Bulletin of the American Physical Society
APS April Meeting 2022
Volume 67, Number 6
Saturday–Tuesday, April 9–12, 2022; New York
Session W16: Gravitational Wave Detection Methods for Pulsar Timing ArraysRecordings Available
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Sponsoring Units: DGRAV Chair: Surabhi Sachdev, University of Wisconsin - Milwaukee Room: Sky Lobby |
Monday, April 11, 2022 5:45PM - 5:57PM |
W16.00001: Reconciling frequentist and Bayesian detection statistics in pulsar-timing-array searches for gravitational waves Michele Vallisneri The pulsar-timing-array community has converged on the detection of "Hellings-Downs" correlations among the timing residuals of multiple pulsars as definitive evidence for the presence of a stochastic gravitational-wave signals. I describe how correlations emerge in frequentist (optimal statistic) and Bayesian (model comparison) tests; I outline how frequentist and Bayesian detection statements can be reconciled within a Bayesian model-checking outlook; and I discuss the appropriate level of significance for the first detection claim. |
Monday, April 11, 2022 5:57PM - 6:09PM |
W16.00002: Posterior predictive tests and Bayesian cross-validation for nano-Hz gravitational-wave background analyses Patrick Meyers, Michele Vallisneri, Katerina Chatziioannou The most recent results from NANOGrav, the Parkes Pulsar Timing Array, and the European Pulsar Timing Array report evidence of a common red noise process across the arrays that is consistent with a gravitational-wave background from a superposition of signals from supermassive black hole binaries. However, no evidence of Hellings-Downs spatial correlation between pulsars, which is required for a detection claim, has been reported. In this talk we explore what to do in the event that we think we have made a detection. We cover Bayesian cross-validation methods, for example leaving one pulsar out of the array and using the array from the remaining pulsars to predict its data. We also discuss alternative detection statistics which offer a different perspective on our results, and can make slightly different statements than classical p-values and Bayes factors. |
Monday, April 11, 2022 6:09PM - 6:21PM |
W16.00003: Investigating Bias from Using a Subset of Pulsars as a Pulsar Timing Array Aaron D Johnson, Sarah J Vigeland, Stephen R Taylor, Xavier Siemens Pulsar timing arrays (PTAs) aim to detect gravitational waves (GWs) by using millisecond pulsars as extremely stable clocks thereby creating a galaxy-wide GW detector. PTAs are predicted to detect a stochastic gravitational wave background (GWB) from a population of gravitational waves emitted from supermassive black hole binaries. PTA collaborations such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), the Parkes Pulsar Timing Array (PPTA), and the European Pulsar Timing Array (EPTA) have recently found a common-spectrum process in their data. However, the median amplitude values for all three detections are in tension with some of the previously computed upper limits. We investigate how using a subset of a set of pulsars affects computing upper limits in order to help explain why previous upper limits might have been biased toward lower values. |
Monday, April 11, 2022 6:21PM - 6:33PM |
W16.00004: Optimal Statistic for multiple cross correlated signals Shashwat C Sardesai, Sarah J Vigeland The optimal statistic is a frequentist method to estimate the amplitude of the stochastic GWB. In presvious analyses, we have calculated the optimal statistic for single, cross-correlated signals. To test the ability of the optimal statistic to retrieve multiple cross-correlated signals with different ORFs, we have created simulated timing residuals that contain a Hellings-Downs signal, or a GW-like monopole signal. We compare the recovered amplitudes and confidence intervals for these simulated data sets using different numbers of cross-correlated signals |
Monday, April 11, 2022 6:33PM - 6:45PM |
W16.00005: A Parallelized Gravitational Wave Detection Pipeline For Pulsar Timing Arrays Stephen R Taylor, Joseph Simon, Levi Schult, Nihan Pol, William Lamb Pulsar Timing Array (PTA) experiments are reporting high-significance measurements of excess low-frequency noise that is statistically consistent across many pulsars and independent datasets. While the data are not yet informative enough to measure the definitive evidence of gravitational waves (GW) in the form of spatial correlations between pulsars, it may be a matter of only a couple of years. As existing pulsars are timed for longer, and new pulsars are incorporated into our datasets, the data volume will continue to expand and throttle the speed of our existing detection pipelines. I will describe a radically new approach to PTA data analysis that parallelizes the GW inference over each pulsar and pieces the results together in post-processing. This renders the measurement of a GW amplitude, its signal-to-noise ratio, and cross-validation statistics, all much faster to compute by several orders of magnitude. I will describe current usage of this new pipeline in flagship analyses, and the prospects for it to be generalized even further. |
Monday, April 11, 2022 6:45PM - 6:57PM |
W16.00006: Rapid & Flexible Gravitational-wave Background Characterization With Pulsar Timing Arrays William Lamb, Stephen R Taylor, Xavier Siemens, Michele Vallisneri, Joseph D Romano Nanohertz-frequency gravitational waves (GW) are expected from various astrophysical and cosmological sources, such as supermassive black-hole binaries, cosmic strings, and cosmological phase transitions. They can be detected by Pulsar Timing Arrays (PTAs), which search for GW-induced deviations between the expected and observed times-of-arrivals (TOAs) of radio pulses in many pulsars. Using Bayesian inference, we conduct our analysis by fitting GW models from various GW sources to PTA timing residuals. Current techniques are cumbersome, with each analysis taking days or even weeks. As PTAs collect more data from more pulsars, current analysis timescales will only slow down. In this talk, I will introduce the Generalised Factorised Likelihood (GFL) method and how it will revolutionise PTA analyses. It is a fast and flexible Bayesian technique to compress and analyse PTA data, accurately characterising the spectral properties from various GW phenomena to place limits on GW models, all while decreasing analysis times to just a few minutes. |
Monday, April 11, 2022 6:57PM - 7:09PM |
W16.00007: Searches for Gravitational Waves from Supermassive Black Hole Binaries using Hamiltonian Monte Carlo Gabriel Freedman Pulsar timing arrays (PTAs) can detect low-frequency gravitational waves by looking for correlated deviations in pulse arrival times. Current Bayesian searches using PTAs are hampered by the large number of parameters needed to be sampled concurrently with Markov Chain Monte Carlo methods. As the data span increases, this problem will only worsen. An alternative Monte Carlo sampling method, Hamiltonian Monte Carlo (HMC), utilizes Hamiltonian dynamics to produce sample proposals informed by first-order gradients of the model likelihood. This in turn allows it to converge faster to high dimensional distributions. We implement HMC as an alternative sampling method in our search for an isotropic stochastic gravitational wave background, and present the accuracy and efficiency of the algorithm for this analysis. We also discuss implications of tailoring this algorithm to additional gravitational wave searches. |
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