Gravitational Wave Data Research Articles

Shining light on the hosts of the nano-Hertz gravitational wave sources: a theoretical perspective

ABSTRACT The formation of supermassive black holes (SMBHs) in the Universe and its role in the properties of the galaxies is one of the open questions in astrophysics and cosmology. Though, traditionally, electromagnetic waves have been instrumental in direct measurements of SMBHs, significantly influencing our comprehension of galaxy formation, gravitational waves (GW) bring an independent avenue to detect numerous binary SMBHs in the observable Universe in the nano-Hertz range using the pulsar timing array observation. This brings a new way to understand the connection between the formation of binary SMBHs and galaxy formation if we can connect theoretical models with multimessenger observations namely GW data and galaxy surveys. Along these lines, we present here the first paper on this series based on romulus25 cosmological simulation on the properties of the host galaxies of SMBHs and propose on how this can be used to connect with observations of nano-Hertz GW signal and galaxy surveys. We show that the most dominant contribution to the background will arise from sources with high chirp masses which are likely to reside in low-redshift early-type galaxies with high stellar mass, largely old stellar population, and low star formation rate, and that reside at centres of galaxy groups and manifest evidence of recent mergers. The masses of the sources show a correlation with the halo mass and stellar mass of the host galaxies. This theoretical study will help in understanding the host properties of the GW sources and can help in establishing a connection with observations.

Deep learning forecasts of cosmic acceleration parameters from DECi-hertz Interferometer Gravitational-wave Observatory

Context. Validating the accelerating expansion of the universe is an important aspect in improving our understanding of the evolution of the universe. By constraining the cosmic acceleration parameter XH, we can discriminate between the cosmological constant plus cold dark matter (ΛCDM) model and the Lemaître–Tolman–Bondi (LTB) model. Aims. In this paper, we explore the possibility of constraining the cosmic acceleration parameter with the inspiral gravitational waveform of neutron star binaries (NSBs) in the frequency range of 0.1 Hz–10 Hz, which can be detected by the second-generation space-based gravitational wave detector DECIGO. Methods. We used a convolutional neural network (CNN) and a long short-term memory (LSTM) network combined with a gated recurrent unit (GRU), along with a Fisher information matrix to derive constraints on the cosmic acceleration parameter, XH. Results. We assumed that our networks estimate the cosmic acceleration parameter without biases (the expected value of the estimation is equal to the true value). Under this assumption, based on the simulated gravitational wave data with a time duration of one month, we conclude that CNN can limit the relative error to 15.71%, while LSTM network combined with GRU can limit the relative error to 14.14%. Additionally, using a Fisher information matrix for gravitational wave data with a five-year observation can limit the relative error to 32.94%. Conclusions. Under the assumption of an unbiased estimation, the neural networks can offer a high-precision estimation of the cosmic acceleration parameter at different redshifts. Therefore, DECIGO is expected to provide direct measurements of the acceleration of the universe by observing the chirp signals of coalescing binary neutron stars.

Gravity Spy: lessons learned and a path forward

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

The discovery of the optical counterpart, along with the gravitational waves (GWs) from GW170817, of the first binary neutron star merger has opened up a new era for multimessenger astrophysics. Combining the GW data with the optical counterpart, also known as AT 2017gfo and classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonovae has brought about a revolution in the domain of multimessenger astronomy, there has been only one kilonova from a GW-detected binary neutron star merger event confirmed so far, and this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once the CVAE is trained, the timescale for light-curve generation is of the order of a few milliseconds, which is a speedup of the generation of light curves by 1000 times as compared to the simulation. The mean squared error between the generated and original light curves is typically 0.015 with a maximum of 0.08 for each set of considered physical parameters, while having a maximum of ≈0.6 error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results.

Importance of stable mass transfer and stellar winds for the formation of gravitational wave sources

ABSTRACT The large number of gravitational wave (GW) detections have revealed the properties of the merging black hole binary population, but how such systems are formed is still heavily debated. Understanding the imprint of stellar physics on the observable GW population will shed light on how we can use the GW data, along with other observations, to constrain the poorly understood evolution of massive binaries. We perform a parameter study on the classical isolated binary formation channel with the population synthesis code seba to investigate how sensitive the properties of the coalescing binary black hole population are on the uncertainties related to first phase of mass transfer and stellar winds. We vary five assumptions: (1) and (2) the mass transfer efficiency and the angular momentum loss during the first mass transfer phase, (3) the mass transfer stability criteria for giant donors with radiative envelopes, (4) the effective temperature at which an evolved star develops a deep convective envelope, and (5) the mass-loss rates of stellar winds. We find that current uncertainties related to first phase of mass transfer have a huge impact on the relative importance of different dominant channels, while the observable demographics of GW sources are not significantly affected. Our varied parameters have a complex, interrelated effect on the population properties of GW sources. Therefore, inference of massive binary physics from GW data alone remains extremely challenging, given the large uncertainties in our current models.

