Abstract

We present a power spectral analysis of the timing noise of the Crab pulsar, mainly using radio measurements from Jodrell Bank taken over the period 1982–89, an interval bounded by sparse data sampling and a large glitch. The power spectral analysis is complicated by non-uniform data sampling and the presence of a steep red power spectrum that can distort power spectra measurement by causing severe power ‘leakage’. We develop a simple windowing method for computing red noise power spectra of uniformly sampled data sets and test it on Monte Carlo generated sample realizations of red power-law noise. We generalize time-domain methods of generating power-law red noise with even integer spectral indices to the case of non-integer spectral indices. The Jodrell Bank pulse phase residuals are dense and smooth enough that an interpolation on to a uniform time-series is possible. A windowed power spectrum is computed, revealing a periodic or nearly periodic component with a period of 568 ± 10 d and a 1/f3 power-law noise component in pulse phase with a noise strength Sφ= (1.24 ± 0.067) × 10−16 cycle2 s−2 over the analysis frequency range f= 0.003–0.1 cycle d−1. This result deviates from past analyses which characterized the pulse phase timing residuals as either 1/f4 power-law noise or a quasiperiodic process. The analysis was checked using the Deeter polynomial method of power spectrum estimation that was developed for the case of non-uniform sampling, but has lower spectral resolution. The timing noise is consistent with a torque noise spectrum rising with analysis frequency as f, implying blue torque noise, a result not predicted by current models of pulsar timing noise. If the periodic or nearly periodic component is due to a binary companion, we find a mass function f(M) = (6.8 ± 2.4) × 10−16 M⊙ and a companion mass, Mc≥ 3.2 M⊕, assuming a Crab pulsar mass of 1.4 M⊙.

Highlights

  • Isolated rotation-powered pulsars generally exhibit a pulse frequency that can be measured to high precision and that decreases, for the most part smoothly, on long time scales, indicating a steady loss of angular momentum from the rotating neutron star

  • We focus on power spectral analysis methods in this paper to characterize the Crab pulsar timing residuals

  • We show that in the Crab pulsar, a real periodicity or quasiperiodicity is present in the power spectrum of the Crab timing residuals produced from the Jodrell Bank observations in addition to a red noise component

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Summary

Introduction

Isolated rotation-powered pulsars generally exhibit a pulse frequency that can be measured to high precision and that decreases, for the most part smoothly, on long time scales, indicating a steady loss of angular momentum from the rotating neutron star. Using a five year span of radio observations from Jodrell Bank in the 1980’s, Lyne, Pritchard & Smith (1988) claimed that the timing residuals were consistent with a physically real quasiperiodic process with a period of ∼ 20 months since the period did not scale with the length of the data being fit, as occurs with the apparent periods observed in the residuals of polynomial fits to red noise The disagreement between these various analyses has motivated this study of the timing properties of the Crab pulsar and a search for analysis techniques to better characterize the statistical nature of the timing noise. The smoothness and dense sampling of the Crab timing residuals produced from the Jodrell Bank observations makes possible an interpolation onto a uniform grid and computation of a high resolution power spectrum This new analysis shows that the pulse phase timing noise is composed of two components: a red noise process. At lower spectral resolution, using the Deeter polynomial method

The major components of the pulse phase of the Crab pulsar
Observations and computation of pulse timing residuals
Red Power-law noise processes
Power Spectral Density Estimation of Red Noise Processes
Computation of a Windowed PDS in the Presence of Red Power-Law Noise
Windowed Power Spectrum
Quasiperiodicity
The 1986 Glitch
Deeter Polynomial Power Spectral Estimation
10. Discussion
Generation of Red Power-Law Noise Using Power-Law Weighting of White Noise
Time Domain Properties
Discrete Red Power-law Noise
Findings
Generation of Red Power-Law Noise Using a Modified Timmer-Konig Method

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