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Sub-parsec precision measurement of pulsar distances with nanohertz gravitational waves

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abstract

The recent evidence of nanohertz (nHz) gravitational wave (GW) background by pulsar timing array (PTA) collaborations has sparked considerable interest in understanding its astrophysical origins, particularly regarding supermassive black hole binaries (SMBHBs). In this work, we focus on individual SMBHBs that will be hopefully detected in upcoming PTA observations. The effect of nHz GWs on the pulse arriving times is in general decomposed as a pulsar term and an Earth term, where the pulsar term encodes the pulsar-Earth distance as a phase shift relative to the Earth term, but is usually treated as an extra noise source since the pulsar distance is in general not well measured with uncertainty larger than the wavelength of nHz GWs. We propose that the pulsar distance could be constrained by combining the phase information of multiple SMBHBs that are individually resolved. Using Markov chain Monte Carlo (MCMC) simulations, we demonstrate that the pulsar distances can be measured to better than $0.4$ pc (1 pc) for pulsars at $D\sim 1$ kpc ($\sim 2.2$ kpc) with 30 years of observations by a 20-pulsar PTA with a noise level of $\sigma_{\rm n}=20$ ns in the Square Kilometre Array (SKA) era.

fields

astro-ph.HE 1

years

2026 1

verdicts

CONDITIONAL 1

representative citing papers

Population statistics of nanohertz gravitational wave sources

astro-ph.HE · 2026-07-08 · conditional · novelty 6.0

A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.

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Showing 1 of 1 citing paper.

  • Population statistics of nanohertz gravitational wave sources astro-ph.HE · 2026-07-08 · conditional · none · ref 92 · internal anchor

    A hierarchical Bayesian inference framework combining free-spectrum reconstruction with population-level likelihoods distinguishes finite SMBHB populations from Gaussian primordial GWB using mock PTA data.