Bulletin of the American Physical Society
APS April Meeting 2020
Volume 65, Number 2
Saturday–Tuesday, April 18–21, 2020; Washington D.C.
Session R15: Gravitational Wave Data AnalysisLive

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Sponsoring Units: DGRAV Chair: Maximilano Isi, Massachusetts Institute of Technology Room: Virginia B 
Monday, April 20, 2020 1:30PM  1:42PM Live 
R15.00001: Stein Variational Inference for Gravitational Wave Likelihood Estimation Alex Leviyev, Bassel Saleh, Joshua Chen, Peng Chen, Omar Ghattas, Aaron Zimmerman The planned upgrades to the gravitational wave detectors promises a vastly improved detection rate for binary inspirals; however, a full parameter estimation analysis of such a signal can take days or weeks. This presents a bottleneck in the performance of a gravitational wave analysis pipeline. Improvements in the speed and efficiency of parameter estimation would have many potential benefits, e.g. facilitating the use of more sophisticatedand computationally expensivesignal models. Current parameter estimation techniques (such as nested sampling) rely on stochastic sampling algorithms. We propose a new optimization based algorithm which relies on minimizing the KullbackLeibler divergence between an approximation of, and a reference posterior. This procedure yields a transport map that can be used to produce an arbitrary sized empirical sampling. We test whether this indeed can lead to a faster, yet still accurate alternative. [Preview Abstract] 
Monday, April 20, 2020 1:42PM  1:54PM Live 
R15.00002: A manifoldbased sampling method for scientific inference Alvin Chua Bayesian inference in the physical sciences typically involves the density estimation of a defined posterior distribution through stochastic sampling, but this procedure can be hindered by the complexity and computational cost of using an accurate physical model. However, the noise assumptions in the inference problem are often much simpler, allowing the posterior to be cast as a common probability distribution (e.g., multivariate normal) that is restricted to an embedding of the model space into its domain. I propose a sampling framework that uses geometric information from such an embedding to aid local convergence, and to enable hyperefficient upsampling for a nearconverged set of samples. [Preview Abstract] 
Monday, April 20, 2020 1:54PM  2:06PM Live 
R15.00003: Subtracting compact binary foreground sources to reveal primordial gravitationalwave backgrounds Surabhi Sachdev, Tania Regimbau, Bangalore Sathyaprakash In addition to the loud and nearby sources of gravitational waves from black holeblack hole and neutron starneutron star binaries that are seen as isolated transient events, there is a population of weak, unresolved sources at higher redshifts. The superposition of these sources is expected to be the main contributor to the astrophysical stochastic background which may be detected in the next few years as the Advanced LIGO and Virgo detectors reach their design sensitivity. The astrophysical background contains a wealth of information about the history and evolution of point sources but it is a confusion background that obscures the observation of the primordial gravitationalwave background produced during the early stages of the Universe. In this talk, I will discuss the possibility of subtracting the astrophysical background with the third generation groundbased detectors, such as the Einstein Telescope and Cosmic Explorer in order to observe the primordial background. [Preview Abstract] 
Monday, April 20, 2020 2:06PM  2:18PM Live 
R15.00004: The reliability of the lowlatency estimation of binary neutron star chirp mass Salvatore Vitale, Andrea Sylvia Biscoveanu, CarlJohan Haster The LIGO and Virgo Collaborations currently conduct searches for gravitational waves from compact binary coalescences in realtime. For promising candidate events, a sky map and distance estimation are released in lowlatency, to facilitate their electromagnetic followup. Currently, no information is released about the masses of the compact objects. Recently, Margalit and Metzger (2019) have suggested that knowledge of the chirp mass of the detected binary neutron stars could be useful to prioritize the electromagnetic followup effort, and have urged the LIGOVirgo collaboration to release chirp mass information in lowlatency. However, the lowlatency searches for compact binaries make simplifying assumptions that could introduce biases in the mass parameters: neutron stars are treated as point particles with dimensionless spins below 0.05 perfectly aligned with the orbital angular momentum. Furthermore, the template bank used to search for them has a finite resolution. In this presentation we will show that none of these limitations can introduce chirp mass biases larger than $\sim10^{3}$ solar masses. We also quantify biases on other mass parameters. [Preview Abstract] 
Monday, April 20, 2020 2:18PM  2:30PM 
