analysis technique capable of identifying and removing the bright sources, we apply an iterative procedure which allows us to predict the different levels of foreground noise. [ABSTRACT FROM AUTHOR]

Transient non-Gaussian noise in gravitational wave detectors, commonly referred to as glitches, pose challenges for detection and inference of the astrophysical properties of detected signals when the two are coincident in time. Current analyses aim toward modeling and subtracting the glitches from the data using a flexible, morphology-independent model in terms of sine-Gaussian wavelets before the signal source properties are inferred using templates for the compact binary signal. We present a new analysis of gravitational wave data that contain both a signal and glitches by simultaneously modeling the compact binary signal in terms of templates and the instrumental glitches using sine-Gaussian wavelets. The model for the glitches is generic and can thus be applied to a wide range of glitch morphologies without any special tuning. The simultaneous modeling of the astrophysical signal with templates allows us to efficiently separate the signal from the glitches, as we demonstrate using simulated signals injected around real O2 glitches in the two LIGO detectors. We show that our new proposed analysis can separate overlapping glitches and signals, estimate the compact binary parameters, and provide ready-to-use glitch-subtracted data for downstream inference analyses. [ABSTRACT FROM AUTHOR]

We present a systematic comparison of the binary black hole BBH signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the ten BBH events in the first gravitational wave transient catalog (GWTC-1). The second set is constructed from a population of BBH systems with total masses and signal strengths in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case, where the presence of a mismodeled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo. [ABSTRACT FROM AUTHOR]

In the coming years gravitational-wave detectors will undergo a series of improvements, with an increase in their detection rate by about an order of magnitude. Routine detections of gravitational-wave signals promote novel astrophysical and fundamental theory studies, while simultaneously leading to an increase in the number of detections temporally overlapping with instrumentally- or environmentally-induced transients in the detectors (glitches), often of unknown origin. Indeed, this was the case for the very first detection by the LIGO and Virgo detectors of a gravitational-wave signal consistent with a binary neutron star coalescence, GW170817. A loud glitch in the LIGO-Livingston detector, about one second before the merger, hampered coincident detection (which was initially achieved solely with LIGO-Hanford data). Moreover, accurate source characterization depends on specific assumptions about the behavior of the detector noise that are rendered invalid by the presence of glitches. In this paper, we present the various techniques employed for the initial mitigation of the glitch to perform source characterization of GW170817 and study advantages and disadvantages of each mitigation method. We show that, despite the presence of instrumental noise transients louder than the one affecting GW170817, we are still able to produce unbiased measurements of the intrinsic parameters from simulated injections with properties similar to GW170817. [ABSTRACT FROM AUTHOR]

We present a robust method to characterize the gravitational wave emission from the remnant of a neutron star coalescence. Our approach makes only minimal assumptions about the morphology of the signal and provides a full posterior probability distribution of the underlying waveform. We apply our method on simulated data from a network of advanced ground-based detectors and demonstrate the gravitational wave signal reconstruction. We study the reconstruction quality for different binary configurations and equations of state for the colliding neutron stars. We show how our method can be used to constrain the yet-uncertain equation of state of neutron star matter. The constraints on the equation of state we derive are complementary to measurements of the tidal deformation of the colliding neutron stars during the late inspiral phase. In the case of nondetection of a post-merger signal following a binary neutron star inspiral, we show that we can place upper limits on the energy emitted. [ABSTRACT FROM AUTHOR]