Gravitational Wave Data Research Articles

Artificial intelligence model for gravitational wave search based on the waveform envelope

In recent years, artificial intelligence technology for gravitational wave data analysis has developed rapidly. In this paper, we put forward a new artificial intelligence model for gravitational wave search. The framework of such a model includes a detection stage and a testing stage. We first use the deep learning technology to extract the envelope information of the gravitational wave candidate and use the coalescence time obtained from the envelope to further confirm the detection results. Within the detection stage, we use wavelet denoising and a special training strategy to improve the performance of the gravitational wave detection model. The lowest false alarm rate of the detection model is about 1.7 per month without the testing stage. When the testing stage is added, the lowest false alarm rate decreases to 0.046 per year. The efficiency of our model is demonstrated by the data obtained from the first, second, and third observing runs of the LIGO-VIRGO-KAGRA collaboration. The search results of confident events on the three observing runs indicate that the efficiency of our deep learning algorithm can achieve 80% of the traditional algorithm based on matched filtering.

Solving the Teukolsky equation with physics-informed neural networks

We use physics-informed neural networks (PINNs) to compute the first quasinormal modes of the Kerr geometry via the Teukolsky equation. This technique allows us to extract the complex frequencies and separation constants of the equation without the need for sophisticated numerical techniques, and with an almost immediate implementation under the pytorch framework. We are able to compute the oscillation frequencies and damping times for arbitrary black hole spins and masses, with accuracy typically below the percentual level as compared to the accepted values in the literature. We find that PINN-computed quasinormal modes are indistinguishable from those obtained through existing methods at signal-to-noise ratios (SNRs) larger than 100, making the former reliable for gravitational-wave data analysis in the mid term, before the arrival of third-generation detectors like LISA or the Einstein Telescope, where SNRs of $\mathcal{O}(1000)$ might be achieved.

Rapid hierarchical inference of neutron star equation of state from multiple gravitational wave observations of binary neutron star coalescences

Bayesian hierarchical inference of phenomenological parametrized neutron star equations of state (EoS) from multiple gravitational wave observations of binary neutron star mergers is of fundamental importance in improving our understanding of neutron star structure, the general properties of matter at supra nuclear densities, and the strong nuclear force. However, such an analysis is computationally costly, as it is unable to reuse single-event EoS agnostic parameter estimation runs that are carried out regardless for generating gravitational wave transient catalogs. With the number of events expected to be observable during the fourth observing run (O4) of LIGO/Virgo/KAGRA, this problem can only be expected to worsen. We develop a novel and robust algorithm for rapid and computationally cheap hierarchical inference of parametrized EoS from gravitational wave data which reuses single-event EoS agnostic parameter estimation samples to significantly reduce computational cost. We efficiently include a priori knowledge of neutron star physics as Bayesian priors on the EoS parameters. The high speed and low computational cost of our method allow for efficient recomputation of EoS inference every time a new binary neutron star event is discovered or whenever new observations and theoretical discoveries change the prior on EoS parameters. We test our method on both real and simulated gravitational wave data to demonstrate its accuracy. We show that our computationally cheap method produces EoS constraints that are completely consistent with existing analysis for real data, the chosen fiducial EoS for simulated data. Armed with our fast analysis scheme, we also study the variability of EoS constraints with binary neutron star properties for sets of simulated events drawn in different signal-to-noise ratio and mass ranges.

Nonlinear Effects in Black Hole Ringdown.

We report evidence for nonlinear modes in the ringdown stage of the gravitational waveform produced by the merger of two comparable-mass black holes. We consider both the coalescence of black hole binaries in quasicircular orbits and high-energy, head-on black hole collisions. The presence of nonlinear modes in the numerical simulations confirms that general-relativistic nonlinearities are important and must be considered in gravitational-wave data analysis.

