Bayesian Solar Wind Modeling with Pulsar Timing Arrays

TL;DR

This paper develops a Bayesian framework to analyze solar electron density using pulsar timing data, revealing detailed solar wind properties and improving gravitational-wave data analysis.

Contribution

Introduces new Bayesian tools and models for solar wind analysis within pulsar timing arrays, enabling detailed characterization of solar electron density variations.

Findings

Recovered solar wind parameters from PTA data.

Detected higher order corrections to the $1/r^2$ wind model.

Provided continuous solar electron density measurements over a solar cycle.

Abstract

Using Bayesian analyses we study the solar electron density with the NANOGrav 11-year pulsar timing array (PTA) dataset. Our model of the solar wind is incorporated into a global fit starting from pulse times-of-arrival. We introduce new tools developed for this global fit, including analytic expressions for solar electron column densities and open source models for the solar wind that port into existing PTA software. We perform an ab initio recovery of various solar wind model parameters. We then demonstrate the richness of information about the solar electron density, $n_E$, that can be gleaned from PTA data, including higher order corrections to the simple $1/r^2$ model associated with a free-streaming wind (which are informative probes of coronal acceleration physics), quarterly binned measurements of $n_E$ and a continuous time-varying model for $n_E$ spanning approximately one solar cycle period. Finally, we discuss the importance of our model for chromatic noise mitigation in gravitational-wave analyses of pulsar timing data and the potential of developing synergies between sophisticated PTA solar electron density models and those developed by the solar physics community.