6.39466 Frequency (Hz) 6.39464 6.39462 6.3946 6.39458 6.39456 6.39454? 6.39452 6.3945 42000 44000 46000 48000 50000 52000 54000 MJD (Days) Glitches in radio pulsars Cristóbal Espinoza Jodrell Bank Centre for Astrophysics
ν 10 3 ν 100 µhz!! ("Hz) 30 20 10 Glitch 0-7260 -7280 ν! (10-15 Hz s -1 ) -7300-7320 -7340-7360 -7380-1500 -1000-500 0 500 1000 1500 Days from MJD=53737
What can produce a glitch? Caused by crust rearrangements; cooling, slowdown re-shaping. (Baym et al. 1969) Caused by rapid angular momentum exchange between the inner superfluid and the Crust; result of halted vortex migration. (Anderson & Itoh 1975; Alpar et al. 1984; many many others) Caused by magnetic field stresses on the crust, driven by vortex migration. (Ruderman et al. 1998) M. Ruderman (2009) INTERNAL OR CRUST PROCESS
Glitch 100 TG -10 Glitch detected Magnetar Detections -11-12 1 TG 315 glitches in plot -13 1 Kyr 378 detected so far http://www.jb.man.ac.uk/ pulsar/glitches.html log(period derivative) -14-15 -16 100 Kyr 0.01 TG -17 ~130 pulsars -18 10 Myr -19 1 Gyr -20 100 MG -21 0.001 0.01 0.1 1 10 Period (s)
Glitch spin-up rate (integrated glitch activity) [Lyne, Shemar & Graham-Smith (2000)] Separate pulsars according to their spindown rate and estimate their spin-up rate: ν glitch = i j ν ij k T k ν j counts the glitches on pulsar i Tk is the total time for which pulsar k has been observed (and sums over ALL pulsars in the group) 622 pulsars observed for more than 3 yr
! (10-15 Hz s -1 ) 0.01 0.1 1 10 100 1000 10000 100000 1x10 6 1000 (a) Glitch spin-up rate (10-15 Hz s -1 ) 100 10 1 0.1 0.01 0.001 0.0001 0.00001 1x10-6 0.01 0.1 1 10 100 1000 10000 100000 1x10 6! N g " (yr -1 ) 1 0.1 0.01 0.001 0.0001 Glitching rate -2 0 2 4 log! (10-15 Hz s -1 ) % of! reversed by glitches 1 0.1 0.01 0.001 (b) -2 0 2 4 6 log! (10-15 Hz s -1 ) Espinoza, Lyne, Stappers & Kramer (2011) Glitch activity grows linearly with spindown rate
3 regimes young pulsars ν glitch ν ν glitch ν old pulsars pulsar evolution?
60 50 0.0001 0.001 0.01 0.1 1 10 100 315 glitches 315 glitches (smaller bin) Magnetars!! ["Hz] Bi-modal: two types of glitches? 40 Number 30 20 10 Large glitches: seen mostly in Vela-like pulsars 0-4 -3-2 -1 0 1 2 log(!!) ["Hz] (Espinoza et al. 2011) Distribution of Frequency steps
young pulsars Giant glitchers selected for showing large frequency and spindown rate steps 100 ν glitch ν 10 1 old pulsars!! ("Hz) 0.1 0.01 0.001 0.0001 Positive df1 jump Negative df1 jump 0.001 0.01 0.1 1 10 100 1000 10000!! (10-15 Hz s -1 )
Waiting time (days) Giant glitchers 10000 1000 100 Narrow size distributions. Quasi periodic behaviour. Waiting times consistent with reaching critical lag. Permanent spindown rate changes B2334+61 J0729-1448 Vela J0537-6910 1e-13 1e-12 1e-11 1e-10 1e-09!!! (10-15 Hz s -1 )!! ("Hz)!! ("Hz)! (10-15 Hz s -1 )!! ("Hz)! (10-15 Hz s -1 ) 0 0.02-1500 -1000-500 0 50010001500-1500 -1000-500 0 50010001500-1500 -1000-500 0 50010001500-1500 -1000-500 0 5001000150-88.6-88.3 0 0.005 0.01-28.7-28.5 0 2 4 6 8-8940 -8900 B1838-04 53388 J1847-0130 54784.449 0 0.05-89 -88.2 0 1 2-1177 -1176-1175 -1500-1000-500 0 50010001500-1500 -1000-500 0 50010001500-1500 -1000-500 0 5001000150 0 20 40 model should relax the rigid rotation assumption for the charged component and include the effect of Ekman pumping. Further developments -1500-1000-500 should 0 50010001500 also include -1500-1000-500 more realistic 0 50010001500-1500 -1000-500 0 5001000150 models for the drag parameters in the star, as the density dependence of the coupling strength clearly has an impact on the amount of angular momentum that can be exchanged on different timescales. Truly quantitative results could then J2021+3651 be obtained with thej2229+6114 use of realistic equations J2229+6114 of state together with consistent estimates of the pinning force, such B2334+61 54177 53064 54110 53642 as those of (Grill & Pizzochero 2011) and (Grill 2011). Note that we have assumed that a giant glitch only occurs when the maximum critical lag is reached. If unpinning could be triggered earlier, this could -1500 generate -1000-500smaller 0 50010001500-1500 -1000-500 0 5001000150 glitches. In fact cellular automaton