Search

Gravitational waves from inspiralling neutron stars carry information about matter at extreme gravity and density. The binary neutron star (BNS) event GW170817 provided, for the first time, insight into dense matter through this window. Since then, another BNS (GW190425) and several neutron star-black hole events have been detected, although the tidal measurements were not expected to be well-constrained from them. Collective information regarding the behavior of nuclear matter at extreme densities can be done by performing a joint population inference for the masses, spins, and equation-of-state [1] to enable better understanding. This population inference, in turn, relies on accurate estimates of intrinsic parameters of individual events. In this study, we investigate how the differences in parameter inference of BNS events using different waveform models can affect the eventual inference of the nuclear equation-of-state. We use the state-of-the-art model TEOBResumS with IMRPhenomD NRTidalv2 as a comparison model., Comment: 13 pages, 10 figures

Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily misaligned spins in LIGO and Virgo data taken during the third observing run. Our target systems show strong imprints of precession whereas current searches have non-optimal sensitivity in detecting them. After measuring the sensitivity improvement brought by our search over standard spin-aligned searches, we report the detection of 30 gravitational wave events already discovered in the latest version of the Gravitational Wave Transient Catalog. However, we do not find any additional significant gravitational wave candidates. Our results allow us to place an upper limit of $R_{90\%} = 0.28^{+0.33}_{-0.04}\;\; \mathrm{Gpc^{-3}yr^{-1}}$ on the merger rate of a hypothetical subpopulation of asymmetric, heavily precessing signals, not identified by other searches. Since our upper limit is consistent with the latest rate estimates from the LIGO-Virgo-KAGRA collaboration, our findings rule out the existence of a yet-to-be-discovered population of precessing binaries.

The wide range of sub- and super-nuclear densities achieved in neutron stars makes them ideal probes of dense nuclear behavior in the form of the nuclear equation of state (EoS). Studying neutron stars both in isolation and in highly dynamic events, many recent observations, most famously the gravitational wave and electromagnetic signals associated with the BNS merger GW170817/AT2017gfo, have provided suggestive insight into these highest nuclear densities. Measurements of galactic neutron star masses and radii from NICER and other radio and X-ray measurements provide critical complementary perspectives, bounding other features of the EoS. Though nominally congruent, in this paper we highlight many underappreciated "hidden" priors embedded in joint analysis of these many messengers, and their systematic impact on joint EoS inference. In this work, we perform a careful step-by-step Bayesian inference using a simple low-dimensional parametric EoS model, incrementally adding information from galactic pulsars, gravitational wave sources, and more speculative constraints involving kilonovae and the neutron star maximum mass. At each stage, we carefully discuss the marginal likelihood, explaining how hidden priors impact conclusions. Conversely, we also quantify how much information these measurements have, arguing that many if not most have minimal impact relative to the explicit and hidden prior assumptions. Specifically, we find two features dominate our inference: on the one hand, the choice of EoS parameterization and particularly hyperprior, and on the other the astrophysical priors associated with interpreting events, particularly the multimessenger source GW170817. In an appendix, we outline a simple semianalytic projection suitable for assessing the measurability of the EoS with ensembles of present and future detections., Comment: 26 pages, 19 figures

Precession in Binary Black Holes (BBH) is caused by the failure of the Black Hole spins to be aligned and its study can open up new perspectives in gravitational waves (GW) astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly non-trivial task, as standard matched filtering pipelines for GW searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio $q\lesssim 6$, a volume increase of up to 100\% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the $\xi^2$ signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections.

