Search Results for author: Michele Vallisneri

Found 5 papers, 3 papers with code

The NANOGrav 12.5 yr Data Set: Observations and Narrowband Timing of 47 Millisecond Pulsars

no code implementations13 May 2020 Md F. Alam, Zaven Arzoumanian, Paul T. Baker, Harsha Blumer, Keith E. Bohler, Adam Brazier, Paul R. Brook, Sarah Burke-Spolaor, Keeisi Caballero, Richard S. Camuccio, Rachel L. Chamberlain, Shami Chatterjee, James M. Cordes, Neil J. Cornish, Fronefield Crawford, H. Thankful Cromartie, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, Justin A. Ellis, Robert D. Ferdman, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Yhamil Garcia, Nathan Garver-Daniels, Peter A. Gentile, Deborah C. Good, Jordan A. Gusdorff, Daniel Halmrast, Jeffrey Hazboun, Kristina Islo, Ross J. Jennings, Cody Jessup, Megan L. Jones, Andrew R. Kaiser, David L. Kaplan, Luke Zoltan Kelley, Joey Shapiro Key, Michael T. Lam, T. Joseph W. Lazio, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Dustin Madison, Kaleb Maraccini, Maura A. McLaughlin, Chiara M. F. Mingarelli, Cherry Ng, Benjamin M. X. Nguyen, David J. Nice, Timothy T. Pennucci, Nihan S. Pol, Joshua Ramette, Scott M. Ransom, Paul S. Ray, Brent J. Shapiro-Albert, Xavier Siemens, Joseph Simon, Renee Spiewak, Ingrid H. Stairs, Daniel R. Stinebring, Kevin Stovall, Joseph K. Swiggum, Stephen R. Taylor, Michael Tripepi, Michele Vallisneri, Sarah J. Vigeland, Caitlin A. Witt, Weiwei Zhu

We detail our observational methods and present a set of TOA measurements, based on "narrowband" analysis, in which many TOAs are calculated within narrow radio-frequency bands for data collected simultaneously across a wide bandwidth.

Time Series Analysis High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics

Learning Bayesian posteriors with neural networks for gravitational-wave inference

1 code implementation12 Sep 2019 Alvin J. K. Chua, Michele Vallisneri

To do so, we train a deep neural network to take as input a signal + noise data set (drawn from the astrophysical source-parameter prior and the sampling distribution of detector noise), and to output a parametrized approximation of the corresponding posterior.

Astronomy Bayesian Inference

Reduced-order modeling with artificial neurons for gravitational-wave inference

1 code implementation13 Nov 2018 Alvin J. K. Chua, Chad R. Galley, Michele Vallisneri

Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images.

Time Series Time Series Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.