Design and implementation of a seismic Newtonian noise cancellation system for the Virgo gravitational-wave detector

Terrestrial gravity perturbations caused by seismic fields produce the so-called Newtonian noise in gravitational-wave detectors, which is predicted to limit their sensitivity in the upcoming observing runs. In the past, this noise was seen as an infrastructural limitation, i.e., something that cannot be overcome without major investments to improve a detector’s infrastructure. However, it is possible to have at least an indirect estimate of this noise by using the data from a large number of seismometers deployed around a detector’s suspended test masses. The noise estimate can be subtracted from the gravitational-wave data, a process called Newtonian noise cancellation (NNC). In this article, we present the design and implementation of the first NNC system at the Virgo detector as part of its AdV+ upgrade. It uses data from 110 vertical geophones deployed inside the Virgo buildings in optimized array configurations. We use a separate tiltmeter channel to test the pipeline in a proof-of-principle. The system has been running with good performance over months.

Deep learning for parameter estimation of supermassive binary black holes with simulated LISA data

Previous studies have demonstrated that deep learning is an effective approach for processing gravitational wave (GW) data obtained from ground-based detectors, as it can enhance the efficiency of data processing and offer great potential for real-time parameter estimation. In this paper, we explore three different deep learning architectures (MCD, TFP, and VI) for inferring the chirp mass (Mc) and the luminosity distance (DL) of supermassive binary black holes (SMBBHs) using simulated data from the Laser Interferometer Space Antenna (LISA). We train the neural networks with the simulated data and evaluate their performance on predicting the parameters (Mc,DL). The results show that more than 97.5% of the true values fall within the 3σ confidence interval of the predicted values with the optimal network. To verify the accuracy of the network on parameter estimation, we also calculate the estimation error of the parameters using the Fisher Information Matrix (FIM) with the same simulated data. By comparing the root-mean-square error (RMSE) between deep learning and FIM, we find that the two methods are comparable, which implies that deep learning can be reliable for GW parameter estimation.

Using Gravitational Waves to Determine if Primordial Black Holes are Sources of Dark Matter

Scientists do not have much information about or evidence of dark matter. Dark matter does not emit electromagnetic radiation and does not interact with normal matter, but some researchers have found evidence of its existence throughout the universe. Scientists have narrowed down to two possible sources of dark matter: Massive Compact Halo Objects (MACHOs) and Weakly Interacting Massive Particles (WIMPs). In our research, we analyzed MACHOs, which are primordial black holes, using gravitational wave data from the LIGO/Virgo collaboration. We hypothesized that MACHOs may be dark matter due to the higher black hole mass formed from binary mergers between primordial black holes, compared to the black hole mass between binary mergers from simulated stellar black holes. Using the data derived from primordial black hole pairs, we used Python to perform Bayesian inference parameter estimation to generate another set of simulated datasets (which are stellar black hole pairs) to compare the final masses of the black hole mergers in the simulated and original gravitational wave datasets. Our results showed that primordial black holes (MACHOs) may not be dark matter, but in the future, based on the research on dark matter, astrophysicists’ can further improve understanding of primordial black holes and their effect in the universe.

Primordial Black Hole Merger Rate in f(R) Gravity

Primordial black holes (PBHs) are known as one of the potential candidates for dark matter. They are expected to have formed due to the direct gravitational collapse of density fluctuations in the early Universe. In this regard, examining the merger rate of PBHs within modified theories of gravity can offer a deeper insight into their abundance. In this work, we delve into the calculation of the merger rate of PBHs within the theoretical framework of f(R) gravity. Our analysis reveals an enhancement in the merger rate of PBHs compared to that obtained from general relativity. Additionally, modulating the field strength f R0 induces shifts in the PBH merger rate, presenting a potential observational signature of modified gravity. We also explore the upper bounds on the abundance of PBHs obtained from f(R) gravity models by comparing the results with gravitational-wave and observational data. The results indicate that in certain regions not excluded by benchmarking data, the parameter space for these upper bounds may be considered reliable.

