4.7 Article

A new small glitch in Vela discovered with a hidden Markov model

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 522, Issue 4, Pages 5469-5478

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stad1335

Keywords

stars: neutron; pulsars: individual: Vela; stars: rotation

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A study is conducted to better understand the small glitches in Vela pulsar using high-cadence observations from the Mount Pleasant Observatory. A hidden Markov model is utilized to search for previously undetected glitches and estimates their parameters. The study also sets upper limits on the sizes of missed glitches, providing crucial information for studying small glitches.
A striking feature of the Vela pulsar (PSR J0835 -4510) is that it undergoes sudden increases in its spin frequency, known as glitches, with a fractional amplitude of the order of 10(-6) approximately every 900 d. Glitches of smaller magnitudes are also known to occur in Vela. Their distribution in both time and amplitude is less well constrained but equally important for understanding the physical process underpinning these events. In order to better understand these small glitches in Vela, an analysis of high-cadence observations from the Mount Pleasant Observatory is presented. A hidden Markov model (HMM) is used to search for small, previously undetected glitches across 24 yr of observations co v ering MJD 44929 to MJD 53647. One previously unknown glitch is detected around MJD 48636 (1992 January 15), with fractional frequency jump Delta f/f= (8.19 +/- 0.04) x 10(-10) and frequency deri v ati ve jump Delta<(f)over dot>/<(f)over dot> = (2 . 98 +/- 0 . 01) x 10 (-4) . Two previously reported small glitches are also confidently redetected, and independent estimates of their parameters are reported. Excluding these events, 90 per cent confidence frequentist upper limits on the sizes of missed glitches are also set, with a median upper limit of A Delta f(90%)/f = 1.35 x 10 (-9). Upper limits of this kind are enabled by the semi-automated and computationally efficient nature of the HMM, and are crucial to informing studies that are sensitive to the lower end of the glitch size distribution.

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