Bulletin of the American Physical Society
APS April Meeting 2019
Volume 64, Number 3
Saturday–Tuesday, April 13–16, 2019; Denver, Colorado
Session Y10: Gravitational Waves: Data Analysis Techniques 2 |
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Sponsoring Units: DGRAV Chair: Karan Jani, Georgia Tech Room: Sheraton Governor's Square 12 |
Tuesday, April 16, 2019 1:30PM - 1:42PM |
Y10.00001: Integrated approaches to deep learning in gravitational-wave signal modeling and statistical inference Alvin J. K. Chua The detection and characterization of gravitational-wave signals in data from contemporary and next-generation detectors can be computationally challenging or even prohibitive, due to the accuracy requirements imposed on certain waveform models and the high-dimensional posterior sampling required for Bayesian inference. I discuss several promising strategies for streamlining the process with an integrated model-to-inference framework, making use of techniques from the deep-learning paradigm. |
Tuesday, April 16, 2019 1:42PM - 1:54PM |
Y10.00002: NNETFIX: A neural network to 'fix' Gravitational Wave signals overlapping with short-duration glitches in LIGO-Virgo data. Sumeet Kulkarni, Marco Cavaglia With improved Advanced LIGO and Virgo detector sensitivity, it is increasingly likely that astrophysical Gravitational Wave (GW) signals overlap with short-duration noise transients. This may affect the initial parameter estimation and sky localization of the glitch-affected GW triggers, resulting in inaccurate low-latency alerts to observatories for Electromagnetic follow-up. Here we introduce NNETFIX: A multi-layered perceptron-based neural network algorithm that 'fixes' GW signals coincident with short-duration glitches. NNETFIX operates by gating the glitch and identifying the features of the GW signal to reconstruct it in the portion of the data affected by the glitch, improving upon the signal-to-noise ratio and recovering the signal parameters effectively. We present an application of NNETFIX on real Advanced LIGO data and propose plans to incorporate it into the low-latency LIGO and Virgo analysis framework. |
Tuesday, April 16, 2019 1:54PM - 2:06PM |
Y10.00003: A new method to find the origin of glitches in ground-based gravitational-wave detectors Kentaro Mogushi, Massimiliano Razzano, Marco Cavaglia, Giancarlo Cella, Giorgio Nicolini Excess transient noise events, or "glitches", impact the data quality of ground-based gravitational-wave detectors and impair the detection of signals produced by astrophysical sources. Identification of the causes of these glitches is a crucial starting point for the improvement of gravitational-wave signals detectability. However, glitches are the product of linear and non-linear couplings among the interrelated detector-control systems that include mitigation of ground motion and regulation of optic motion, which generally makes it difficult to find their origin. We present a method that uses information from instrumental control systems and environmental sensors around the time when glitches are present in the detector's output to reveal essential clues about their origin. We illustrate the method on Advanced LIGO and Virgo data. |
Tuesday, April 16, 2019 2:06PM - 2:18PM |
Y10.00004: Rapid parameter estimation for compact binary coalescence events in LIGO data Sinead Walsh The benefit of prompt and accurate estimation of the parameters of binary coalescences is obvious in its coupling to electromagnetic observations. Popular Bayesian schemes to measure properties of compact object binaries use Markovian sampling to compute the posterior. While very successful, in some cases, convergence is delayed until well after the electromagnetic fluence has subsided thus diminishing the potential science return. In this talk, I discuss a scheme which is also Bayesian, but has a drastically lower convergence time of a few tens of minutes. I will describe recent studies to reduce the latency of results further, and demonstrate the capabilities of our parameter estimation framework. |
Tuesday, April 16, 2019 2:18PM - 2:30PM |