We develop a robust and self-consistent framework to extract and classify gravitational wave candidates from noisy data, for the purpose of assisting in real-time multi-messenger follow-ups during LIGO-Virgo-KAGRA's fourth observing run~(O4). Our formalism implements several improvements to the low latency calculation of the probability of astrophysical origin~(\PASTRO{}), so as to correctly account for various factors such as the sensitivity change between observing runs, and the deviation of the recovered template waveform from the true gravitational wave signal that can strongly bias said calculation. We demonstrate the high accuracy with which our new formalism recovers and classifies gravitational wave triggers, by analyzing replay data from previous observing runs injected with simulated sources of different categories. We show that these improvements enable the correct identification of the majority of simulated sources, many of which would have otherwise been misclassified. We carry out the aforementioned analysis by implementing our formalism through the \GSTLAL{} search pipeline even though it can be used in conjunction with potentially any matched filtering pipeline. Armed with robust and self-consistent \PASTRO{} values, the \GSTLAL{} pipeline can be expected to provide accurate source classification information for assisting in multi-messenger follow-up observations to gravitational wave alerts sent out during O4., Comment: v2 upload was accidental. revert back to v1

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to optimize the detection algorithm of low-latency search pipelines, increasing their sensitivities to gravitational waves from compact binary coalescences. In this work, we discuss several new features developed for ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of: the signal contamination removal, the bank-$\xi^2$ incorporation, the upgraded $\rho-\xi^2$ signal model and the integration of KAGRA. An injection study demonstrates that these new features improve the pipeline's sensitivity by approximately 15% to 20%, paving the way to further multi-messenger observations during the upcoming observing run., Comment: 13pages, 6figures

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation., Comment: 19 pages, 21 figures

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, {\fontfamily{qcr}\selectfont manifold}. The template bank contains $1.8 \times 10^6$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400$ $M_\odot$ with component masses between $1$-$200$ $M_\odot$ and mass ratios between $1$ and $20$ under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, {\fontfamily{qcr}\selectfont manifold}, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The {\fontfamily{qcr}\selectfont GstLAL} search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly $5$ times as compared to previously reported SVD sorting schemes.

We demonstrate a new geometric method for fast template placement for searches for gravitational waves from the inspiral, merger and ringdown of compact binaries. The method is based on a binary tree decomposition of the template bank parameter space into non-overlapping hypercubes. We use a numerical approximation of the signal overlap metric at the center of each hypercube to estimate the number of templates required to cover the hypercube and determine whether to further split the hypercube. As long as the expected number of templates in a given cube is greater than a given threshold, we split the cube along its longest edge according to the metric. When the expected number of templates in a given hypercube drops below this threshold, the splitting stops and a template is placed at the center of the hypercube. Using this method, we generate aligned-spin template banks covering the mass range suitable for a search of Advanced LIGO data. The aligned-spin bank required ~24 CPU-hours and produced 2 million templates. In general, we find that other methods, namely stochastic placement, produces a more strictly bounded loss in match between waveforms, with the same minimal match between waveforms requiring about twice as many templates with our proposed algorithm. Though we note that the average match is higher, which would lead to a higher detection efficiency. Our primary motivation is not to strictly minimize the number of templates with this algorithm, but rather to produce a bank with useful geometric properties in the physical parameter space coordinates. Such properties are useful for population modeling and parameter estimation.

The response of the Advanced LIGO interferometers is known to vary with time [arXiv:1608.05134]. Accurate calibration of the interferometers must therefore track and compensate for temporal variations in calibration model parameters. These variations were tracked during the first three Advanced LIGO observing runs, and compensation for some of them has been implemented in the calibration procedure. During the second observing run, multiplicative corrections to the interferometer response were applied while producing calibrated strain data both in real-time and in high-latency. In a high-latency calibration produced after the second observing run and during the entirety of the third observing run, a correction involving updating filters was applied to the calibration -- the time dependence of the coupled cavity pole frequency $f_{\rm cc}$. This paper describes the methods developed to compensate for variations in the interferometer response requiring time-dependent filters, including variable zeros, poles, gains, and time delays. The described methods were used to provide compensation for well-modeled time dependence of the interferometer response, which has helped to reduce systematic errors in the calibration to $<2$% in magnitude and $<2^{\circ}$ in phase across LIGO's most sensitive frequency band of 20 - 2000 Hz [arXiv:2005.02531, arXiv:2107.00129]. Additionally, this paper shows how such compensation is relevant for astrophysical inference studies by reducing uncertainty and bias in the sky localization for a simulated binary neutron star merger., Comment: 17 pages, 13 figures, 3 tables

We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC - Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity., Comment: 12 pages, 6 figures