Black-hole ringdown as a probe of higher-curvature gravity theories

Detecting gravitational waves from coalescing compact binaries allows us to explore the dynamical, nonlinear regime of general relativity and constrain modifications to it. Some of the gravitational-wave events observed by the LIGO-Virgo Collaboration have sufficiently high signal-to-noise ratio in the merger, allowing us to probe the relaxation of the remnant black hole to its final, stationary state---the so-called black-hole ringdown, which is characterized by a set of quasinormal modes. Can we use the ringdown to constrain deviations from general relativity, as predicted by several of its contenders? Here, we address this question by using an inspiral-merger-ringdown waveform model in the effective-one-body formalism, augmented with a parametrization of the ringdown based on an expansion in the final black hole's spin. We give a prescription on how to include in this waveform model, the quasinormal mode frequencies calculated on a theory-by-theory basis. In particular, we focus on theories that modify general relativity by higher-order curvature corrections, namely, Einstein-dilaton-Gauss-Bonnet, dynamical Chern-Simons theories, and cubic- and quartic-order effective-field-theories of general relativity. We use this parametrized waveform model to measure the ringdown properties of the two loudest ringdown signals observed so far, GW150914 and GW200129. We find that while the Einstein-dilaton-Gauss-Bonnet theory cannot be constrained with these events, we can place upper bounds on the fundamental lengthscale of cubic- (${\ensuremath{\ell}}_{\text{cEFT}}\ensuremath{\le}38.2\text{ }\text{ }\mathrm{km}$) and quartic-order (${\ensuremath{\ell}}_{\text{qEFT}}\ensuremath{\le}51.3\text{ }\text{ }\mathrm{km}$) effective-field-theories of general relativity, and of dynamical Chern-Simons gravity (${\ensuremath{\ell}}_{\text{dCS}}\ensuremath{\le}38.7\text{ }\text{ }\mathrm{km}$). The latter result is a concrete example of a theory presently unconstrained by inspiral-only analyses which, however, can be constrained by merger-ringdown studies with current gravitational-wave data.

Gaussian processes for glitch-robust gravitational-wave astronomy

ABSTRACT Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enable detailed analysis of the curved space-time around black holes. With nearly 100 signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as ‘glitches’ which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realization of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realize the full potential of the Gaussian process approach.

The Astrophysical Multimessenger Observatory Network: a Summary

The Astrophysical Multimessenger Observatory Network (AMON) aims to connect the world’s leading high-energy and multimessenger observatories. AMON’s objective are to evoke the discovery of new multimessenger phenomena, exploit these phenomena as tools for fundamental physics and astrophysics, and explore archival datasets in search of multimessenger activity. Present projects include distributing low-latency multimessenger alerts from the Neutrino-Electromagnetic (NuEM) channel, as well as distrubting alerts from individual detectors including IceCube and HAWC. Looking ahead, AMON will continue providing useful real-time analyses of a wide variety of high-energy and multimessenger data streams, including coincidences between gravitational-wave data and gamma-ray data. It will also start using the SCiMMA-standard cyberinfrastructure, and keep strengthening its ties with the theoretical and time domain astrophysics communities.

Relativistic Correction to the r-mode Frequency in Light of Multimessenger Constraints

The r-mode oscillations of rotating neutron stars are promising candidates for continuous gravitational-wave (GW) observations. The r-mode frequencies for slowly rotating Newtonian stars are well known and independent of the equation of state (EOS), but for neutron stars several mechanisms can alter the r-mode frequency for which the relativistic correction is dominant and relevant for most of the neutron stars. The most sensitive searches for continuous GWs are those for known pulsars for which GW frequencies are in targeted narrow frequency bands of a few hertz. In this study, we investigate the effect of several state-of-the-art multimessenger constraints on the r-mode frequency for relativistic, slowly rotating, barotropic stars. Imposing these recent constraints on the EOS, we find that the r-mode frequency range is slightly higher than that from the previous study and the narrowband frequency range can increase by up to 25% for the most promising candidate PSR J0537−6910 depending on the range of compactness. We also derive universal relations between r-mode frequency and dimensionless tidal deformability that can be used to estimate the dynamical tide of the r-mode resonant excitation during the inspiral signal. These results can be used to construct the parameter space for r-mode searches in GW data and also constrain the nuclear EOS following a successful r-mode detection.

