Hierarchical Bayesian estimation of population-level torque law parameters from anomalous pulsar braking indices

Abstract

Abridged. Stochastic fluctuations in the spin frequency of a rotation-powered pulsar affect how accurately one measures the power-law braking index, n pl, defined through =Kn pl, and can lead to measurements of anomalous braking indices, with n = / 2 1, where the overdot symbolizes a derivative with respect to time. Previous studies show that the variance of the measured n obeys the predictive, falsifiable formula n2 = n pl2+σ22γ-2-4T obs-1 for K=0, where σ is the timing noise amplitude, γ-1 is a stellar damping time-scale, and T obs is the total observing time. Here we combine this formula with a hierarchical Bayesian scheme to infer the population-level distribution of n pl for a pulsar population of size M. The scheme is validated using synthetic data. For a plausible test population with M=100 and injected n pl values drawn from a population-level Gaussian with mean μ pl=4 and standard deviation σ pl=0.5, intermediate between electromagnetic braking and mass quadrupole gravitational radiation reaction, the Bayesian scheme infers μ pl=3.89+0.24-0.23 and σ pl=0.43+0.21-0.14. The M=100 per-pulsar posteriors for n pl and σ2γ-2 contain 87\% and 69\%, respectively, of the injected values within their 90\% credible intervals. Comparable accuracy is achieved for (i) population sizes spanning the range 50 ≤ M ≤ 300, and (ii) wide priors satisfying μ pl ≤ 103 and σ pl ≤ 102, which accommodate plausible spin-down mechanisms with K≠0.

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