The NANOGrav 12.5-year Data Set: Chromatic Noise Characterization & Mitigation with Time-Domain Kernels
Authors
Jeffrey S. Hazboun
Joseph Simon
Jeremy Baier
Bjorn Larsen
Daniel J. Oliver
Paul T. Baker
Bence Bécsy
Siyuan Chen
Alberto Diaz Hernandez
Justin A. Ellis
A. Miguel Holgado
Kristina Islo
Aaron Johnson
Andrew R. Kaiser
Nima Laal
Alexander McEwen
Nihan S. Pol
Joey Shapiro Key
Min Young Kim
Matthew Samson
Brent J. Shapiro-Albert
Jerry P. Sun
Stephen R. Taylor
Caitlin A. Witt
Jeremy Volpe
Christine Ye
Harsha Blumer
Paul R. Brook
Shami Chatterjee
James M. Cordes
Fronefield Crawford
H. Thankful Cromartie
Megan E. DeCesar
Paul B. Demorest
Timothy Dolch
Robert D. Ferdman
Elizabeth C. Ferrara
William Fiore
Emmanuel Fonseca
Nathan Garver-Daniels
Peter A. Gentile
Deborah C. Good
Ross J. Jennings
Megan L. Jones
David L. Kaplan
Michael T. Lam
T. Joseph W. Lazio
Duncan R. Lorimer
Jing Luo
Ryan S. Lynch
Dustin R. Madison
Maura A. McLaughlin
Chiara M. F. Mingarelli
Cherry Ng
David J. Nice
Timothy T. Pennucci
Scott M. Ransom
Paul S. Ray
Xavier Siemens
Renée Spiewak
Ingrid H. Stairs
Daniel R. Stinebring
Kevin Stovall
Joseph K. Swiggum
Jacob E. Turner
Michele Vallisneri
Sarah J. Vigeland
Abstract
Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gaussian processes to model the GWB and intrinsic long time-correlated (red) noise, techniques to model the time-correlated radio frequency-dependent (chromatic) processes have varied from collaboration to collaboration. Here we test a new class of models for PTA data, Gaussian processes based on time-domain kernels that model the statistics of the chromatic processes starting from the covariance matrix. As we will show, these models can be effectively equivalent to Fourier-domain models in mitigating chromatic noise. This work presents a method for Bayesian model selection across the various choices of kernel as well as deterministic chromatic models for non-stationary chromatic events and the solar wind. As PTAs turn towards high frequency (>1/yr) sensitivity, the size of the basis used to model these processes will need to increase, and these time-domain models present some computational efficiencies compared to Fourier-domain models.
