Effects of periodicity in observation scheduling on parameter estimation of pulsar glitches
Liam Dunn, Marcus E. Lower, Andrew Melatos

TL;DR
This paper investigates how periodic observation scheduling affects pulsar glitch parameter estimation, revealing biases in traditional methods and proposing models that improve glitch detection and measurement accuracy.
Contribution
It introduces a systematic analysis of degeneracies caused by periodic scheduling and evaluates new approaches like hidden Markov models for unbiased glitch estimation.
Findings
Bias towards small glitches in traditional timing methods.
Local frequency estimates reduce bias.
Hidden Markov models detect multiple solutions and improve glitch size recovery.
Abstract
In certain pulsar timing experiments, where observations are scheduled approximately periodically (e.g. daily), timing models with significantly different frequencies (including but not limited to glitch models with different frequency increments) return near-equivalent timing residuals. The average scheduling aperiodicity divided by the phase error due to time-of-arrival uncertainties is a useful indicator of when the degeneracy is important. Synthetic data are used to explore the effect of this degeneracy systematically. It is found that phase-coherent tempo2 or temponest-based approaches are biased sometimes toward reporting small glitch sizes regardless of the true glitch size. Local estimates of the spin frequency alleviate this bias. A hidden Markov model is free from bias towards small glitches and announces explicitly the existence of multiple glitch solutions but sometimes…
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