The goal of this page is to help the on-boarding of new students who are interesting in single pulse searches.

The tutorial uses PRESTO. Although PRESTO may not be the best for a computationally intensive pipelines, it is pedagogically useful by allowing the user to manually run each pipeline step.

Link to PRESTO tutorial in PRESTO's github repo

Notes:

• I providing the commands outside of singularity. Run them with singularity as you prefer.
• I assume that the input data is in filterbank format and done my best to note where commands may differ in the case of PSRFITS format.

1. Data: short observation of a test pulsar (MY_DATA.fil)
2. Software: either a working PRESTO install or a singularity image containing PRESTO

Become familiar with the structure and contents of the data using waterfaller.py.

Example command:

> waterfaller.py --show-ts --show-spec --colour-map='viridis' -d 57 -T 100 -t 5 --downsamp=8 --scaleindep MY_DATA.fil

Parameters to play around with:

• Turn on and off dedispersion
• Go to a different time in the file
• Change the downsample factor
• Look at data with and without independently scaling each frequency channel

PRESTOs removes RFI by calculating a mask. The mask informtion is saved in a number of files, one of which can be passed to the following steps. Therefore, a new, cleaned copy of the original data file is not produced.

Example command:

> rfifind -o mask_file_name -time 1.0 -zapchan N:N MY_DATA.fil

Parameters to play around with:

• Duration of processing time (-time)
• In the case of PSRFITS data: -noscales -nooffsetts
• Change thresholding parameters (-timesig, -freqsig, -chanfrac, -intfrac)
• Add channels to manually zap (-zapchan)

Note, rfifind can be quite slow to run, but it is possible under some circumstances to recalculate the mask from the mask itself rather than rerunning the entire code.

Data file: MY_DATA.fil
Original mask: mask_file_name_rfifind.mask

> rfifind -o mask_file_name -nocompute -freqsig 8 -mask mask_file_name_rfifind.mask MY_DATA.fil

This command can be used to generate a single dedipsersed time series by setting -numdms 1. Having dedispered time series over a range around the true value is useful for in Step 4.

> prepsubband -nobary -lodm 52.4 -dmstep 0.5 -numdms 20 -clip 0 -downsamp 1 -mask mask_file_name_rfifind.mask -o my_ts MY_DATA.fil

Variations:

• PSRFITS files: if you are searching a PSRFITS file and applied --noscales -nooffsets in the RFIfind stage, you should do so here too.
• Change the DM step and DM range (and see its impact on Step 4)
• Change the downsampling factor
• Run with clipping to see if this changes the time series

Manually inspect the time series:

> exploredat my_ts.dat

• Can you see bright pulses?
• Note the slowly varying
• If you've applied an RFIfind mask, what impact does this have on the time series?

> single_pulse_search.py -m 30 -t 6 -b *.dat

Parameters to vary:

• Turn on bad block flagging
• Use different detrend chunksizes

Look at SP diagnostic plot: my_ts_singlepulse.ps

Recreate this plot with a different threshold or time range:

> single_pulse_search.py -m 30 -t 8 -s 0 -e 120 -b *.singlepulse