Y10.00005: Background reduction in LIGO-Virgo searches for supernova signals with machine learning Marco Cavaglia, Sergio Gaudio, Travis James Hansen, Kai Staats, Marek Szczepanczyk, Michele Zanolin About 20% of the data collected by Advanced LIGO and Virgo in the next observing runs will be single-interferometer data, i.e., they will be collected at times when only one detector in the network is operating in observing mode. If a galactic supernova occurs during single-interferometer times, separation of its gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors. We present a method to improve the background of LIGO and Virgo single-interferometer supernova searches based on the standard LIGO-Virgo coherent WaveBurst (cWB) pipeline and genetic programming, a supervised machine learning algorithm that uses the strategy of natural selection to solve complex problems. We show that it is possible to discriminate galactic gravitational-wave supernova signals from noise transients with high efficiency, thus increasing the supernova detection reach of Advanced LIGO and Virgo. |
Tuesday, April 16, 2019 2:30PM - 2:42PM |
Y10.00006: The impact of LIGO noise transients on accurate gravitational-wave event skymaps Jess McIver, Thomas J Massinger, Derek Davis, Laura Kate Nuttall, Vivien Raymond, Rory Smith Frequent transient noise artifacts, or 'glitches', in Advanced LIGO data limit the sensitivity of gravitational wave (GW) searches, and in cases of overlap, can bias the estimation of the astrophysical parameters of GW sources. We explore the effect of common classes of transient noise in the Advanced LIGO detectors on the parameter estimation of compact object mergers, including accurate sky location, and the prospects for rapidly informing gravitational-wave candidate event alerts of noise bias effects. |
Tuesday, April 16, 2019 2:42PM - 2:54PM |
Y10.00007: Strain Histograms for Evaluating Continuous Gravitational Wave Candidates Grant Weldon, Keith Riles Following the recent discoveries of gravitational waves from compact binary mergers, searches are under way in Advanced LIGO and Virgo data for continuous gravitational radiation emanating from rapidly spinning neutron stars. Continuous wave (CW) search algorithms often yield outlier candidates of non-astrophysical origin due to instrumental and environmental artifacts across one or both detectors. Strain histograms in the detector reference-frame frequency space permit visual assessment of outlier contamination from artifacts via superposition of the putative signal contribution on a background estimated from interpolation between neighboring spectral bands. Signal strain histograms can be constructed from computationally expensive simulations or from more rapid approximation methods. The development of a program for histogram generation using both exact and approximate simulation methods will be presented, with examples shown from CW searches in Advanced LIGO data. |
Tuesday, April 16, 2019 2:54PM - 3:06PM |
Y10.00008: Modern Pulsar Timing Array Sensitivity Curves Jeffrey Shafiq Hazboun As pulsar timing arrays (PTAs) get closer than ever to observing gravitational waves, it is important that we are able to characterize the noise in these galactic-scale detectors. Sensitivity curves, constructed using the power spectral density of the noise in a detector, together with its response to gravitational waves, are used by the gravitational-wave community to characterize sensitivity to a particular kind of signal. While these sensitivity curves are detailed for ground-based and space-based detectors, those for PTAs are often presented in the form of a pie wedge, basically describing the white noise limitations on detection of gravitational waves. Characterization of the various sources of noise in our pulsars has evolved to a point where different types of white noise and low-frequency time-correlated (red) noise can be accounted for in our gravitational wave searches. Here we report on our investigations of calculating more realistic sensitivity curves for pulsar timing arrays using data from the North American Nanohertz Observatory for Gravitational Waves to provide the noise power spectral density. |
Tuesday, April 16, 2019 3:06PM - 3:18PM |
Y10.00009: The "Dropout Method”: A Bayesian technique for identifying spurious signals in pulsar timing arrays Sarah J Vigeland, Michele Vallisneri, Stephen R Taylor Pulsar timing arrays (PTAs) detect gravitational waves (GWs) by looking for correlations in the pulse times of arrival of an array of millisecond pulsars (MSPs). A gravitational wave will affect many MSPs, whereas most noise sources should be confined to the residuals of a single pulsar. However, we have found that unmodeled signals in a single pulsar can be confused for a GW. I will present the "dropout method," which can be used to identify these spurious signals by determining how much each pulsar contributes to the signal, and discuss how it has been used in the analysis of the NANOGrav 11-year data set for GWs from individual supermassive black hole binaries. |
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