Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array Software correlators as testbeds Rapid evaluation and prototyping of RFI algorithms Adam Deller NRAO Socorro March 31, 2010

Why use in-correlator techniques for RFI detection and mitigation? Software correlators: – Key attributes and advantages – Applications in RFI algorithm development Test case: Kurtosis-based detection of RFI Conclusions 2 Outline

Higher time resolution data Easier to identify impulsive/short time duration RFI Removal of affected areas can potentially lead to less data loss Other techniques such as Field of View (FOV) shaping require modifying data on timescales shorter than 1 integration 3 Why in-correlator techniques?

4 EVLA example Frequency Time Aircraft radar (12s period)

5 Software correlators Correlation algorithm is coded in a high-level language such as C++, runs on commodity machines (nowadays, multicore rackmount servers) Rapid and inexpensive to develop Widely used in VLBI (DiFX correlator used by LBA, VLBA, MPIfR Bonn, …) Key point here: quick/easy to modify

6 Software correlators The hardware used for the VLBA DiFX software correlator in Socorro; 5 x dual motherboard, dual CPU quad cores (process 10 stations x 128 MHz b/w in real time)

7 The DiFX architecture Interconnect is commodity ethernet (Message Passing Interface). Optimised C vector libraries used for speed-up. Master Node Core 1DataStream 1 DataStream 2 DataStream N Core 2 Core M … … Timerange, destination Baseband data Visibilities Source data Large, segmented ring buffer Up to 100s MB/ a few or more seconds Visibility buffer processing buffer (~100ms) All processing done in floats

8 The DiFX architecture FX style correlator Requires only a couple of libraries and a C++ compiler - easy to get going DiFX reads baseband data from a file or network stream (Mk4, VLBA, LBA, VDIF*) Output: Produces FITS-IDI files (easy path to AIPS/CASA) * Aside: VDIF is a simple but general packet-based format, hopefully convergence here - for specs see specification Release 1.0 ratified.pdf

9 DiFX info Google group: users?hl=en users?hl=en Wiki: SVN codebase:

10 Kurtosis analysis Basically measures the peakedness of pdf of a time varying quantity - equal to 3 for normally distributed quantities Impulsive RFI leads to a pdf with many outliers and a kurtosis value >> 3 Subtle differences between real time domain data and complex frequency domain data (Nita, earlier)

11 Kurtosis analysis I applied kurtosis analysis to channelized (post- FFT) data from each antenna (not cross- correlations) Easy to calculate - just need 2nd (autocorrelation) and 4th (autocorrelation^2) central moments of the quantity of interest Easiest to maintain moments about the origin 1 - 4, convert at desired duty cycle De Roo (2009), IEEE Trans. Geosc. Rem. Sens.

12 Implementing kurtosis in DiFX Allocate a few extra arrays and make a few extra function calls to calculate moments about the origin Convert to central moments and calculate kurtosis at the end of every subintegration Getting results out is no hassle (maybe unlike clocked h/w system with less I/O) Total development time: 1.5 hours

LL polarisation shown at 1/3 real time (2 second integrations, 100ms kurtosis calc) 16 Kurtosis results A “normal” RFI-free band from one station

LL polarisation shown at 1/3 real time (2 second integrations, 100ms kurtosis calc) 17 Kurtosis results Same band at Hancock, where the RFI is clearly much worse

Basing flagging on a kurtosis threshold is trivial! One line of code to zero any affected channels in all baselines to given antenna Currently dumping at the “subintegration” timescale - usually of order 20ms Easy to integrate further downstream; DiFX also has a feature to manipulate data on timescales shorter than 1 subintegration 18 Implementing kurtosis flagging

Correctly calculate expected value of kurtosis for 2 bit quantized input data after channelization Test the effect of kurtosis-based clipping on the interferometer output (imaging statistics) Test the implementation on a connected- element system like the EVLA (which can produce VDIF output suitable for DiFX) 19 Work to do

FOV shaping; weighting subintegration (or smaller) chunks of visibility data to improve correlator FOV “filter” Other thresholding or kurtosis on autocorrelations or crosscorrelations With VLBI (or potentially EVLA) data we can record the baseband and test RFI algorithms many times in a controlled way 20 Other RFI algorithm possibilities

High time resolution RFI detection and/or rejection in interferometers is an interesting and worthwhile pursuit Software correlators make testing “in- correlator” algorithms much easier! A simple kurtosis-based RFI auto-flagger will be made available in DiFX Plenty of scope for further development 21 Conclusions

22 Questions?

By repeating this operation multiple times one can generate an arbitrary number of “pencil beams” (as CPU memory permits) The overhead is small compared to the cost of correlating the data: generating 100s of pencil beams only requires ~3x the compute power 23 The use of multiple FOVs primary beam uv-shifted “pencil” fields Not to scale!!

Low-resolution radio data can provide a flux- complete sample to be surveyed: The known empty space is ignored! 24 Directing the survey Random cutout from the NRAO FIRST survey primary beam

Datastream correlation flow Data buffer Start time Valid samples Num sent MPI_Send * handle Lock Read thread Send thread “Segment” “Send” ….. FFT = 2x num channels Requested time sent to Core

Core in pictures Core object Subint slot Baseband data from each telescope Subint visibilities Proc. thread Thread visibilities Mode objects for each datastream Read/send thread Repeated for each subband Baseband data pointer unpacked data Intermediatiate data Final data for XMACXMAC