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Register a new userReconstruction of Chirp Mass in the Search of Compact Binaries
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Excess energy method is used in searches of gravitational waves (GWs) produced from sources with poorly modeled characteristics. It identifies GW events by searching for a coincidence appearance of excess energy in a GW detector network. While it is sensitive to a wide range of signal morphologies, the energy outliers can be populated by background noise events (background), thereby reducing the statistical confidence of a true signal. However, if the physics of the source is partially understood, weak model dependent constraints can be imposed to suppress the background. This letter presents a novel idea of using the reconstructed chirp mass along with two goodness of fit parameters for suppressing background when search is focused on GW produced from the compact binary coalescence.
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Shortly after a new class of objects is discovered, the attention shifts from the properties of the individual sources to the question of their origin: do all sources come from the same underlying population, or several populations are required? What are the properties of these populations? As the detection of gravitational waves is becoming routine and the size of the event catalog increases, finer and finer details of the astrophysical distribution of compact binaries are now within our grasp. This Chapter presents a pedagogical introduction to the main statistical tool required for these analyses: hierarchical Bayesian inference in the presence of selection effects. All key equations are obtained from first principles, followed by two examples of increasing complexity. Although many remarks made in this Chapter refer to gravitational-wave astronomy, the write-up is generic enough to be useful to researchers and graduate students from other fields.
We present an Advanced LIGO and Advanced Virgo search for sub-solar mass ultracompact objects in data obtained during Advanced LIGOs second observing run. In contrast to a previous search of Advanced LIGO data from the first observing run, this search includes the effects of component spin on the gravitational waveform. We identify no viable gravitational wave candidates consistent with sub-solar mass ultracompact binaries with at least one component between 0.2 - 1.0 solar masses. We use the null result to constrain the binary merger rate of (0.2 solar mass, 0.2 solar mass) binaries to be less than 3.7 x 10^5 Gpc^-3 yr^-1 and the binary merger rate of (1.0 solar mass, 1.0 solar mass) binaries to be less than 5.2 x 10^3 Gpc^-3 yr^-1. Sub-solar mass ultracompact objects are not expected to form via known stellar evolution channels, though it has been suggested that primordial density fluctuations or particle dark matter with cooling mechanisms and/or nuclear interactions could form black holes with sub-solar masses. Assuming a particular primordial black hole formation model, we constrain a population of merging 0.2 solar mass black holes to account for less than 16% of the dark matter density and a population of merging 1.0 solar mass black holes to account for less than 2% of the dark matter density. We discuss how constraints on the merger rate and dark matter fraction may be extended to arbitrary black hole population models that predict sub-solar mass binaries.
We study the compact binary population in star clusters, focusing on binaries containing black holes, using a self-consistent Monte Carlo treatment of dynamics and full stellar evolution. We find that the black holes experience strong mass segregation and become centrally concentrated. In the core the black holes interact strongly with each other and black hole-black hole binaries are formed very efficiently. The strong interactions, however, also destroy or eject the black hole-black hole binaries. We find no black hole-black hole mergers within our simulations but produce many hard escapers that will merge in the galactic field within a Hubble time. We also find several highly eccentric black hole-black hole binaries that are potential LISA sources, suggesting that star clusters are interesting targets for space-based detectors. We conclude that star clusters must be taken into account when predicting compact binary population statistics.
The construction of constraint-satisfying initial data is an essential element for the numerical exploration of the dynamics of compact-object binaries. While several codes have been developed over the years to compute generic quasi-equilibrium configurations of binaries comprising either two black holes, or two neutron stars, or a black hole and a neutron star, these codes are often not publicly available or they provide only a limited capability in terms of mass ratios and spins of the components in the binary. We here present a new open-source collection of spectral elliptic solvers that are capable of exploring the major parameter space of binary black holes (BBHs), binary neutron stars (BNSs), and mixed binaries of black holes and neutron stars (BHNSs). Particularly important is the ability of the spectral-solver library to handle neutron stars that are either irrotational or with an intrinsic spin angular momentum that is parallel to the orbital one. By supporting both analytic and tabulated equations of state at zero or finite temperature, the new infrastructure is particularly geared towards allowing for the construction of BHNS and BNS binaries. For the latter, we show that the new solvers are able to reach the most extreme corners in the physically plausible space of parameters, including extreme mass ratios and spin asymmetries, thus representing the most extreme BNS computed to date. Through a systematic series of examples, we demonstrate that the solvers are able to construct quasi-equilibrium and eccentricity-reduced initial data for BBHs, BNSs, and BHNSs, achieving spectral convergence in all cases. Furthermore, using such initial data, we have carried out evolutions of these systems from the inspiral to after the merger, obtaining evolutions with eccentricities $lesssim 10^{-4}-10^{-3}$, and accurate gravitational waveforms.
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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