6.8 Appendix A
8.3.5 Conformity, información y votación sincera
Sifting
After each of the dedispersed time series has passed through the periodicity (and, optionally, acceleration) searching pipeline, the detected suspects are recorded into a list. A typical representative of this list is characterised by a spin frequency/period, DM, acceleration (if relevant) and spectral S/N corresponding to each harmonic summation round. The exact number of candidates generated in one observation varies for a particular survey, depending on the technical facilities available (antenna gain, receiver bandwidth, etc.), integration time, local RFI situation and detection thresh- olds. For modern large-scale surveys it is counted in tens of thousands, while the total expected outputs (upon completion) reach millions of suspects! Only a small frac- tion of these suspects will contribute to pulsar detections. To reduce the number of
2.2. Working with data: pulsar searching 43
Figure 2.8: A set of diagnostic plots produced in the result of folding a 3-minute observation containing PSR J1730−3350 with P=139.94 ms and DM=261.7 pc cm−3. The data are taken
within the HTRU-North project.
candidates that will proceed to the next stages of the analysis, the procedure of sifting should be performed. It typically includes rejection of periodicities that have:
1) low S/N (beyond the survey’s estimated detection threshold);
2) periods not expected for real pulsars (e.g. P >15 s and P <0.5 ms); 3) very low DMs (DM<1–2 pc cm−3).
Furthermore, the same pulsar may show up in different harmonics and at multiple trial DM and acceleration values – these duplicate periodicities also need to be removed. Once this is finished, the “survivors” of sifting are ranked by their spectral S/N and stored in a new list.
Folding
Next the top candidates are folded. For this the original filterbank file is dedispersed at the candidate’s DM and the resultant de-dispersed time series is divided into nsi
identical segments – subintegrations – containing an integer number m of candidate’s pulse periods P. (If the number of time samples at the end of the time series is not enough to form a whole period, these time samples are cut, and the whole time series has a slightly reduced length t∗obs.) In other words, each subintegration has a length tsi = t′obs/nsi = mp. Each of m time intervals P is, in turn, divided into
nbinequally spaced elements (time bins) corresponding to particular rotational phases. Thus, the whole time series is now represented as an array of intensity values: aikn,
wherei= 1,2, ..., nbin;k= 1,2, ..., m;n= 1,2, ...nsi. As a next step, the averaged sum
of values across k is calculated for every subintegration. This results in a new array of elements ˜ain which can also be considered as a sequence of nsisubintegrated pulse
profiles, each made of nbin time bins. These subintegrated profiles already contain a
much stronger signal than does a time interval corresponding to a single pulse period: the pulsar signal increases proportionally to the number of summed elements, while the increase in noise follows the square root law. Further summation of elements across n gives the last arrayAi, the intensity averaged over the whole observation as a function
of pulsar’s rotational phase. This is referred as the integrated pulse profile.
Candidate selection
The outcome of folding is a set of diagnostic plots (see Fig.2.8) containing information about the integrated pulse profile, DM−S/N and acceleration−S/N or ˙P−S/N curves, as well as about the signal’s presence in particular subbands/subintegrations (obser- vation time−pulse phase, frequency−pulse phase plots). Some peculiar features in these plots can help to distinguish a true pulsar signal from RFI. For example, though genuine pulsar pulse profiles can differ by their exact morphology, they are unlikely to have a pure sinusoidal or sawteeth form – such pattern is a telltale sign of signal’s industrial origin. Further, since pulsars are known to emit in a broad range of frequen- cies, the true signal should cover the whole (effective) observing bandwidth. And vice versa: a narrowband trace in the frequency−pulse phase plot can definitely rule the candidate out. Additionally, the majority of pulsars, excluding scintillating ones and RRATs, are expected to appear persistently during the whole observation, unlike, for example, space-time coded terrestrial signals. Finally, a noticeable peak centred at a non-zero value in the DM−S/N curve may be a testimony of the signal’s extraterres- trial origin. However, it should be noticed that a strong RFI very often also shows up at non-zero DMs. Moreover, a number of RFI signals may demonstrate a pulsar-like behaviour with narrow-peak profiles and wideband emission. Thus, a decision whether the candidate is a pulsar or not can not be made based on one factor, it is necessary to consider all the pieces of information provided in diagnostic plots together.
The results of folding can either go directly to a trained human inspector who makes the final decision or can be first analysed by specific programs. Since modern pulsar surveys produce millions of candidates, it is inexpedient to rely on human resources only. A much more effective approach includes applying different“smart” algorithms for ranking the candidates by their “pulsar-likeness”. These algorithms may operate within artificial neural networks (Eatough et al.,2010) or/and be based on some euristic scores (Lee et al.,2013) or use image pattern recognition (Zhu et al.,2014). Implementation of such algorithms into the pipeline may save many “human-hours” and significantly reduce the number of plots for visual inspection.
The most promising candidates chosen by a human inspector are saved for confir- mation with a telescope. If the signal with the same (or close) parameters appears in a new observation, its pulsar nature is considered proved and the source becomes a subject of further timing (see Chapter 4).
2.2. Working with data: pulsar searching 45