Compact Binary Coalescence Searches Research Articles

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

Because of the electromagnetic (EM) radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave (GW) signal with EM telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-nearest neighbors and random forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist EM telescopes in deciding whether to follow up on GW events in real time.

Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

Search for Subsolar-Mass Binaries in the First Half of Advanced LIGO's and Advanced Virgo's Third Observing Run.

We report on a search for compact binary coalescences where at least one binary component has a mass between 0.2 M_{⊙} and 1.0 M_{⊙} in Advanced LIGO and Advanced Virgo data collected between 1 April 2019 1500 UTC and 1 October 2019 1500 UTC. We extend our previous analyses in two main ways: we include data from the Virgo detector and we allow for more unequal mass systems, with mass ratio q≥0.1. We do not report any gravitational-wave candidates. The most significant trigger has a false alarm rate of 0.14 yr^{-1}. This implies an upper limit on the merger rate of subsolar binaries in the range [220-24200] Gpc^{-3} yr^{-1}, depending on the chirp mass of the binary. We use this upper limit to derive astrophysical constraints on two phenomenological models that could produce subsolar-mass compact objects. One is an isotropic distribution of equal-mass primordial black holes. Using this model, we find that the fraction of dark matter in primordial black holes in the mass range 0.2 M_{⊙}<m_{PBH}<1.0 M_{⊙} is f_{PBH}≡Ω_{PBH}/Ω_{DM}≲6%. This improves existing constraints on primordial black hole abundance by a factor of ∼3. The other is a dissipative dark matter model, in which fermionic dark matter can collapse and form black holes. The upper limit on the fraction of dark matter black holes depends on the minimum mass of the black holes that can be formed: the most constraining result is obtained at M_{min}=1 M_{⊙}, where f_{DBH}≡Ω_{DBH}/Ω_{DM}≲0.003%. These are the first constraints placed on dissipative dark models by subsolar-mass analyses.

Hierarchical approach to matched filtering using a reduced basis

Searching for gravitational waves from compact binary coalescence (CBC) is performed by matched filtering the observed strain data from gravitational-wave observatories against a discrete set of waveform templates designed to accurately approximate the expected gravitational-wave signal, and are chosen to efficiently cover a target search region. The computational cost of matched filtering scales with both the number of templates required to cover a parameter space and the in-band duration of the waveform. Both of these factors increase in difficulty as the current observatories improve in sensitivity, especially at low frequencies, and may pose challenges for third-generation observatories. Reducing the cost of matched filtering would make searches of future detector data more tractable. In addition, it would be easier to conduct searches that incorporate the effects of eccentricity, precession or target light sources (e.g. subsolar). We present a hierarchical scheme based on a reduced basis method to decrease the computational cost of conducting a matched-filter based search. Compared to the current methods, we estimate without any loss in sensitivity, a speedup by a factor of ~ 10 for sources with signal-to-noise ratio (SNR) of at least =6.0, and a factor of ~ 6 for SNR of at least 5. Our method is dominated by linear operations which are highly parallelizable. Therefore, we implement our algorithm using graphical processing units (GPUs) and evaluate commercially motivated metrics to demonstrate the efficiency of GPUs in CBC searches. Our scheme can be extended to generic CBC searches and allows for efficient matched filtering using GPUs.

Hierarchical search for compact binary coalescences in the Advanced LIGO’s first two observing runs

Detection of many compact binary coalescences (CBCs) is one of the primary goals of the present and future ground-based gravitational-wave (GW) detectors. While increasing the detectors' sensitivities will be crucial in achieving this, efficient data analysis strategies can play a vital role. With given computational power in hand, efficient data analysis techniques can expand the size and dimensionality of the parameter space to search for a variety of GW sources. Matched filtering based analyses that depend on modeled signals to produce adequate signal-to-noise ratios for signal detection may miss them if the parameter space is too restrained. Specifically, the CBC search is currently limited to non-precessing binaries only, where the spins of the components are either aligned or anti-aligned to the orbital angular momentum. A hierarchical search for CBCs is thus well motivated. The first stage of this search is performed by matched filtering coarsely sampled data with a coarse template bank to look for candidate events. These candidates are then followed up for a finer search around the vicinity of an event's parameter space. Performing such a search leads to enormous savings in computational cost. Here we report the first successful implementation of the hierarchical search as a PyCBC-based production pipeline to perform a complete analysis of LIGO observing runs. With this, we analyze Advanced LIGO's first and second observing run data. We recover all the events detected by the PyCBC (flat) search in the first GW catalog, GWTC-1, published by the LIGO-Virgo collaboration, with nearly the same significance using a scaled background. In the analysis, we get an impressive factor of 20 reduction in computation compared to the flat search. With a standard injection study, we show that the sensitivity of the hierarchical search remains comparable to the flat search within the error bars.

Dynamic normalization for compact binary coalescence searches in non-stationary noise

The output of gravitational-wave interferometers, such as LIGO and Virgo, can be highly non-stationary. Broadband detector noise can affect the detector sensitivity on the order of tens of seconds. Gravitational-wave transient searches, such as those for colliding black holes, estimate this noise in order to identify gravitational-wave events. During times of non-stationarity we see a higher rate of false events being reported. To accurately separate signal from noise, it is imperative to incorporate the changing detector state into gravitational-wave searches. We develop a new statistic which estimates the variation of the interferometric detector noise. We use this statistic to re-rank candidate events identified during LIGO–Virgo’s second observing run by the PyCBC search pipeline. This results in a 5% improvement in the sensitivity volume for low mass binaries, particularly binary neutron stars mergers.