We present updates to GstLAL, a matched filter gravitational-wave search pipeline, in Advanced LIGO and Virgo's third observing run. We discuss the incorporation of statistical data quality information into GstLAL's multi-dimensional likelihood ratio ranking statistic and additional improvements to search for gravitational wave candidates found in only one detector. Statistical data quality information is provided by iDQ, a data quality pipeline that infers the presence of short-duration transient noise in gravitational-wave data using the interferometer's auxiliary state, which has operated in near real-time since before LIGO's first observing run in 2015. We look at the performance and impact on noise rejection by the inclusion of iDQ information in GstLAL's ranking statistic, and discuss GstLAL results in the GWTC-2 catalog, focusing on two case studies; GW190424A, a single-detector gravitational-wave event found by GstLAL and a period of time in Livingston impacted by a thunderstorm., Comment: 8 pages, 5 figures

Observations of the properties of multiple coalescing neutron stars will simultaneously provide insight into neutron star mass and spin distribution, the neutron star merger rate, and the nuclear equation of state. Not all merging binaries containing neutron stars are expected to be identical. Plausible sources of diversity in these coalescing binaries can arise from a broad or multi-peaked NS mass distribution; the effect of different and extreme NS natal spins; the possibility of NS-BH mergers; or even the possibility of phase transitions, allowing for NS with similar mass but strongly divergent radius. In this work, we provide a concrete algorithm to combine all information obtained from GW measurements into a joint constraint on the NS merger rate, the distribution of NS properties, and the nuclear equation of state. Using a concrete example, we show how biased mass distribution inferences can significantly impact the recovered equation of state, even in the small-$N$ limit. With the same concrete example, we show how small-$N$ observations could identify a bimodal mass and spin distribution for merging NS simultaneously with the EOS. Our concordance approach can be immediately generalized to incorporate other observational constraints.

The detection of GW170817, the first neutron star-neutron star merger observed by Advanced LIGO and Virgo, and its following analyses represent the first contributions of gravitational wave (GW) data to understanding dense matter. Parameterizing the high density section of the equation of state (EOS) of both neutron stars through spectral decomposition, and imposing a lower limit on the maximum mass value, led to an estimate of the stars' radii of $R_1 = 11.9_{- 1.4}^{+ 1.4}$ km and $R_2 = 11.9_{- 1.4}^{+ 1.4}$ km. These values do not, however, take into account any uncertainty owed to the choice of the crust low-density EOS, which was fixed to reproduce the SLy EOS model. We here re-analyze GW170817 data and establish that different crust models do not strongly impact the mass or tidal deformability of a neutron star: it is impossible to distinguish between low-density models with GW analysis. However, the crust does have an effect on the inferred radius. We predict the systematic error due to this effect using neutron star structure equations, and compare the prediction to results from full parameter estimation runs. For GW170817, this systematic error affects the radius estimate by 0.3 km, approximately $3\%$ of the NS radii., Comment: 11 pages, 6 figures

After their successful first observing run (September 12, 2015 - January 12, 2016), the Advanced LIGO detectors were upgraded to increase their sensitivity for the second observing run (November 30, 2016 - August 26, 2017). The Advanced Virgo detector joined the second observing run on August 1, 2017. We discuss the updates that happened during this period in the GstLAL-based inspiral pipeline, which is used to detect gravitational waves from the coalescence of compact binaries both in low latency and an offline configuration. These updates include deployment of a zero-latency whitening filter to reduce the over-all latency of the pipeline by up to 32 seconds, incorporation of the Virgo data stream in the analysis, introduction of a single-detector search to analyze data from the periods when only one of the detectors is running, addition of new parameters to the likelihood ratio ranking statistic, increase in the parameter space of the search, and introduction of a template mass-dependent glitch-excision thresholding method., Comment: 12 pages, 7 figures, to be submitted to Phys. Rev. D, comments welcome