Machine-learning Love: classifying the equation of state of neutron stars with transformers

The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures long-range global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals from binary neutron star coalescences, built from five distinct, cold equations of state (EOS) of nuclear matter. From the analysis of the mass dependence of the tidal deformability parameter for each EOS class it is shown that the AST model achieves a promising performance in correctly classifying the EOS purely from the gravitational wave signals, especially when the component masses of the binary system are in the range [1,1.5]M ⊙. Furthermore, the generalization ability of the model is investigated by using gravitational-wave signals from a new EOS not used during the training of the model, achieving fairly satisfactory results. Overall, the results, obtained using the simplified setup of noise-free waveforms, show that the AST model, once trained, might allow for the instantaneous inference of the cold nuclear matter EOS directly from the inspiral gravitational-wave signals produced in binary neutron star coalescences

Constrain the time variation of the gravitational constant via the propagation of gravitational waves

The gravitational constant variation means the breakdown of the strong equivalence principle. As the cornerstone of general relativity, the validity of general relativity can be examined by studying the gravitational constant variation. Such variations have the potential to affect both the generation and propagation of gravitational waves. Here, our focus lies on the effect of gravitational constant variation specifically on the propagation of gravitational waves. In a previous paper (An et al., 2023 [13]) we have studied such effect based on a Maxwell-like equation. That work admits two drawbacks. The assumption that describing gravitational waves with Maxwell-like equation is not solid. And more such treatment can only deal with space dependent gravitational constant. In the current paper we will remedy these two issues. We employ two analytical methods, namely based on the Fierz-Pauli action and the perturbation of Einstein-Hilbert action around Minkowski spacetime, both leading to the same gravitational wave equation. By solving this equation, we find the effects of gravitational constant variation on gravitational wave propagation. The result is consistent with previous investigations based on Maxwell-like equations for gravitational waves for space dependent gravitational constant cases. In addition we can constrain the time variation of the gravitational constant via the propagation of gravitational waves based on GW170817 data. And more, we find that small variations in the gravitational constant result in an amplitude correction at the leading order and a phase correction at the sub-leading order for gravitational waves. These results provide valuable insights for probing gravitational constant variation and can be directly applied to gravitational wave data analysis.

Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the ‘chirp’ signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. We show that these gains are achieved at practically zero loss in terms of model performance on real LIGO data before and after GAN training. Through a direct search on days of LIGO data, we recover two significant CBC events from GWTC-2.1 (Abbott et al 2022 2108.01045 [gr-qc]), GW190519_153544 and GW190521_074359. We also report the search sensitivity obtained from an injection study.

Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data

Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data

Constraining extended cosmologies with GW×LSS cross-correlations

The rapid development of gravitational wave astronomy provides the unique opportunity of exploring the dynamics of the Universe using clustering properties of coalescing binary black hole mergers. Gravitational wave data, along with information coming from future galaxy surveys, have the potential of shedding light about many open questions in Cosmology, including those regarding the nature of dark matter and dark energy. In this work we explore which combination of gravitational wave and galaxy survey datasets are able to provide the best constraints both on modified gravity theories and on the nature of the very same binary black hole events. In particular, by using the public Boltzmann code Multi_CLASS, we compare cosmological constraints on popular ΛCDM extensions coming from gravitational waves alone and in conjunction with either deep and localized or wide and shallow galaxy surveys. We show that constraints on extensions of General Relativity will be at the same level of existing limits from gravitational waves alone or one order of magnitude better when galaxy surveys are included. Furthermore, cross-correlating both kind of galaxy survey with gravitational waves datasets will allow to confidently rule in or out primordial black holes as dark matter candidate in the majority of the allowed parameter space.

Nonparametric Inference of the Population of Compact Binaries from Gravitational-wave Observations Using Binned Gaussian Processes

The observation of gravitational waves from multiple compact binary coalescences by the LIGO–Virgo–KAGRA detector networks has enabled us to infer the underlying distribution of compact binaries across a wide range of masses, spins, and redshifts. In light of the new features found in the mass spectrum of binary black holes and the uncertainty regarding binary formation models, nonparametric population inference has become increasingly popular. In this work, we develop a data-driven clustering framework that can identify features in the component mass distribution of compact binaries simultaneously with those in the corresponding redshift distribution, from gravitational-wave data in the presence of significant measurement uncertainties, while making very few assumptions about the functional form of these distributions. Our generalized model is capable of inferring correlations among various population properties, such as the redshift evolution of the shape of the mass distribution itself, in contrast to most existing nonparametric inference schemes. We test our model on simulated data and demonstrate the accuracy with which it can reconstruct the underlying distributions of component masses and redshifts. We also reanalyze public LIGO–Virgo–KAGRA data from events in GWTC-3 using our model and compare our results with those from some alternative parametric and nonparametric population inference approaches. Finally, we investigate the potential presence of correlations between mass and redshift in the population of binary black holes in GWTC-3 (those observed by the LIGO–Virgo–KAGRA detector network in their first three observing runs), without making any assumptions about the specific nature of these correlations.

What constraints can one pose on the maximum mass of neutron stars from multimessenger observations?

