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rotating radio transients ( rrats ) were first discovered by mclaughlin  ( * ? ? ?
* ( 2006 ) ) and are characterized by occasional single radio pulses , with no underlying periodicity easily detected , in contrast to typical rotation - powered pulsars .
the discovery of rrats was surprising and important as it revealed a class of neutron stars that had previously been missed by pulsar surveys , thereby suggesting a potentially enormous increase in the inferred neutron - star birthrate .
moreover the discovery raised the issue of why some neutron stars show such sporadic emission , whereas others are ` on ' continuously .
rrats may exhibit up to @xmath01000 periods of separation between detected pulses , and as such are not detected in periodicity searches .
furthermore , observational biases against detecting rrats suggest that these sources represent a significant fraction of radio - active galactic neutron stars , yet the current known population is very small . today
@xmath070 rrats are known however the vast majority do not yet have their basic properties measured .
those rrats whose properties have been measured tend to have longer periods and are mostly seen to lie between the regular pulsars and the magnetars , as shown in the @xmath1 diagram in figure  [ fig : ppdot ] .
this may suggest an evolutionary link between rrats and other pulsar classes , but with such a small known rrat population , it is difficult to draw conclusions regarding their place within the global pulsar population .    since the initial discovery of the first 11 rrats , there has been a major paradigm shift in radio pulsar searches .
single - pulse searches are now routine and a large number of new rrat discoveries are being made ( e.g. ( * ? ? ?
* keane  2010 ) ; @xcite ) . on the other hand ,
a lot of human involvement is required in examining the output of single - pulse searches in order to look for rrat - like patterns .
this , along with the already present biases against finding rrats , makes expanding and studying the rrat population a difficult task .
hence , it is imperative to make automated rrat searches a routine part of radio pulsar searching .
we have developed new post - processing techniques for automatically identifying single bright astrophysical pulses , such as those emitted from rrats . in implementing this search algorithm on data from the green bank telescope 350 mhz drift - scan survey
, we have discovered 33 new rrat candidates , of which 6 have already been confirmed .
here we describe these techniques and present our discoveries .
diagram for all known neutron stars outside of globular clusters .
the 18 rrats with measured @xmath2 are indicated by triangles , and dots represent other neutron stars .
lines of constant magnetic field are solid and lines of constant characteristic age are dashed .
as can be seen , these 18 rrats tend to have longer periods and larger magnetic fields than the bulk of long - period pulsars.,width=283 ]
we have searched for rrats in data from the green bank telescope ( gbt ) 350 mhz drift - scan survey .
this survey was conducted over the summer of 2007 , while the azimuth track of the gbt was undergoing repairs , and thus the telescope was stationary at several fixed elevations , collecting data as the sky drifted over it .
the data were analyzed in 140-s sections , corresponding roughly to the time it takes a point on the sky to pass through the full width half maximum of the telescope beam .
this survey produced 134 tb of data , covering over 10,000 square degrees of the sky , and has yielded 34 pulsars thus far . for more information on this survey , see @xcite and @xcite .
these data were then processed using the presto software suite ( @xcite ) , which included radio frequency interference ( rfi ) excision , de - dispersion , searches for periodic signals in the fourier domain , and single - pulse searches . in single - pulse searches , signals in the de - dispersed time series that deviate from the mean
significantly are identified , and information such as signal - to - noise , time , dispersion measure ( dm ) , and pulse width is recorded .
diagnostic  single - pulse plots " summarizing this information are then produced for each beam and saved for human inspection . in this survey ,
the data were divided into 30,000 140-s  pointings " , producing 120,000 such diagnostic plots ( 4 plots per pointing , each plot spanning a different dm range ) that then required visual examination .
this is a tedious , time - consuming task , and can thus be a bottleneck in discovering single - pulse sources .
we have developed an automated search algorithm in order to identify rrat candidates in the output of single - pulse searches described above , eliminating the need for manual inspection of each diagnostic plot produced .
our algorithm is based on the following concepts :    1 .
[ dmrange ] a bright signal will be detected over a range of dms , with the strongest detection at the optimal dm and weaker detections above and below this dm .