Multi-detector null-stream-based statistic for compact binary coalescence searches

We develop a new multi-detector signal-based discriminator to improve the sensitivity of searches for gravitational waves from compact binary coalescences. The new statistic is the traditional computed on a null-stream synthesized from the gravitational-wave detector strain time-series of three detectors. This null-stream- statistic can be extended to networks involving more than three detectors as well. The null-stream itself was proposed as a discriminator between correlated unmodeled signals in multiple detectors, such as arising from a common astrophysical source, and uncorrelated noise transients. It can be useful even when the signal model is known, such as for compact binary coalescences. The traditional , on the other hand, is an effective discriminator when the signal model is known and lends itself to the matched-filtering technique. The latter weakens in its effectiveness when a signal lacks enough cycles in band; this can happen for high-mass black hole binaries. The former weakens when there are concurrent noise transients in different detectors in the network or the detector sensitivities are substantially different. Using simulated binary black hole signals, noise transients and strain for Advanced LIGO (in Livingston and Hanford) and Advanced Virgo detectors, we compare the performance of the null-stream- statistic with that of the traditional statistic using receiver-operating characteristics. The new statistic may form the basis for better signal-noise discriminators in multi-detector searches in the future.

Template bank for compact binary coalescence searches in gravitational wave data: A general geometric placement algorithm

We introduce an algorithm for placing template waveforms for the search of compact binary mergers in gravitational wave interferometer data. We exploit the smooth dependence of the amplitude and unwrapped phase of the frequency-domain waveform on the parameters of the binary. We group waveforms with similar amplitude profiles and perform a singular value decomposition of the phase profiles to obtain an orthonormal basis for the phase functions. The leading basis functions span a lower-dimensional linear space in which the unwrapped phase of any physical waveform is well approximated. The optimal template placement is given by a regular grid in the space of linear coefficients. The algorithm is applicable to any frequency-domain waveform model and detector sensitivity curve. It is computationally efficient and requires little tuning. Applying this method, we construct a set of template banks suitable for the search of aligned-spin binary neutron star, neutron-star--black-hole and binary black hole mergers in LIGO--Virgo data.

A Fermi Gamma-Ray Burst Monitor Search for Electromagnetic Signals Coincident with Gravitational-wave Candidates in Advanced LIGO's First Observing Run

Abstract We present a search for prompt gamma-ray counterparts to compact binary coalescence gravitational wave (GW) candidates from Advanced LIGO’s first observing run (O1). As demonstrated by the multimessenger observations of GW170817/GRB 170817A, electromagnetic and GW observations provide complementary information about the astrophysical source, and in the case of weaker candidates, may strengthen the case for an astrophysical origin. Here we investigate low-significance GW candidates from the O1 compact binary coalescence searches using the Fermi Gamma-Ray Burst Monitor (GBM), leveraging its all sky and broad energy coverage. Candidates are ranked and compared to background to measure the significance. Those with false alarm rates (FARs) of less than 10−5 Hz (about one per day, yielding a total of 81 candidates) are used as the search sample for gamma-ray follow-up. No GW candidates were found to be coincident with gamma-ray transients independently identified by blind searches of the GBM data. In addition, GW candidate event times were followed up by a separate targeted search of GBM data. Among the resulting GBM events, the two with the lowest FARs were the gamma-ray transient GW150914-GBM presented in Connaughton et al. and a solar flare in chance coincidence with a GW candidate.

Effects of data quality vetoes on a search for compact binary coalescences in Advanced LIGO’s first observing run

The first observing run of Advanced LIGO spanned 4 months, from 12 September 2015 to 19 January 2016, during which gravitational waves were directly detected from two binary black hole systems, namely GW150914 and GW151226. Confident detection of gravitational waves requires an understanding of instrumental transients and artifacts that can reduce the sensitivity of a search. Studies of the quality of the detector data yield insights into the cause of instrumental artifacts and data quality vetoes specific to a search are produced to mitigate the effects of problematic data. In this paper, the systematic removal of noisy data from analysis time is shown to improve the sensitivity of searches for compact binary coalescences. The output of the PyCBC pipeline, which is a python-based code package used to search for gravitational wave signals from compact binary coalescences, is used as a metric for improvement. GW150914 was a loud enough signal that removing noisy data did not improve its significance. However, the removal of data with excess noise decreased the false alarm rate of GW151226 by more than two orders of magnitude, from 1 in 770 yr to less than 1 in 186 000 yr.

Targeted coherent search for gravitational waves from compact binary coalescences

We present the details of a method for conducting a targeted, coherent search for compact binary coalescences. The search is tailored to be used as a followup to electromagnetic transients such as Gamma Ray Bursts. We derive the coherent search statistic for Gaussian detector noise and discuss the benefits of a coherent, multi-detector search over coincidence methods. To mitigate the effects of non-stationary data, we introduce a number of signal consistency tests, including the null SNR, amplitude consistency and several $\chi^{2}$ tests. We demonstrate the search performance on Gaussian noise and on data from LIGO's fourth science run and verify that the signal consistency tests are capable of removing the majority of noise transients and the search gives an efficiency comparable to that achieved in Gaussian noise.