The repeating FRB 121102 (the "repeater") shows repetitive bursting activities and was localized in a host galaxy at z = 0.193. On the other hand, despite dozens of hours of telescope time spent on follow-up observations, no other FRBs have been observed to repeat. Yet, it has been speculated that the repeater is the prototype of FRBs, and that other FRBs should show similar repeating patterns. Using the published data, we compare the repeater with other FRBs in the observed time interval (∆t) -flux ratio (S i /S i+1 ) plane. We find that whereas other FRBs occupy the upper (large S i /S i+1 ) and right (large ∆t) regions of the plane due to the non-detections of other bursts, some of the repeater bursts fall into the lower-left region of the plot (short interval and small flux ratio) excluded by the non-detection data of other FRBs. The trend also exists even if one only selects those bursts detectable by the Parkes radio telescope. If other FRBs were similar to the repeater, our simulations suggest that the probability that none of them have been detected to repeat with the current searches would be ∼ (10 −4 − 10 −3 ). We suggest that the repeater is not representative of the entire FRB population, and that there is strong evidence of more than one population of FRBs.

The detection of seven fast radio bursts (FRBs) has recently been reported. FRBs are short duration (∼1 ms), highly dispersed radio pulses from astronomical sources. The physical interpretation for the FRBs remains unclear but is thought to involve highly compact objects at cosmological distance. It has been suggested that a fraction of FRBs could be physically associated with gamma-ray bursts (GRBs). Recent radio observations of GRBs have reported the detection of two highly dispersed short duration radio pulses using a 12 m radio telescope at 1.4 GHz. Motivated by this result, we have performed a systematic and sensitive search for FRBs associated with GRBs. We have observed five GRBs at 2.3 GHz using a 26 m radio telescope located at the Mount Pleasant Radio Observatory, Hobart. The radio telescope was automated to rapidly respond to Gamma-ray Coordination Network notifications from the Swift satellite and slew to the GRB position within ∼140 s. The data were searched for pulses up to 5000 pc cm −3 in dispersion measure and pulse widths ranging from 640 μs to 25.60 ms. We did not detect any events 6σ . An in depth statistical analysis of our data shows that events detected above 5σ are consistent with thermal noise fluctuations only. A joint analysis of our data with previous experiments shows that previously claimed detections of FRBs from GRBs are unlikely to be astrophysical. Our results are in line with the lack of consistency noted between the recently presented FRB event rates and GRB event rates.

Pulsar timing observations have revealed planets around only a few pulsars. We suggest that the rarity of these planets is due mainly to two effects. First, we show that the most likely formation mechanism requires the destruction of a companion star. Only pulsars with a suitable companion (with an extreme mass ratio) are able to form planets. Second, while a dead zone (a region of low turbulence) in the disk is generally thought to be essential for planet formation, it is most probably rare in disks around pulsars because of the irradiation from the pulsar. The irradiation strongly heats the inner parts of the disk pushing the inner boundary of the dead zone out. We suggest that the rarity of pulsar planets can be explained by the low probability for these two requirements -a very low-mass companion and a dead zone -to be satisfied.

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts (FRBs) within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80-90% of the candidates, with an accuracy greater than 98%, leaving only the 10-20% most promising candidates to be reviewed by humans.