Have we seen all glitches?

TL;DR

This paper discusses the detection probability density of neutron star glitches in pulsar timing data, emphasizing its importance for understanding glitch detectability with specific observation setups.

Contribution

It extends previous work by further analyzing the detection probability density of glitches in pulsar data, providing deeper insights into glitch detectability.

Findings

Detection probability density is crucial for glitch detection.

Extended analysis of detection probability density in pulsar data.

Implications for optimizing pulsar observation schedules.

Abstract

Neutron star glitches are observed via artificially scheduled pulsar pulse arrival-time observations. Detection probability density of glitch events for a given data set is essentially required knowledge for realizing glitch detectability with specified observing system and schedule. In Yu & Liu, the detection probability density was derived for the Yu et al. data set. In this proceeding, further discussions are presented.

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Hundreds of pulsars have been being observed at the Parkes Observatory over decades via a number of timing programmes. Search for pulse frequency glitches for a data set that contains an entire 1911 yr observations over 165 pulsars was done by Yu et al. (2013). The result is 107 glitches were identified in 36 pulsars. For the Yu et al. (2013) data set, glitch identification depends on the observing cadences and the observed timing noise. Yu & Liu (2017) thus defined the complete probability formula of identifying glitch (see Eq. 1 therein). They found the derived detection probability densities for both the group and individual cases are not uniform; as glitch becomes larger the density increases (see Figs. 5, 7 and 8 therein). The high-cadence observations for the Crab pulsar showed the $\Delta\nu$ values of glitches are significantly larger than those of timing noise (Espinoza et al., 2014). These imply the cadences with which Yu et al. (2013) pulsars were observed were not adequate to observe all glitches occurred.

In Fig. 1, average pulse time-of-arrival (ToA) interval for each of the 165 pulsars is plotted on the $P-\dot{P}$ diagram. Symbol size is a linear function of the value with positive slope. For the non-glitching pulsars, the averages range between 10.4 d for PSR J1359$-$6038 and 798.7 d for PSR J1047$-$6709; most averages are a few tens of days. For the glitching pulsars, the averages range between 8.3 d for the Vela pulsar and 240.6 d for PSR J1740$-$3015. For PSR J1740$-$3015, the low observing cadences result in the unidentification of thirteen glitches in the Yu et al. (2013) data. PSR J1341$-$6220 which shows seventeen glitches was on average observed every 23.5 d. In Fig. 2, ToA interval modulation index or the average over the intervals’ standard deviation for each pulsar is shown. (Symbol size is a linear function of the value with positive slope.) For the non-glitching pulsars, the indices range between 0.08 for PSR J1456$-$6843 and 1.20 for PSR J1721$-$3532. For the glitching pulsars, the indices range between 0.06 for PSR J1105$-$6107 and 1.12 for PSR J1531$-$5610. PSR J1341$-$6220 has 0.73. In Fig. 3, amplitude of power spectral density of observed timing noise is shown. (Symbol size is a linear function of the value with positive slope.) PSR J1341$-$6220 has the maximum amplitude in the sample. In general, young pulsars show large timing noise while timing noise does not seem to correlate with magnetic field.