Gravitational waves searches for compact binary mergers with LIGO and Virgo are presently a two stage process. First, a gravitational wave signal is identified. Then, an exhaustive search over possible signal parameters is performed. It is critical that the identification stage is efficient in order to maximize the number of gravitational wave sources that are identified. Initial identification of gravitational wave signals with LIGO and Virgo happens in real-time which requires that less than one second of computational time must be used for each one second of gravitational wave data collected. In contrast, subsequent parameter estimation may require hundreds of hours of computational time to analyze the same one second of gravitational wave data. The real-time identification requirement necessitates efficient and often approximate methods for signal analysis. We describe one piece of real-time gravitational-wave identification: an efficient method for ascertaining a signal's consistency between multiple gravitational wave detectors suitable for real-time gravitational wave searches for compact binary mergers. This technique was used in analyses of Advanced LIGO's second observing run and Advanced Virgo's first observing run.

Observations of gravitational-wave signals from binary neutron-star mergers, like GW170817, can be used to constrain the neutron-star equation of state (EoS). One method involves modeling the EoS and measuring the model parameters through Bayesian inference. A previous study has demonstrated the effectiveness of using a phenomenologically parameterized piecewise polytrope to extract constraining information from a simulated population of binary neutron-star mergers. Despite its advantages compared to more traditional methods of measuring the tidal deformability of neutron stars, notable deficiencies arise when using this EoS model. In this work, we describe in detail the implementation of a model built from a spectral decomposition of the adiabatic index that was used by the LIGO-Virgo Collaboration in to constrain the neutron star EoS from GW170817. We demonstrate its overall consistency in recovering the neutron star EoS from a simulated signal to the piecewise polytropic implementation used in and explain any differences that arise. We find that both models recover consistent tidal information from the simulate signals with tightest constraints on the EoS around twice nuclear saturation density. As expected, the statistical error that arises in the piecewise polytropic representation near the fixed joining densities is greatly reduced by using the spectral model. In addition, we find that our choice of prior can have a dominant effect on EoS constraints

Gravitational-wave observations of inspiralling binary neutron star systems can be used to measure the neutron-star equation of state (EOS) through the tidally induced shift in the waveform phase that depends on the tidal deformability parameter $\lambda$. Previous work has shown that $\lambda$, a function of the neutron-star EOS and mass, is measurable by Advanced LIGO for a single event when including tidal information up to the merger frequency. In this work, we describe a method for stacking measurements of $\lambda$ from multiple inspiral events to measure the EOS. We use Markov chain Monte Carlo simulations to estimate the parameters of a 4-parameter piecewise polytrope EOS that matches theoretical EOS models to a few percent. We find that, for "realistic" event rates ($\sim 40$ binary neutron star inspiral events per year with signal-to-noise ratio $> 8$ in a single Advanced LIGO detector), combining a year of gravitational-wave data from a three-detector network with the constraints from causality and recent high mass neutron-star measurements, the EOS above nuclear density can be measured to better than a factor of two in pressure in most cases. We also find that in the mass range $1M_\odot$--$2M_\odot$, the neutron-star radius can be measured to better than $\pm 1$ km and the tidal deformability can be measured to better than $\pm 1 \times 10^{36}$ g cm$^2$ s$^2$ (10%--50% depending on the EOS and mass). The overwhelming majority of this information comes from the loudest $\sim 5$ events. Current uncertainties in the post-Newtonian waveform model, however, lead to systematic errors in the EOS measurement that are as large as the statistical errors, and more accurate waveform models are needed to minimize this error., Comment: 21 pages, 12 figures, submitted to Phys. Rev. D

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star black hole binary and a binary black hole, where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.

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., Comment: 14 pages, submitted to Phys. Rev. D

We consider a simulated population of isolated Galactic neutron stars. The rotational frequency of each neutron star evolves through a combination of electromagnetic and gravitational wave emission. The magnetic field strength dictates the dipolar emission, and the ellipticity (a measure of a neutron star's deformation) dictates the gravitational wave emission. Through both analytic and numerical means, we assess the detectability of the Galactic neutron star population and bound the magnetic field strength and ellipticity parameter space of Galactic neutron stars with or without a direct gravitational wave detection. While our simulated population is primitive, this work establishes a framework by which future efforts can be conducted., Comment: Accepted for publication by Physical Review D, 8 pages, 5 figures