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Register a new userA Machine Learning Based Source Property Inference for Compact Binary Mergers
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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.
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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.
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 rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.
We present the results of a community study aimed at exploring some of the scientific opportunities that the next generation Very Large Array (ngVLA) could open in the field of multi-messenger time-domain astronomy. We focus on compact binary mergers, golden astrophysical targets of ground-based gravitational wave (GW) detectors such as advanced LIGO. A decade from now, a large number of these mergers is likely to be discovered by a world-wide network of GW detectors. This will enable the identification of host galaxies, either directly through detection of electromagnetic (EM) counterparts, or indirectly by probing potential anisotropies in the spatial distribution of mergers. Identifying the host galaxy population of GW mergers would provide a way to constrain the efficiency of various binary neutron star (NS) or black hole (BH) formation scenarios, and the merger timescale distributions as linked to merger rates in early- and late-type galaxies. We discuss how a radio array with ~10x the sensitivity of the current Karl G. Jansky VLA and ~10x the resolution, would enable resolved radio continuum studies of binary merger hosts, probing regions of the galaxy undergoing star formation (which can be heavily obscured by dust and gas), AGN components, and mapping the offset distribution of the mergers with respect to the host galaxy light. For compact binary mergers containing at least one NS and with associated EM counterparts, we show how the ngVLA would enable direct size measurements of the relativistic merger ejecta and probe, for the first time directly, their dynamics.
We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by decision tree based methods, which enables the visualization of the classification categories. Secondly, we investigate how the Mutual Information based Transductive Feature Selection (MINT) algorithm can be used to perform feature pre-selection. If one would like to provide only a small number of input features to a machine learning classification algorithm, feature pre-selection provides a method to determine which of the many possible input properties should be selected. Third is the use of the tree-interpreter package to enable popular decision tree based ensemble methods to be opened, visualized, and understood. This is done by additional analysis of the tree based model, determining not only which features are important to the model, but how important a feature is for a particular classification given its value. Lastly, we use decision boundaries from the model to revise an already existing method of classification, essentially asking the tree based method where decision boundaries are best placed and defining a new classification method. We showcase these techniques by applying them to the problem of star-galaxy separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We use the output of MINT and the ensemble methods to demonstrate how more complex decision boundaries improve star-galaxy classification accuracy over the standard SDSS frames approach (reducing misclassifications by up to $approx33%$). We then show how tree-interpreter can be used to explore how relevant each photometric feature is when making a classification on an object by object basis.
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