Bulletin of the American Physical Society
APS April Meeting 2017
Volume 62, Number 1
Saturday–Tuesday, January 28–31, 2017; Washington, DC
Session X6: Gravitational Wave Data Analysis Techniques |
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Sponsoring Units: DGRAV Chair: Jessica McIver, Caltech Room: Virginia C |
Tuesday, January 31, 2017 10:45AM - 10:57AM |
X6.00001: Realtime detection of gravitational wave bursts Neil Cornish, Margaret Millhouse I will describe a new low-latency algorithm that is being developed to detect gravitational wave bursts using coherent wavelet denoising. The algorithm reconstructs the signals and has the capability to produce sky localization maps in realtime. Triggers from this low-latency pipeline will be fed to the BayesWave algorithm for in-depth study and the assignment of signal-to-noise Bayes factors. [Preview Abstract] |
Tuesday, January 31, 2017 10:57AM - 11:09AM |
X6.00002: Bayesian reconstruction of gravitational wave bursts using chirplets Margaret Millhouse, Neil Cornish, Tyson Littenberg The BayesWave algorithm has been shown to accurately reconstruct unmodeled short duration gravitational wave bursts and to distinguish between astrophysical signals and transient noise events. BayesWave does this by using a variable number of sine-Gaussian (Morlet) wavelets to reconstruct data in multiple interferometers. While the Morlet wavelets can be summed together to produce any possible waveform, there could be other wavelet functions that improve the performance. Because we expect most astrophysical gravitational wave signals to evolve in frequency, modified Morlet wavelets with linear frequency evolution - called chirplets - may better reconstruct signals with fewer wavelets. We compare the performance of BayesWave using Morlet wavelets and chirplets on a variety of simulated signals. [Preview Abstract] |
Tuesday, January 31, 2017 11:09AM - 11:21AM |
X6.00003: Targeting highly eccentric black hole binaries with a gravitational wave burst search Paul Baker, Neil Cornish, Sean McWilliams Recent studies have suggested that a non-negligible fraction of coalescing binary black hole systems may enter the aLIGO band with large eccentricity. These systems are challenging to detect with template-based gravitational wave searches due to systematic modeling errors. Current gravitational wave burst searches may miss these signals, because their power can be spread across several time-separated bursts and a wider bandwidth than quasi-circular signals. We describe a new search method being developed for highly eccentric binary black hole systems. This search uses a fast wavelet denoising method that can increase signal-to-noise ratio by collecting several associated bursts. In the future we hope to implement this method to generate low latency triggers that can be further analyzed by the BayesWave burst parameter estimation pipeline. [Preview Abstract] |
Tuesday, January 31, 2017 11:21AM - 11:33AM |
X6.00004: Inference of the compact binary population and merger rate from transient gravitational wave observations Daniel Wysocki, Richard O'Shaughnessy, Ernest Fokoue In this talk, we present a comparison of methods for estimating the rate density as a function of parameters for compact binary coalescences (CBC's) observed by LIGO. We focus on weakly-parametric and non-parametric methods, including Gaussian mixture models, Gaussian processes, and classical infinite dimensional regularized Bayesian techniques. We assess the performance of these methods on constructed synthetic, plausible populations of CBC's. We discuss the scaling of these methods to higher dimensions and appropriate measures of performance. [Preview Abstract] |
Tuesday, January 31, 2017 11:33AM - 11:45AM |
X6.00005: A Bayesian-style approach to estimating LISA science capability. John Baker, Sylvain Marsat A full understanding of LISA's science capability will require accurate models of incident waveform signals and the instrumental response. While Fisher matrix analysis is useful for some estimates, a Bayesian characterization of simulated probability distributions is needed for understanding important cases at the limit of LISA's capability. We apply fast analysis algorithms enabling accurate treatment using EOB waveforms with relevant higher modes and the full-featured LISA response to study these aspects of LISA science capability. [Preview Abstract] |
Tuesday, January 31, 2017 11:45AM - 11:57AM |
X6.00006: Bayesian model-emulation of stochastic gravitational-wave spectra for probes of the final-parsec problem with pulsar-timing arrays Stephen Taylor, Joseph Simon, Laura Sampson The final parsec of supermassive black-hole binary evolution is subject to the complex interplay of stellar loss-cone scattering, circumbinary disk accretion, and gravitational-wave emission, with binary eccentricity affected by all of these. The strain spectrum of gravitational-waves in the pulsar-timing band thus encodes rich information about the binary population's response to these various environmental mechanisms. Current spectral models have heretofore followed basic analytic prescriptions, and attempt to investigate these final-parsec mechanisms in an indirect fashion. Here we describe a new technique to directly probe the environmental properties of supermassive black-hole binaries through "Bayesian model-emulation". We perform black-hole binary population synthesis simulations at a restricted set of environmental parameter combinations, compute the strain spectra from these, then train a Gaussian process to learn the shape of spectrum at any point in parameter space. We describe this technique, demonstrate its efficacy with a program of simulated datasets, then illustrate its power by directly constraining final-parsec physics in a Bayesian analysis of the NANOGrav 5-year dataset. The technique is fast, flexible, and robust. [Preview Abstract] |
Tuesday, January 31, 2017 11:57AM - 12:09PM |
X6.00007: Gravitational Wave Emulation Using Gaussian Process Regression Zoheyr Doctor, Ben Farr, Daniel Holz Parameter estimation (PE) for gravitational wave signals from compact binary coalescences (CBCs) requires reliable template waveforms which span the parameter space. Waveforms from numerical relativity are accurate but computationally expensive, so approximate templates are typically used for PE. These 'approximants', while quick to compute, can introduce systematic errors and bias PE results. We describe a machine learning method for generating CBC waveforms and uncertainties using existing accurate waveforms as a training set. Coefficients of a reduced order waveform model are computed and each treated as arising from a Gaussian process. These coefficients and their uncertainties are then interpolated using Gaussian process regression (GPR). As a proof of concept, we construct a training set of approximant waveforms (rather than NR waveforms) in the two-dimensional space of chirp mass and mass ratio and interpolate new waveforms with GPR. We demonstrate that the mismatch between interpolated waveforms and approximants is below the 1\% level for an appropriate choice of training set and GPR kernel hyperparameters. [Preview Abstract] |
Tuesday, January 31, 2017 12:09PM - 12:21PM |
X6.00008: LIGO detector characterization with genetic programming Marco Cavaglia, Kai Staats, Luciano Errico, Kentaro Mogushi, Hunter Gabbard Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches. [Preview Abstract] |
Tuesday, January 31, 2017 12:21PM - 12:33PM |
X6.00009: An architecture for efficient multimodal parameter estimation with linear surrogate models Richard O'Shaughnessy, Scott Field In this talk, we present a natural union of two techniques: reduced order modeling and an alternative factorization of the likelihood function. We show that in a suitable (linear) basis, likelihood evaluations become effectively analytic, enabling embarassingly parallel Monte Carlo integration over all intrinsic parameters, except mass. We demonstrate the utility of our method using synthetic events similar to GW150914. We describe the extraordinary efficiency this calculation allows. We discuss applications to low-latency parameter estimation and searches for gravitational waves. [Preview Abstract] |
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