[ rfidm0 ] since signals are strongest at the optimal dm , we expect that signals of terrestrial origin ( namely rfi ) will peak at a dm of 0 .
we can thus classify any signals that peak at dm@xmath00 as not astrophysical and reject them .
concept ( [ dmrange ] ) means that a given pulse , whether astrophysical or not , will be associated with many statistically significant  single - pulse events " that will be found in the single - pulse search .
these events will be spread over a small range of dms , and will occur at approximately the same time .
the first step in our algorithm is thus to * group * events that belong to the same pulse by checking whether they satisfy this criterion , that is , lie within some small window of dm and time .
once the single - pulse events in a beam have been divided into groups , we * examine each group s collective properties * in order to decide whether it behaves like an astrophysical pulse , and * rate * it based on these results .
the first test we employ is group size .
if a group has too few events , we classify it as noise .
the following test examines signal - to - noise vs. dm behaviour . from concept ( [ rfidm0 ] )
, we expect that a group of events that is due to an rfi signal will have a peak signal - to - noise at dm@xmath00 .
thus , any groups that satisfy this criterion are classified as rfi .
finally , we again make use of concept ( [ dmrange ] ) by looking for groups that have peak signal - to - noise at some given ( non - zero ) dm , which then decreases above and below that given dm .
these groups are classified as likely astrophysical pulses , and depending on the peak signal - to - noise attained , are classified as  likely " to  very likely " pulses .
once the beams have all undergone this search algorithm , those that have been flagged as having  very likely " pulses are visually examined . for those beams that indeed look like astrophysical sources ,
we then generate  waterfall plots " , which are frequency vs. time plots that show arrival times of the signal in different frequency bins .
since astrophysical signals experience a frequency - dependent dispersive delay while propagating through the interstellar medium , we use these waterfall plots as a final means of testing the astrophysical nature of a signal , before deciding whether it is a candidate worthy of follow - up .
we have processed all 30,000 pointings of the gbt drift - scan survey with the search algorithm described above , resulting in a total of 33 rrat candidates . of the 7 candidates that have been followed up , 6 have been confirmed .
the remaining sources look extremely promising , based on their strong single - pulse detections , as well as dispersive behaviour shown in waterfall plots . it should be noted that , remarkably , the number of rrat candidates found is comparable to that of regular pulsars ( 34 ) found in the drift - scan survey .
figure  [ fig:1537 ] shows an example discovery and confirmation plot of one of the new rrat sources .
the strength of the algorithm is illustrated in these plots : the pulses in the discovery observation may easily be confused with rfi when examined by eye , and this beam would have potentially been dismissed if it were only examined visually .
the algorithm , however , successfully distinguished between the astrophysical pulses and the rfi in this beam , and correctly identified this source as an rrat candidate .
15  pc  @xmath3 , starting at t@xmath037  s , and various rfi spikes throughout . on the right , many strong pulses are seen , again at dm@xmath015  pc  @xmath3 .
the top right panels of each diagram demonstrate the signal - to - noise vs. dm behaviour expected for an astrophysical pulse , as described in section  [ algorithm].,title="fig:",width=240 ] 15  pc  @xmath3 , starting at t@xmath037  s , and various rfi spikes throughout . on the right
, many strong pulses are seen , again at dm@xmath015  pc  @xmath3 .
the top right panels of each diagram demonstrate the signal - to - noise vs. dm behaviour expected for an astrophysical pulse , as described in section  [ algorithm].,title="fig:",width=240 ]
we hope to begin timing the confirmed rrat sources soon , allowing us to measure their basic parameters and place them on the @xmath1 diagram
. we will also follow up the remaining candidates , and begin timing observations of those that are confirmed .
we are also working on making improvements to the search algorithm that will allow us to identify weaker pulses in data , as well as make the algorithm more robust to rfi in order to reduce the number of false positives . finally ,
since this code is not specific to the drift - scan survey it may be applied to other pulsar surveys .
indeed , in the coming weeks it will be implemented in the ongoing green bank north celestial cap survey . applying the search algorithm to this extensive sky survey
promises to yield many more rrat sources , since this survey will have improved sensitivity and will cover an area far larger than that of the gbt drift - scan survey .
we hope that with this new automated search algorithm , the known rrat population will quickly grow , allowing us to study more of these fascinating objects and find their place within the global pulsar population .
rotating radio transients ( rrats ) are a class of pulsars characterized by sporadic bursts of radio emission , which make them difficult to detect in typical periodicity - based pulsar searches . 
 using newly developed post - processing techniques for automatically identifying single bright astrophysical pulses , such as those emitted from rrats , we have discovered approximately 30 new rrat candidates in data from the green bank telescope 350 mhz drift - scan survey . 
 a total of 6 of these have already been confirmed and the remainder look extremely promising . here 
 we describe these techniques and present the most recent results on these new rrat candidates .