Constraints on primordial curvature spectrum from primordial black holes and scalar-induced gravitational waves

The observational data of primordial black holes and scalar-induced gravitational waves can constrain the primordial curvature perturbation at small scales. We parameterize the primordial curvature perturbation by a broken power law form and find that it is consistent with many inflation models that can produce primordial black holes, such as nonminimal derivative coupling inflation, scalar–tensor inflation, Gauss–Bonnet inflation, and K/G inflation. The constraints from primordial black holes on the primordial curvature power spectrum with the broken power law form are obtained, where the fraction of primordial black holes in dark matter is calculated by the peak theory. Both the real-space top-hat and the Gaussian window functions are considered. The constraints on the amplitude of primordial curvature perturbation with Gaussian window function are around three times larger than those with real-space top-hat window function. The constraints on the primordial curvature perturbation from the NANOGrav 12.5 years data sets are displayed, where the NANOGrav signals are assumed as the scalar-induced gravitational waves, and only the first five frequency bins are used.

Generating transient noise artefacts in gravitational-wave detector data with generative adversarial networks

Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this paper, we show how glitches can be simulated using generative adversarial networks (GANs). We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. We show how our GAN-generated images can easily be converted to time series, which would allow us to use GAN-generated glitches in simulations and mock data challenges to improve the robustness of gravitational-wave searches and parameter-estimation algorithms. We perform a neural network classification to show that our artificial glitches are an excellent match for real glitches, with an average classification accuracy across all 22 glitch types of 99.0%.

Identifying glitches near gravitational-wave signals from compact binary coalescences using the Q-transform

We present a computational method to identify glitches in gravitational-wave data that occur nearby gravitational-wave signals from compact binary coalescences. The Q-transform, an established tool in LIGO-Virgo-KAGRA data analysis, computes the probability of any excess in the data surrounding a signal against the assumption of a Gaussian noise background, flagging any significant glitches. Subsequently, we perform validation tests on this computational method to ensure self-consistency in colored Gaussian noise, as well as data that contains a gravitational-wave event after subtracting the signal using the best-fit template. Finally, a comparison of our glitch identification results from real events in LIGO-Virgo’s third observing run against the list of events which required glitch mitigation shows that this tool will be useful in providing precise information about data quality to improve astrophysical analyses of these events.

Eccentricity or spin precession? Distinguishing subdominant effects in gravitational-wave data

ABSTRACT Eccentricity and spin precession are key observables in gravitational-wave astronomy, encoding precious information about the astrophysical formation of compact binaries together with fine details of the relativistic two-body problem. However, the two effects can mimic each other in the emitted signals, raising issues around their distinguishability. Since inferring the existence of both eccentricity and spin precession simultaneously is – at present – not possible, current state-of-the-art analyses assume that either one of the effects may be present in the data. In such a setup, what are the conditions required for a confident identification of either effect? We present simulated parameter inference studies in realistic LIGO/Virgo noise, studying events consistent with either spin precessing or eccentric binary black hole coalescences and recovering under the assumption that either of the two effects may be at play. We quantify how the distinguishability of eccentricity and spin precession increases with the number of visible orbital cycles, confirming that the signal must be sufficiently long for the two effects to be separable. The threshold depends on the injected source, with inclination, eccentricity, and effective spin playing crucial roles. In particular, for injections similar to GW190521, we find that it is impossible to confidently distinguish eccentricity from spin precession.

Probing early Universe supercooled phase transitions with gravitational wave data

We investigate the reach of the LIGO/Virgo/KAGRA detectors in the search for signatures of first-order phase transitions in the early Universe. Utilizing data from the first three observing runs, we derive constraints on the parameters of the underlying gravitational-wave background, focusing on transitions characterized by strong supercooling. As an application of our analysis, we determine bounds on the parameter space of two representative particle physics models. We also comment on the expected reach of third-generation detectors in probing supercooled phase transitions.

