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Register a new userEnhancing Gravitational-Wave Science with Machine Learning
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Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.
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The second generation of gravitational-wave detectors are being built and tuned all over the world. The detection of signals from binary black holes is beginning to fulfill the promise of gravitational-wave astronomy. In this work, we examine several possible configurations for third-generation laser interferometers in existing km-scale facilities. We propose a set of astrophysically motivated metrics to evaluate detector performance. We measure the impact of detector design choices against these metrics, providing a quantitative cost-benefit analyses of the resulting scientific payoffs.
We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations. A machine-learning emulator is trained on numerical population-synthesis predictions and inserted into a Bayesian hierarchical framework. In this case study, a modest but state-of-the-art suite of simulations of isolated binary stars is interpolated across two event parameters and one population parameter. The validation process of our pipelines highlights how omitting some of the event parameters might cause errors in estimating selection effects, which propagates as systematics to the final population inference. Using LIGO/Virgo data from O1 and O2 we infer that black holes in binaries are most likely to receive natal kicks with one-dimensional velocity dispersion $sigma$ = 105+44 km/s. Our results showcase potential applications of machine-learning tools in conjunction with population-synthesis simulations and gravitational-wave data.
We present the results of a search for gravitational-wave bursts associated with 137 gamma-ray bursts (GRBs) that were detected by satellite-based gamma-ray experiments during the fifth LIGO science run and first Virgo science run. The data used in this analysis were collected from 2005 November 4 to 2007 October 1, and most of the GRB triggers were from the Swift satellite. The search uses a coherent network analysis method that takes into account the different locations and orientations of the interferometers at the three LIGO-Virgo sites. We find no evidence for gravitational-wave burst signals associated with this sample of GRBs. Using simulated short-duration (<1 s) waveforms, we set upper limits on the amplitude of gravitational waves associated with each GRB. We also place lower bounds on the distance to each GRB under the assumption of a fixed energy emission in gravitational waves, with typical limits of D ~ 15 Mpc (E_GW^iso / 0.01 M_o c^2)^1/2 for emission at frequencies around 150 Hz, where the LIGO-Virgo detector network has best sensitivity. We present astrophysical interpretations and implications of these results, and prospects for corresponding searches during future LIGO-Virgo runs.
The LIGO observatories detect gravitational waves through monitoring changes in the detectors length down to below $10^{-19}$,$m/sqrt{Hz}$ variation---a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance in the gravitational wave signal of brief, loud noise artifacts called ``glitches, which are environmental or instrumental in origin but can mimic true gravitational waves and therefore hinder sensitivity. Currently they are primarily identified by analysis of the gravitational wave data stream. Here we present a machine learning approach that can identify glitches by monitoring textit{all} environmental and detector data channels, a task that has not previously been pursued due to its scale and the number of degrees of freedom within gravitational-wave detectors. The presented method is capable of reducing the gravitational-wave detector networks false alarm rate and improving the LIGO instruments, consequently enhancing detection confidence.
Soon after the observation of the first black hole binary (BHB) by advanced LIGO (aLIGO), GW150914, it was realised that such a massive system would have been observable in the milli-Hz (mHz) band few years prior to coalescence. Operating in the frequency range 0.1-100 mHz, the Laser Interferometer Space Antenna (LISA) can potentially detect up to thousands inspiralling BHBs, based on the coalescence rates inferred from the aLIGO first observing run (O1). The vast majority of them (those emitting at $f<10$ mHz) will experience only a minor frequency drift during LISA lifetime, resulting in signals similar to those emitted by galactic white dwarf binaries. At $f>10$ mHz however, several of them will sweep through the LISA band, eventually producing loud coalescences in the audio-band probed by aLIGO. This contribution reviews the scientific potential of these new class of LISA sources which, in the past few months, has been investigated in several contexts, including multi-messenger and multi-band gravitational wave astronomy, BHB astrophysics, tests of alternative theories of gravity and cosmography.
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