ABSTRACT The maximum mass of neutron stars (MTOV) plays a crucial role in understanding their equation of state (EoS). Previous studies have used the measurements for the compactness of massive pulsars and the tidal deformability of neutron stars in binary neutron star (BNS) mergers to constrain the EoS and thus the MTOV. The discovery of the most massive pulsar, PSR J0952−0607, with a mass $\sim 2.35\, {\rm M}_{\odot }$, has provided a valuable lower limit for MTOV. Another efficient method to constrain MTOV is by examining the type of central remnant formed after a BNS merger. Gravitational wave (GW) data can provide the total mass of the system, while accompanying electromagnetic signals can help infer the remnant type. In this study, we combine all the previous constraints and utilize the observational facts that about 24 per cent of the short gamma-ray bursts are followed by an X-ray internal plateau, which indicate that roughly this fraction of BNS mergers yield supermassive neutron stars, to perform (Markov Chain) Monte Carlo simulations. These simulations allow us to explore the probability density distribution of MTOV and other parameters related to BNS mergers. Our findings suggest that MTOV is likely around $2.49\!-\!2.52\, {\rm M}_{\odot }$, with an uncertainty range of approximately [$-0.16$, $0.15\, {\rm M}_{\odot }$] ([$-0.28$, $0.26\, {\rm M}_{\odot }$]) at 1σ (2σ) confidence level. Furthermore, we examine the type of merger remnants in specific events like GW170817 and GW190425 to further constrain MTOV and other relevant parameters, which can help to understand the physical processes involved in BNS mergers.

Forecasts analysis on varying-[formula omitted] theories from gravitational wave standard sirens

Motivated by future gravitational waves observations, we perform forecasts analysis to constrain a possible time variation of the fine structure constant (α) within the context of the so-called runaway dilaton model. For this purpose, some gravitational-wave standard sirens mock data within the perspective of Einstein Telescope and LISA mission were considered jointly with current strong gravitational lensing systems observations. We find that future standard sirens observations can also play an important role in the search for possible variations of α within the methodology presented in this work.

A multimessenger model for neutron star–black hole mergers

ABSTRACT We present a semi-analytic model for predicting kilonova light curves from the mergers of neutron stars with black holes (NSBH). The model is integrated into the mosfit platform, and can generate light curves from input binary properties and nuclear equation-of-state considerations, or incorporate measurements from gravitational wave (GW) detectors to perform multimessenger parameter estimation. The rapid framework enables the generation of NSBH kilonova distributions from binary populations, light curve predictions from GW data, and statistically meaningful comparisons with an equivalent binary neutron star (BNS) model in mosfit. We investigate a sample of kilonova candidates associated with cosmological short gamma-ray bursts, and demonstrate that they are broadly consistent with being driven by NSBH systems, though most have limited data. We also perform fits to the very well sampled GW170817, and show that the inability of an NSBH merger to produce lanthanide-poor ejecta results in a significant underestimate of the early (≲2 d) optical emission. Our model indicates that NSBH-driven kilonovae may peak up to a week after merger at optical wavelengths for some observer angles. This demonstrates the need for early coverage of emergent kilonovae in cases where the GW signal is either ambiguous or absent; they likely cannot be distinguished from BNS mergers by the light curves alone from ∼2 d after the merger. We also discuss the detectability of our model kilonovae with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST).

Deep Multimessenger Search for Compact Binary Mergers in LIGO, Virgo, and Fermi/GBM Data from 2016–2017

GW170817–GRB 170817A provided the first observation of gravitational waves from a neutron star merger with associated transient counterparts across the entire electromagnetic spectrum. This discovery demonstrated the long-hypothesized association between short gamma-ray bursts and neutron star mergers. More joint detections are needed to explore the relation between the parameters inferred from the gravitational wave and the properties of the gamma-ray burst signal. We developed a joint multimessenger analysis of LIGO, Virgo, and Fermi/GBM data designed for detecting weak gravitational-wave transients associated with weak gamma-ray bursts. As such, it does not start from confident (GWTC-1) events only. Instead, we take the full list of existing compact binary coalescence triggers generated with the PyCBC pipeline from the second Gravitational-Wave Observing Run (O2), and reanalyze the entire set of public Fermi/GBM data covering this observing run to generate a corresponding set of gamma-ray burst candidate triggers. We then search for coincidences between the gravitational-wave and gamma-ray burst triggers without requiring a confident detection in any channel. The candidate coincidences are ranked according to a statistic combining each candidate’s strength in gravitational-wave and gamma-ray data, their time proximity, and the overlap of their sky localization. The ranking is then converted to a false alarm rate using time shifts between the gravitational-wave and gamma-ray burst triggers. We present the results using O2 triggers, which allowed us to check the validity of our method against GW170817–GRB 170817A. We also discuss the different configurations tested to maximize the significance of the joint detection.

Sensitive test of non-Gaussianity in gravitational-wave detector data

Sensitive test of non-Gaussianity in gravitational-wave detector data