Bulletin of the American Physical Society
APS April Meeting 2019
Volume 64, Number 3
Saturday–Tuesday, April 13–16, 2019; Denver, Colorado
Session X10: Gravitational Waves: Data Analysis Techniques 1 |
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Sponsoring Units: DGRAV Chair: Jess McIver, Caltech Room: Sheraton Governor's Square 12 |
Tuesday, April 16, 2019 10:45AM - 10:57AM |
X10.00001: Improving SEOBNRv3 for LIGO Parameter Estimation Tyler Knowles, Zach B Etienne, Sean T McWilliams The Spinning Effective One Body--Numerical Relativity (SEOBNR) series of gravitational wave approximants are among the best available for Advanced LIGO/Virgo parameter estimation (PE). Over the past two years we have sped up, via direct code optimization, the eight-dimensional SEOBNR version 3 (SEOBNRv3) approximant by a factor of ~340x. This is particularly significant since there is no known way to develop an efficient eight-dimensional Reduced-Order Model that is faster than the SEOB code itself. We report on our latest work, both in speeding up our optimized SEOBNRv3 approximant (SEOBNRv3_opt) by another order of magnitude and in making SEOBNRv3_opt more robust than SEOBNRv3. This work brings direct SEOBNRv3-based PE within reach for many important LIGO sources. |
Tuesday, April 16, 2019 10:57AM - 11:09AM |
X10.00002: Connecting the disconnected: development and testing of a prior to detect highly eccentric sources Belinda D Cheeseboro, Paul T Baker, Sean T McWilliams, Amber Lenon Recent simulations of compact binary populations have predicted that some binaries may have non-negligible eccentricity when entering the LIGO band. Highly eccentric systems produce a series of disconnected bursts that current data analysis methods in LIGO cannot detect, and even modest eccentricities without separated bursts present a challenge for existing methods. Therefore, we are developing an unmodeled algorithm to target disconnected bursts of power in time and frequency due to eccentric modulations. Specifically, we propose a new signal prior for use within the existing BayesWave infrastructure for conducting Bayesian analyses of LIGO data. This prior will help to connect these disconnected bursts occurring throughout the waveform. It does this by using an orbital model that predicts the locations in time and frequency for the next or previous burst, within an appropriately defined uncertainty interval, allowing us to link together individually weaker bursts and identify them collectively as a signal. We will discuss current developments and results of testing this prior. |
Tuesday, April 16, 2019 11:09AM - 11:21AM |
X10.00003: Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning Wei Wei, Eliu Antonio Huerta Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps greater than 0.99. We discuss the implications of these results for the characterization of gravitational wave signals. |
Tuesday, April 16, 2019 11:21AM - 11:33AM |
X10.00004: Optimized convolutional neural networks for the detection of multimodal gravitational wave signals Scott E Field, Collin D Capano, Dwyer Deighan, Gaurav Khanna Gravitational wave astronomy can benefit from the rapid classification of gravitational wave signals buried deep in instrumentation noise. In 2017, George and Huerta (and since then other researchers) have considered Convolutional Neural Networks to detect gravitational wave signals and estimate some of the corresponding binary's parameters. In this talk, I will describe extensions of this classification strategy. In particular, we discuss strategies to optimize the hyperparameters of our network, in an attempt to make our networks as compact and effective as possible. Results will be discussed for training data using models with and without subdominant modes. |
Tuesday, April 16, 2019 11:33AM - 11:45AM |
X10.00005: Impact of subdominant modes on the measurability of binary black hole spin parameters Feroz H Shaik, Scott E Field, Jacob Lange, Richard O'Shaughnessy, Vijay Varma Gravitational-wave Bayesian inference studies require a model to estimate parameter values. Most previous parameter estimation (PE) studies use models that neglect spherical harmonic modes beyond the dominant quadrupole one due to their unavailability. Recently, surrogate models built using numerical relativity (NR) data have been developed that are fast enough for PE studies, contain higher mode content, and cover a large portion of the relevant parameter space. These surrogates also contain the late inspiral, merger, and ringdown of the binary system, making them ideal for parameter estimation studies of heavy systems. In particular, we present results using a recently built aligned-spin surrogate model and a highly-parallelizable rapid inference algorithm. Using these NR-based surrogate models we performed a parameter estimation study to understand the effects of higher modes on spin measurability for both current and future gravitational-wave detectors. |
Tuesday, April 16, 2019 11:45AM - 11:57AM |
X10.00006: Inferring the binary black hole population redshift distribution Denyz Melchor, Rory Smith In the past years LIGO-VIRGO has detected five binary black hole mergers; in the universe, there are one hundred thousand binary black hole mergers a year which creates motivation to investigate populations of black holes. New searches are currently being designed to detect the signature of the gravitational wave background of all the distant binary black hole mergers. I will describe the process of how we apply statistical inference to describe the astrophysical parameters of this background. My focus will be on inferring the redshift distribution of the population of black holes which has implications in star formation and primordial black holes. |
Tuesday, April 16, 2019 11:57AM - 12:09PM |
X10.00007: Early warning detection of gravitational waves from binary neutron stars and neutron star-black hole coalescences Surabhi Sachdev GW170817 was the first gravitational-wave event that was also observed in electromagnetic. However, it’s optical counterpart was not detected for 10 hours after merger since the sky-localization was below horizon for most of the observing telescopes. Therefore, we are left with open questions regarding the electromagnetic emission in every wavelength except gamma rays for the first ten hours. Our goal is to provide alerts for binary neutron stars and neutron star-black hole coalescences 10 s to a minute before merger, with a rough sky location so that the electromagnetic facilities can start the process of setting up their observations in advance of the merger making it possible to capture the earliest possible light with narrow field instruments. We present the results from a mock data study and show the expected rates and sky localizations of such events as a function of time before coalescence. We also present these results in particular from a case study of GW170817. |
Tuesday, April 16, 2019 12:09PM - 12:21PM |
X10.00008: Non-parametric Inference of the Neutron Star Equation of State with Gravitational Waves Reed Essick, Philippe Landry GW170817 provides the first opportunity to constrain the supranuclear equation of state within the core of neutron stars using gravitational-wave observations. As more systems are detected, equation of state modeling systematics could become as significant as statistical uncertainty from the resulting combined measurement. I introduce a non-parameteric inference scheme for the equation of state that supports much broader model freedom and therefore fewer systematic errors than other approaches. Furthermore, this non-parametric scheme can be tuned to resemble existing nuclear theoretic models or specific nuclear phenomenology to a specifiable degree self-consistently. I will summarize the equation of state constraints obtained from GW170817 using this approach and discuss possible implications for nuclear theoretic models. |
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