Investigating RFI Flagging Techniques with LOFAR

Yvette Cendes

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1. Investigating RFI Flagging Techniques with LOFAR Yvette Cendes
2. The LOFAR RFI Environment Ü  RFI occupancy is 1.8% in the low band, 3.2% in the high band Ü  No difference between day or nighttime observations Plot Credit: A. Offringa
3. AOFlagger Ü  Default automated flagging method used by LOFAR Ü  Works with amplitude information of one polarization of a single sub-band Ü  It relies on thresholding, where cutoffs depend on the surrounding signal levels A LOFAR RFI pipeline A.R. Offringa 2.1 Input
                                       !"    # !$    %     & '    $(  !  ))*)+*+)*++ &  " % %    + Figure 1: Overview of the RFI flag- The flagger is executed on the amplitude information of one polarisation of a single sub-band of a baseline. In LO- FAR’s common operation, a sub-band consists of 256 chan- nels of 0.8 kHz resolution. The full band has 248 sub-bands. LOFAR can observe in two bands: the 10-80 MHz low band and the 110-240 MHz high band, which are observed by phys- cally different antennae. If speed is essential, the algorithm can be executed once on the Stokes-I values. Otherwise, if accuracy is more impor- ant than speed, the algorithm can be executed on the individ- ual XX and YY or LL and RR polarisations, or on all polar- sations individually. We do see some RFI that manifests in only one of the polarisations, or rotates through the polarisa- ions, and some advantage is therefore seen when flagging all polarisations individually. 2.2 Iterations A part of the algorithm is iterated a few times, depicted n Figure 1 by the “Continue iterating” block. This is nec- Image Credit: A. Offringa
4. Transients vs. AOFlagger Ü  The default flagger is designed to catch all the RFI, even if some non-contaminated data gets flagged Ü  AOFlagger uses time selection steps which compares RMS values, and automatically flags anything with a sigma > 3.5 in order to quickly reach convergence This may not be ideal for observations containing transients
5. AOFlagger Test signals in rfigui, flagged out by AOFlagger
6. Modified Flagger Ü  Modified AOFlagger to run more quickly, detect transient signals Ü  Changes include deleting time selection, decrease ‘sliding window’ resolution in time, ignore thresholding in frequency ✻  Tested on MSSS data which showed high RFI percentages flagged in processing- starting from raw data to re-flag, demix, calibrate, and image…
7. Test Data Default AOFlagger Modified AOFlagger
8. L44766, SB151: CS002LBA x CS004LBA Default Flagger Modified Flagger Polarization statistics: Polarization statistics: XX: 3.4%, XY: 3.5%, XX: 0.32%, XY: 0.33%, YX: 3.4%, YY: 3.4% YX: 0.49%, YY: 0.29%
9. Results (Frequency= 73.2 MHz)
10. Results The offset is fairly constant for both strategies
11. Results Computational time decreases, background RMS doesn’t
12. Results L21641 (Bell #1: Nov 28, 2010)
13. When No Flagging Occurs… Ü  When you do no flagging (including any post- BBS flagging) the RMS values increase dramatically including big spikes in amplitude Ü  When you do post-BBS flagging only (eg a simple amplitude cut), you get similar RMS values to a normal image Ü  How is post-BBS flagging in general affecting transient sources?
14. Inserting Fake Signals Ü  Work has begun on inserting artificial signals into MSSS data (starting with simple point sources) Ü  We need to figure out how a bright transient affects calibration and image quality when it isn’t in the sky model Image credit: Dan Calvelo
15. Summary & Future Work Ü  Modifying the default flagger for transient searches appears to work with automatic flagging Ü  Image quality does not appear to be compromised for faster computational speeds Ü  Future work needs to focus on testing the modified flagger on a wider range of data, eg. MSSS HBA Ü  See how this can apply to AARTFAAC Credit: ASTRON Daily Image
16. See You in Mauritius!