An Implementation of Galactic White Dwarf Binary Data Analysis for MLDC-3.1

The space-borne gravitational wave detectors will observe a large population of double white dwarf binaries in the Milky Way. However, the search for double white dwarfs in the gravitational wave data will be time-consuming due to the large number of templates involved and antenna response calculation. In this paper, we implement an iterative combinatorial algorithm to search for double white dwarfs in MLDC-3.1 data. To quickly determine the rough parameters of the target sources, the following algorithms are adopted in a coarse search process: (1) using the downsampling method to reduce the number of original data points; (2) using the undersampling method to speed up the generation of a single waveform template; (3) using the stochastic template bank method to quickly construct the waveform template bank while achieving high coverage of the parameter space; (4) combining the FFT acceleration algorithm with the stochastic template bank to reduce the calculation time of a single template. A fine search process is applied to further determine the parameters of the signals based on the coarse search, for which we adopt the particle swarm optimization. Finally, we detect double white dwarf signals, validating the feasibility of our method.

Adding gamma-ray polarimetry to the multi-messenger era

Context. The last decade has seen the emergence of two new fields within astrophysics: gamma-ray polarimetry and gravitational wave (GW) astronomy. The former, which aims to measure the polarization of gamma rays in the energy range of tens to hundreds of keV, from astrophysical sources, saw the launch of the first dedicated polarimeters such as GAP and POLAR. Due to both a large scientific interest as well as their large signal-to-noise ratios, gamma-ray bursts (GRBs) are the primary source of interest of the first generation of polarimeters. Polarization measurements are theorized to provide a unique probe of the mechanisms at play in these extreme phenomena. On the other hand, GW astronomy started with the detection of the first black hole mergers by LIGO in 2015, followed by the first multi-messenger detection in 2017. Aims. While the potential of the two individual fields has been discussed in detail in the literature, the potential for joint observations has thus far been ignored. In this article, we aim to define how GW observations can best contribute to gamma-ray polarimetry and study the scientific potential of joint analyses. In addition, we aim to provide predictions on feasibility of such joint measurements in the near future. Methods. We study which GW observables can be combined with measurements from gamma-ray polarimetry to improve the discriminating power regarding GRB emission models. We then provide forecasts for the joint detection capabilities of current and future GW detectors and polarimeters. Results. Our results show that by adding GW data to polarimetry, a single precise joint detection would allow for the majority of emission models to be ruled out. We show that in the coming years, joint detections between GW and gamma-ray polarimeters might already be possible. Although these would allow one to constrain part of the model space, the probability of highly constraining joint detections will remain small in the near future. However, the scientific merit held by even a single such measurement makes it important to pursue such an endeavour. Furthermore, we show that using the next generation of GW detectors, such as the Einstein Telescope, joint detections for which GW data can better complement the polarization data become possible.

The Late Afterglow of GW170817/GRB 170817A: A Large Viewing Angle and the Shift of the Hubble Constant to a Value More Consistent with the Local Measurements

The multimessenger data of neutron star merger events are promising for constraining the Hubble constant. So far, GW170817 is still the unique gravitational wave event with multiwavelength electromagnetic counterparts. In particular, its radio and X-ray emission have been measured in the past ∼3–5 yr. In this work, we fit the long-lasting X-ray, optical, and radio afterglow light curves of GW170817/GRB 170817A, including the forward shock radiation from both the decelerating relativistic gamma-ray burst outflow and the subrelativistic kilonova outflow (though whether the second component contributes significantly is still uncertain), and find out a relatively large viewing angle (∼0.5 rad). Such a viewing angle has been taken as a prior in the gravitational wave data analysis, and the degeneracy between the viewing angle and the luminosity distance is broken. Finally, we have a Hubble constant , which is more consistent with that obtained by other local measurements. If rather similar values are inferred from multimessenger data of future neutron star merger events, it will provide critical support to the existence of the Hubble tension.