models have shown that the waiting time and size distributions of pulsar glitches can be successfully explained by vortex avalanche dynamics, related -500 to 0 random 500 1000 unpinning -1000-500 events 0 500 1000 (Warszawski -1000-500 & Melatos 0 500 1000-1000 -500 0 500 1000-1000 2010; Days from Melatos Glitch & Warszawski Days from 2009; Glitch Warszawski Days & Melatos from Glitch Days from Glitch 2011). It would thus be of great interest to use our long-term hydrodynamical models, with realistic pinning forces, as a background for such cellular automaton models that model -2960-2920 0 10 20 30-29500 -29200 1 0.8 0.6 J1841-0524 53562 P(!!) 0.4 0.2 0 B1853+01 54123 J0631+1036 B1737-30 0 10 2-7340 -7260 0 0.1-203.15-203.05 Crab 0 0.1 0.2-28.45-28.4-28.35 0 5 10-29500 -29300 J1845-0316 52128 B1338-62 0.01 0.1 1 10 B1859+01 51318!! ["Hz] Modelling pulsar glitches 13 0 10-805 -800-795 0 0.2 0.4-203.5-203.1 0 0.1 0.2 0.3-255 -250 0 20 40-800 -790-780 J1845-0316 54170 Vela J1913+0832 54653.908.! (Hz/s) (Haskell et al. 2012) (Espinoza et al. 2011) Figure 14. We plot the approximate waiting time between glitches for the pulsars that have shown multiple giant glitches, as afunctionofthespindownrate.wealsoincludetwopulsarsthat J0537-6910
CRAB ν(t) VELA ν(t) RESIDUALS RESIDUALS ~36 YR ~14 YR ν PERMANENT CHANGES ( ) THE CRAB AND VELA PULSARS ν p
Small glitches 100 Meet detection issues 10 Present in all pulsars, but preferably in low spin-down rate ones ν jumps appear smaller...!! ("Hz) 1 0.1 0.01 7 days 1 day Do not seem to show significant permanent steps. ν Small glitches with large ν jumps may go undetected 0.001 0.0001 0.001 ph 0.0001 ph 0.001 0.01 0.1 1 10 100 1000 10000!! (10-15 Hz s -1 )
Residuals for ν = 0.003 µhz (ν, ν fit until glitch epoch) 10 Residuals for ν = 0.003 µhz (ν, ν fit entire data span) fit until glitch ν = 0.00 ν = 0.01 ν = 0.1 Increasing ν 10 15 ν = 0.3 ν = 1.0 overall fit ν = 0.00 ν = 0.1 ν = 0.01 Increasing ν 10 15 Hz s 1 Residuals for ν = 0.0 µhz ν = 0.00 ν = 0.01 (ν, ν fit until glitch epoch) ν = 0.3 ν = 0.1 Residuals for ν = 0.0 µhz ν = 0.00 ν = 1.0 ν = 0.01 ν = 1.0 Hz s 1 ν > 0 Timing noise ν = 0.3 & ν < 0 (ν, ν fit entire data span) ν = 0.1 ν = 0.3 ν = 1.0 ν < 0 ν > 0 ν = 0.00 ν = 0.01 ν = 0.1 ν = 0.3 ν = 0.3 ν = 0.01 ν = 0.00 ν = 0.1August 2012 IAU Symposium 291 Beijing, ν = 1.0 Timing noise refers to unmodelled deviations from a simple spindown model, appearing as a random wandering of the phase residuals. As pictured by Lyne+2010, most timing noise could be caused by changes, with both signs, of the spindown rate. ν = 1.0 Glitches and timing noise can be confused. Small glitches might remain undetected because they look like timing noise and timing noise might be confused with glitches (and reported as glitches with unusual properties). Characterising glitches and timing irregula in pulsars and magnetars C. M. Espinoza1*, D. Antonopoulou2^, B. W. Stappers1, A. Watts2 ν > 0 Glitches Study of the small glitches population is complicated Glitches are unresolved positive steps in pulsar spin because of accompanied contamination other phenomena frequency, normally by a changeby in the spindown Here is w 0 rate. After some glitches the frequency and the spin-down rate relax back to the pre-glitch state on timescales of up to ~100 days. These events are thought to be caused by the interaction of the neutron star crust with the internal neutron superfluid. Many recent detections of small events show unusual f Glitch ν JUMPS BECOMES IMPORTANT Phase residuals UNDERSTANDING OF -0.005-0.01 tup -0.015-0.02-0.025 φm
Summary: what we know about glitches All pulsars can glitch. But different pulsars glitch different. Pulsar with faster spindown rates glitch more often. The cumulative pulsars with large ~%1 of the spindown rate. ν ν ν effect over time is larger for (linearly) and it represents -distribution is bimodal. Some pulsars only exhibit large glitches.
Future work...or in progress Any trend involving improve error determinations. ν -jumps? Re-measure and Are all glitches followed by a permanent ν change? How is the size distribution towards lower end?
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