Slide 1 25-Jan-10Huib Intema Recent low-frequency developments at NRAO Charlottesville and some other USA institutes

Slide 2 25-Jan-10Huib Intema Overview Improvements / open issues of SPAM RFI mitigation

Slide 3 25-Jan-10Huib Intema Short SPAM intro Source Peeling and Atmospheric Modeling Obtain initial sky model and calibration Measure ionospheric phases through peeling bright FoV sources Fit single / multi-layer turbulent ionosphere model to peeling phases Calculate model phase corrections for grid of facets covering FoV Apply corrections during imaging & deconvolution Possibly repeat from step 2 Mainly tested on archival VLA 74 MHz and GMRT 153 MHz data Implemented in Python, using ParselTongue interface to AIPS

Slide 4 25-Jan-10Huib Intema SPAM single layer geometry

Slide 5 25-Jan-10Huib Intema SPAM issues Needed to adapt to latest AIPS and ParselTongue versions Several model fit convergence problems (TIDs) –Bad antennas / ‘bad’ sources –More severe ionospheric conditions –Re-sampling / unwrapping of lower S/N peeling phase solutions Performance of model fitting & generation of solutions

Slide 6 25-Jan-10Huib Intema SPAM improvements Updated to latest AIPS & ParselTongue –Revealed bugs in AIPS: some solved, some work-arounds Improved model fit convergence –Phase interpolation: fixed problem in phase unwrapping routine –Phase interpolation: re-reference to nearest antenna –Initial fit: FT of phase solutions + weighted average near peak –Initial fit: Additional 2 nd order polynomial fit + projection onto KL base vectors –Restricted criteria to reject excessive antennas and/or sources + re-fit Performance improvements –Better model re-use of previous time step –Simplifications in calculation of ionospheric pierce points –Better model convergence allows for single fit of all model parameters

Slide 7 25-Jan-10Huib Intema Test results on VLA 74 MHz data in B-configuration Convergence of SPAM model fits has improved significantly –Post-fit phase RMS is roughly constant during quiet ionosphere –Data loss has gone down significantly during TID periods –S/N of peeling solutions biggest problem: VLA-B requires <1 minute solution intervals for good interpolation and model fits Performance of SPAM has improved significantly: –Despite using Python / AIPS, scripted data reduction of VLSS data sets now matter of hours instead of days (on 3 GHz Linux desktop) Possible VLSS DR2 would require production version, e.g. SPAM implementation in faster C/C++-code

Slide 8 25-Jan-10Huib Intema RFI mitigation Main idea from Athreya 2009 (ApJ, 696, 885): –Use fringe rate to identify and remove RFI signals from visibilities –Subtract rather than flag –Requires quasi-constant RFI signal –Doesn’t work for near-zero fringe sources (e.g., near celestial poles, but also near u=0) © Ramana Athreya

Slide 9 25-Jan-10Huib Intema Similar developments based on Athreya scheme Implementation in AIPS by Leonid Kogan (NRAO Soc) –Fit of both circles and spirals Implementation in Python by Joe Helmboldt (NRL) –De-rotation and fitting Implementation in Obit by Bill Cotton (NRAO CV) –De-rotation and fitting –Testing and bug-fixing by yours truly Issues: –Possible source flux subtraction bias

Slide Jan-10Huib Intema RFI subtraction source bias Artificial VLSS data of one 1 Jy source at varying position in field After subtraction, source detection in UV plane No noise added Noise added © Joe Helmboldt (NRL)

Slide Jan-10Huib Intema Example of Obit implementation on VLSS data (1) Combination of amplitude clipping and subsequent RFI subtraction 5x15 minutes VLA-B 74 MHz, BW 1.5 MHz, 127 ch, 10 sec integr. Time -> <- frequency © Bill Cotton (NRAO)

Slide Jan-10Huib Intema Example of Obit implementation on VLSS data (2) Imaged using field-based calibration Input image 78.4 mJy/beam Output image 69.3 mJy/beam © Bill Cotton (NRAO)

Slide Jan-10Huib Intema Test results on VLA 74 MHz data in B-configuration Reduction of ripples & noise in image background –But improvements are moderate when considering magnitude of removed RFI Scheme needs initial RFI flagging Scheme needs criteria on fringe rate range and vis. amplitude range Subtraction bias is little when using residual UV data –Requires good sky model and calibration Probably most useful for saving short baselines