Simultaneous inference of neutron star equation of state and the Hubble constant with a population of merging neutron stars

We develop a method for implementing a proposal on utilizing knowledge of neutron star (NS) equation of state (EoS) for inferring the Hubble constant from a population of binary neutron star (BNS) mergers. This method is useful in exploiting BNSs as standard sirens when their redshifts are not available. Gravitational wave (GW) signals from compact object binaries provide a direct measurement of their luminosity distances, but not their redshifts. Unlike in the past, here we employ a realistic EoS parametrization in a Bayesian framework to simultaneously measure the Hubble constant and refine the constraints on the EoS parameters. The uncertainty in the redshift depends on the uncertainties in the EoS and the mass parameters estimated from GW data. Combining the inferred BNS redshifts with the corresponding luminosity distances, one constructs a redshift-distance relation and deduces the Hubble constant from it. Here, we show that in the Cosmic Explorer era, one can measure the Hubble constant to a precision of $\lesssim 5\%$ (with a $90\%$ credible interval) with a realistic distribution of a thousand BNSs, while allowing for uncertainties in their EoS parameters. Such a measurement can potentially resolve the current tension in the measurements of the Hubble constant from the early- and late-time universe. The methodology implemented in this work demonstrates a comprehensive prescription for inferring the NS EoS and the Hubble constant by simultaneously combining GW observations from merging NSs, while employing a simple population model for NS masses and keeping the merger rate of NSs constant in redshift. This method can be immediately extended to incorporate merger rate, population properties, and additional cosmological parameters.

From inflation to black hole mergers and back again: Gravitational-wave data-driven constraints on inflationary scenarios with a first-principle model of primordial black holes across the QCD epoch

Recent population studies have searched for a subpopulation of primordial black holes (PBHs) in the gravitational-wave (GW) events so far detected by LIGO/Virgo/KAGRA (LVK), in most cases adopting a phenomenological PBH mass distribution. When deriving such population from first principles in the standard scenario, however, the equation of state of the Universe at the time of PBH formation may strongly affect the PBH abundance and mass distribution, which ultimately depend on the power spectrum of cosmological perturbations. Here we improve on previous population studies on several aspects: (i) we adopt state-of-the-art PBH formation models describing the collapse of cosmological perturbations across the QCD epoch; (ii) we perform the first Bayesian multi-population inference on GW data including PBHs and directly using power spectrum parameters instead of phenomenological distributions; (iii) we critically confront the PBH scenario with LVK phenomenological models describing the GWTC-3 catalog both in the neutron-star and in the BH mass ranges, also considering PBHs as subpopulation of the total events. Our results confirm that LVK observations prevent the majority of the dark matter to be in the form of stellar mass PBHs. We find that the best fit PBH model can comprise a small fraction of the total events, in particular it can naturally explain events in the mass gaps. If the lower mass-gap event GW190814 is interpreted as a PBH binary, we predict that LVK should detect up to a few subsolar mergers and one to $\approx 30$ lower mass gap events during the upcoming O4 and O5 runs. Finally, mapping back the best-fit power spectrum into an ultra slow-roll inflationary scenario, we show that the latter predicts detectable PBH mergers in the LVK band, a stochastic GW background detectable by current and future instruments, and may include the entirety of dark matter in asteroid-mass PBHs.

Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning

ABSTRACT The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of the order of 105–106 and 7 × 104 yr−1, respectively. We explored the efficiency of using convolutional neural networks (CNNs) for the detection of BBHs’ mergers in synthetic noisy data that was generated according to ET’s parameters. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet, and DenseNet. ResNet has significantly better performance, and was able to detect BBHs sources with SNR of 8 or higher with 98.5 per cent accuracy, and with 92.5 per cent, 85 per cent, 60 per cent, and 62 per cent accuracy for sources with SNR range of 7–8, 6–7, 5–6, and 4–5, respectively. ResNet, in qualitative evaluation, was able to detect a BBH’s merger at 60 Gpc with 4.3 SNR. It was also shown that CNN can be used efficiently for near-real time detection of BBHs.

Introduction of machine learning for astronomy (hands-on workshop)

This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to process gravitational wave data.