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Register a new userRanking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers
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In the multi-messenger astronomy era, accurate sky localization and low latency time of gravitational-wave (GW) searches are keys in triggering successful follow-up observations on the electromagnetic counterpart of GW signals. We, in this work, focus on the latency time and study the feasibility of adopting supervised machine learning (ML) method for ranking candidate GW events. We consider two popular ML methods, random forest and neural networks. We observe that the evaluation time of both methods takes tens of milliseconds for $sim$ 45,000 evaluation samples. We compare the classification efficiency between the two ML methods and a conventional low-latency search method with respect to the true positive rate at given false positive rate. The comparison shows that about 10% improved efficiency can be achieved at lower false positive rate $sim 2 times 10^{-5}$ with both ML methods. We also present that the search sensitivity can be enhanced by about 18% at $sim 10^{-11}$Hz false alarm rate. We conclude that adopting ML methods for ranking candidate GW events is a prospective approach to yield low latency and high efficiency in searches for GW signals from compact binary mergers.
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In this paper we present the results of the first low frequency all-sky search of continuous gravitational wave signals conducted on Virgo VSR2 and VSR4 data. The search covered the full sky, a frequency range between 20 Hz and 128 Hz with a range of spin-down between $-1.0 times 10^{-10}$ Hz/s and $+1.5 times 10^{-11}$ Hz/s, and was based on a hierarchical approach. The starting point was a set of short Fast Fourier Transforms (FFT), of length 8192 seconds, built from the calibrated strain data. Aggressive data cleaning, both in the time and frequency domains, has been done in order to remove, as much as possible, the effect of disturbances of instrumental origin. On each dataset a number of candidates has been selected, using the FrequencyHough transform in an incoherent step. Only coincident candidates among VSR2 and VSR4 have been examined in order to strongly reduce the false alarm probability, and the most significant candidates have been selected. Selected candidates have been subject to a follow-up by constructing a new set of longer FFTs followed by a further incoherent analysis, still based on the FrequencyHough transform. No evidence for continuous gravitational wave signals was found, therefore we have set a population-based joint VSR2-VSR4 90$%$ confidence level upper limit on the dimensionless gravitational wave strain in the frequency range between 20 Hz and 128 Hz. This is the first all-sky search for continuous gravitational waves conducted, on data of ground-based interferometric detectors, at frequencies below 50 Hz. We set upper limits in the range between about $10^{-24}$ and $2times 10^{-23}$ at most frequencies. Our upper limits on signal strain show an improvement of up to a factor of $sim$2 with respect to the results of previous all-sky searches at frequencies below $80~mathrm{Hz}$.
In light of the recent dazzling discovery of GW170817, we discuss several new scientific opportunities that would emerge in multi-messenger time-domain astrophysics if a facility like the next generation Very Large Array (ngVLA) were to work in tandem with ground-based gravitational wave (GW) detectors. These opportunities include probing wide-angle ejecta and off-axis afterglows of neutron star (NS)-NS mergers; enabling direct size measurements of radio ejecta from NS-NS mergers; and unraveling the physics behind the progenitors of compact binary mergers via host galaxy studies at radio wavelengths. Our results show that, thanks to its unprecedented sensitivity and resolution, the ngVLA will enable transformational results in the multi-messenger exploration of the transient radio sky.
The detection of the binary neutron star (BNS) merger, GW170817, was the first success story of multi-messenger observations of compact binary mergers. The inferred merger rate along with the increased sensitivity of the ground-based gravitational-wave (GW) network in the present LIGO/Virgo, and future LIGO/Virgo/KAGRA observing runs, strongly hints at detection of binaries which could potentially have an electromagnetic (EM) counterpart. A rapid assessment of properties that could lead to a counterpart is essential to aid time-sensitive follow-up operations, especially robotic telescopes. At minimum, the possibility of counterparts require a neutron star (NS). Also, the tidal disruption physics is important to determine the remnant matter post merger, the dynamics of which could result in the counterparts. The main challenge, however, is that the binary system parameters such as masses and spins estimated from the real time, GW template-based searches are often dominated by statistical and systematic errors. Here, we present an approach that uses supervised machine-learning to mitigate such selection effects to report possibility of counterparts based on presence of a NS component, and presence of remnant matter post merger in real time.
We apply machine learning methods to build a time-domain model for gravitational waveforms from binary black hole mergers, called mlgw. The dimensionality of the problem is handled by representing the waveforms amplitude and phase using a principal component analysis. We train mlgw on about $mathcal{O}(10^3)$ TEOBResumS and SEOBNRv4 effective-one-body waveforms with mass ratios $qin[1,20]$ and aligned dimensionless spins $sin[-0.80,0.95]$. The resulting models are faithful to the training sets at the ${sim}10^{-3}$ level (averaged on the parameter space). The speed up for a single waveform generation is a factor 10 to 50 (depending on the binary mass and initial frequency) for TEOBResumS and approximately an order of magnitude more for SEOBNRv4. Furthermore, mlgw provides a closed form expression for the waveform and its gradient with respect to the orbital parameters; such an information might be useful for future improvements in GW data analysis. As demonstration of the capabilities of mlgw to perform a full parameter estimation, we re-analyze the public data from the first GW transient catalog (GWTC-1). We find broadly consistent results with previous analyses at a fraction of the cost, although the analysis with spin aligned waveforms gives systematic larger values of the effective spins with respect to previous analyses with precessing waveforms. Since the generation time does not depend on the length of the signal, our model is particularly suitable for the analysis of the long signals that are expected to be detected by third-generation detectors. Future applications include the analysis of waveform systematics and model selection in parameter estimation.
Gravitational-wave memory manifests as a permanent distortion of an idealized gravitational-wave detector and arises generically from energetic astrophysical events. For example, binary black hole mergers are expected to emit memory bursts a little more than an order of magnitude smaller in strain than the oscillatory parent waves. We introduce the concept of orphan memory: gravitational-wave memory for which there is no detectable parent signal. In particular, high-frequency gravitational-wave bursts ($gtrsim$ kHz) produce orphan memory in the LIGO/Virgo band. We show that Advanced LIGO measurements can place stringent limits on the existence of high-frequency gravitational waves, effectively increasing the LIGO bandwidth by orders of magnitude. We investigate the prospects for and implications of future searches for orphan memory.
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