Analysis Of Glitch Activity In Rotating Neutron Stars

ABSTRACT

A total of 660 discrete jumps in the rotation frequency (ı) and the spin-down rate (ıı) of

about 140 pulsars were studied. Out of the 660 discrete jumps, 394 were classical glitches

(the so-called macroglitches) and 266 were microglitches. The objects are grouped into

normal radio pulsars, anomalous x-ray pulsars and recycled millisecond pulsars. A bimodal

distribution was observed in many of the pulsar glitch parameters, namely the discrete

absolute fractional jumps in the rotation frequency (| Δııı|), the entire absolute discrete

jumps in the spin down rate (|Δıı|), cumulative of the absolute jumps in the rotation frequency

(Σ|Δı|), cumulative of the absolute fractional jumps in rotation frequency (Σ| Δııı|) for

macroglitches may suggest that glitch events may be triggered by dual glitch mechanism.

The distribution of the entire absolute discrete fractional jumps in the rotation frequency

(|Δııı|), cumulative of the absolute jumps in the rotation frequency (Σ|Δı|) and the

cumulative of the absolute jumps in spin down rate (Σ|Δıı|) of microglitches equally suggests

that a glitch event is triggered by one mechanism. It was observed that some of the

macroglitches have magnitudes in ı (rotation frequency) which overlapped with the

microglitches completely which suggest that some of the rotational jumps that was

characterized as macroglitches by previous authors should have been recorded as

microglitches since their glitch magnitude Δııı < 10ıı. The distribution of the glitches

over the spin down parameters shows that pulsars with characteristic age 3 – 4, rotational

frequency of ~ 0.4 − 0.9, spin down rate ~ − 13 − (− 12) and surface magnetic field

strength of 12 − 13 on logarithmic scales exhibit the highest frequency of macroglitches

while those within the characteristic age 5 − 6 , rotational frequency of ~ − 0.1 − 0.4 , spin

down rate of − 14 – (− 13) and surface magnetic field strength of 11 − 12 on logarithmic

scales exhibit the highest frequency of microglitches. From the regression analysis, it was

observed that there was a strong positive linear relationship between (Σ |Δı|) − (Σ|Δıı|)for

the macroglitches and microglitches data when analysed separately and jointly. There was no

correlation between (Σ |Δı|) – ıdata for both samples. On the otherhand, there was a strong

(Σ |Δı|) – |ıı| correlation for the macroglitches and microglitches data when analysed