R15.00005: Deep Learning at Scale for Gravitational Wave Parameter Estimation from Surrogate Model Waveforms of Spinning Black Hole Mergers Asad Khan, Arnav Das, Eliu Huerta We explore the applications of deep learning for parameter estimation from waveforms of nonprecessing binary black hole mergers. The particular deep learning model used is a simplified version of WaveNet, an autoregressive model originally released by Google for generating raw audio waveforms. We modify the model to predict the mass ratio and individual spins of the two binary black holes by training it on quadrupole modes of 1.5 million waveforms simulated from NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms that is valid for the entire LIGO band. Since the surrogate model was trained on 108 numerical relativity waveforms with mass ratios $q \leq 8$ and spins $\chi_{1_z}, \chi_{2_z} \leq 0.8$, we restrict to the same parameter ranges for training and inference. Due to inherent degeneracies in the parameter space, waveform matching between simulated quadrupole modes of the ground truth parameters and the predicted parameters respectively is used to quantify the prediction errors. Our preliminary results show promising prediction errors, with waveform matches of greater than $0.98$ for $72$% of the test dataset. [Preview Abstract] 
Monday, April 20, 2020 2:30PM  2:42PM 
R15.00006: A Method of Enhancing the SignaltoNoise of Transient Radio and Gravitational Wave Sources Kristen Lackeos, Richard Lieu The Gaussian phase noise of radio intensity time series is demonstrated to be drastically reduced when the raw voltage data are digitally filtered through an arbitrarily large number $n$ of orthonormal bandpass profiles sharing the same intensity bandwidth, and the resulting intensity series are coadded. Specifically, the relative noise variance of the summed series at the resolution of one coherence time or less, goes down with increasing $n$ as $1/n$, although (consistent with the radiometer equation) the advantage gradually disappears when the series is bin averaged to lower resolution. Thus the algorithm is designed to enhance the sensitivity of detecting transients that are smoothed out by time averaging and too faint to be visible in the noisy unaveraged time series, as demonstrated by the simulation of a weak embedded time varying signal of either a periodic nature or a fast and unrepeated pulse. The algorithm is then applied to a 10 minute observation of the pulsar PSR 1937+21 by the VLA, where the theoretical predictions were verified by the data. Moreover, it is shown that microstructures within the time profile are better defined as the number $n$ of filters used increases, and a periodic signal of period $1.86 \times 10^{5}$s is discovered in the pulse profile. [Preview Abstract] 
Monday, April 20, 2020 2:42PM  2:54PM 
R15.00007: Bayesian Methods in TimeDelay Interferometric Ranging for the LISA Mission Jessica Page, Tyson Littenberg Laser frequency noise (LFN) due to unequal separations between spacecraft is the loudest source of noise expected in the LISA mission at $10^7$ greater magnitude than the typical strain expected for LISA GW signals. Timedelay interferometry (TDI) suppresses LFN to an acceptable level by linearly combining measurements from individual spacecraft delayed by durations that correspond to their relative separations. Knowledge of the delay durations is crucial for TDI effectiveness, and itâ€™s been shown that they can be estimated from the raw phasemeter data using fractional delay interpolation (FDI), allowing for TDI implementation during the postprocessing of data (timedelay interferometric ranging, TDIR) once data is telemetered to Earth. This work performs TDIR using Bayesian methods to estimate the delay durations. Including TDIR parameters in the LISA data model as part of a "Global Fit" analysis pipeline produces GW inferences that are marginalized over uncertainty in the spacecraft separations. As an initial step towards this goal, a Monte Carlo Markov Chain (MCMC) is used for estimating the four timeindependent delays required in the rigidly rotating unequalarm Michaelson approximation of the spacecraft configuration. [Preview Abstract] 
Monday, April 20, 2020 2:54PM  3:06PM Not Participating 
R15.00008: Inferring gravitational wave polarization content without templates Katerina Chatziioannou, Max Isi, Tyson Littenberg, Carl Johan Haster The addition of further detectors in the network of groundbased gravitational wave detectors offers the possibility to test the polarization content of the detected signals. I will discuss a morphologyindependent way to probe the polarization content of a signal using BayesWave, a data analysis pipeline that does not rely on compact binary waveform templates to model the observed signal. I will show how the polarization content can be used to study effects such as spinprecession. Additionally, I will describe how this data analysis framework can be generalized to incorporate polarization modes beyond the ones predicted by General Relativity and place constraints on their amplitude. [Preview Abstract] 
Monday, April 20, 2020 3:06PM  3:18PM Not Participating 
R15.00009: GlitchBuster: If there's something strange in your strain data, who you gonna call? Neil Cornish The LIGO/Virgo data are polluted by frequent noise transients, or glitches, which can mimic gravitational wave signals, and bias parameter estimates for real signals. Signals that inband for tens of seconds or more, such as those from binary neutron stars, have a high probability of competing with one or more glitches. Here I present a wavelet denoising algorithm that can clean the data of glitches in real time, while preserving any gravitational wave signals. [Preview Abstract] 
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