slide 1 25-jan-10huib intema recent low-frequency developments at nrao charlottesville and some...

25-Jan-10 Huib Intema Slide 1

Recent low-frequency developmentsat NRAO Charlottesville

and some other USA institutes


Overview

• Improvements / open issues of SPAM

• RFI mitigation


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


SPAM single layer geometry


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


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 2nd 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


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


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


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


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)


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 ->

<-

fre

quen

cy

© Bill Cotton (NRAO)


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)


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

slide 1 25-jan-10huib intema recent low-frequency developments at nrao charlottesville and some...

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