ETA Data Processing

Steve EllingsonLow Frequency Software Workshop – Chicago – Aug 10, 2008

RFI Environment: Bad But Manageable

TV

Ch 4

Ch 3

Ch 2

Ch 5

Ch 6

KEY POINT:

Can observe here – but need good linearity and narrow channelization

~ 100 s of noise-limited sensitivity using > 95% of contiguous 5

MHz band around 38 MHz

Search Range

(29-47 MHz)

Primary threat to linearity – receiver design challenge

Often, but not always

possible.

In-Band RFI Challenges

Wideband junk

Wideband junk

Wideband junkS

elf-

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6-m

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Ionospheric enhancement

Ionospheric enhancement

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izen

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Impulsive noise starts to become a problem at

resolutions ~100 s

Galactic background clearly visible underneath sparse RFI

Self-RFI is a relatively minor problem

Offline ProcessingUp to 200 x 1GB (17s) Files7+7 bit complex @ 7.5 MSPS

Data integrity check

Create raw spectragrams

Create baseline spectragrams

Calibrate spectragrams

RFI mitigation

Incoherent dedispersion

Integrate time series

Manual inspection for pulses

Data transfer errors (rare but significant)Sample value histograms / clipping (checking for intermittent RFI swamping)

1K FFT (yields freq-time resolution 7.324 kHz x 136.5 s)Integrate to 8.738 ms (for Crab GP search; also, suppresses impulsive RFI)

Updated every ~7.5 minutes (timed to track Galactic background variation)using spectragrams hand-picked for low RFI

Remove frequency response; Linear interpolation between baseline spectragrams to track Galactic background

Three passes of “plinking” (replacing extreme values with median values):(1) Time-frequency pixels one at a time [th1](2) All freq pixels for a given time, triggered on total power thresholding [th2](3) All time pixels for a given freq, triggered on integrated spectrum thresholding [th3]

Operates on 7.324 kHz x 8.738 ms spectragramsw/o interpolation

In effect, smoothing to expected resolution of scattered-broadened pulse (We use 498 ms for Crab)

Difficult to automate due to RFI and time-domain baseline fluxuations

Possible Incoherent combining of polarizations / dipole signals

Example of RFI Mitigation

Before

3.75 MHz

3600 sAfter

th1 = 0.40 (time-freq)th2 = 0.03 (time)th3 = 0.02 (freq)

< 1% pixels plinked

3.75 MHz

= 7.324 kHz = 498 ms

= 7.324 kHz = 498 ms

38.0 MHz

38.0 MHz

Plotting power; Extreme values in this plot

are typically within a few % of mean

Example Simple Pulse Detection (old toolchain – sorry!)

RFI Mitigation, DM = 56.791 pc/cm3

No RFI Mitigation, No Dedispersion

RFI Mitigation, No Dedispersion

5

5

Duration ~ 2 sPeak DM = 56.791 pc/cm3

Est. flux ~ 876 Jy

DM sweep

Example of Relatively Good RFI Conditions

No RFI Mit, No Dedispersion

RFI Mit, No Dedispersion

RFI Mit, DM = 56.791 pc/cm3

Off-Line Processing Summary Data processing

– Operates on coherently-sampled voltage data (dipoles or beams)– 1 hour of observation is typically about 1 TB raw (data constipation!)– 100% new C-language source code / tool chains– Nothing special for computing (tend to use existing PC cluster to minimize

amount of data transfer)

Lessons Learned (from the perspective of a dispersed pulse hunter)– Value of extensive diagnostic “pre-analysis” to identify problematic data:

Smallest fraction of FLOPS, but greatest fraction of person-hours Weak RFI (histograms over many domains & resolutions) Spurious ionospheric conditions Consistency with sky model (“Error” in time-varying continuum small?) Repeatability (is today within a few tenths of percent of yesterday?)

– Seems to be more productive to reobserve than to try to salvage “subtly problematic” data, even if only portions look bad.

By our standards, we end up throwing out about ½ of data that initially looks good

– Extent of site multipath (self-inflicted), impact– Antenna & cable dispersion, impact– Value in keeping coherent dipole voltage data, despite logistics, to maximally

facilitate reprocessing

9

ETA A/D-RX Board

Analog SignalFrom ARX

120 MHzSystem Clock

Parallel(4b + CLK)LVDS toRCC:

7.5 MSPSI7+Q7, plus in-band data(240 Mb/s)

29 47

3.75

18

Altera Stratix EP1S25 25,560 LEs80 9-bit DSP blocks1,944,576 memory bits

LVDS direct-connects via Mictor connector

12-bit,120 MSPS

digitization

1

Reconfigurable Computing Cluster (RCC)

• 16-node “Virtual FPGA”

• Each node is a development board with Xilinx XC2VP30 FPGA

• Edge nodes (“E”) catch streaming LVDS from digital receivers

• 3.125 Gb/s Infiniband-like interconnects

• Center nodes (“C”) route between RCC nodes & push results to PC cluster

• PPCs internal to FPGAs run Linux, perform GPP-type functions

Xilinx ML310

1

RCC “All Dipoles” Mode

240 MB/s aggregate

(60 MB/s per PC)

Coherent time series, 3.75 MHz BW

Acknowledgements:

John Simonetti PhysCameron Patterson CpE

Zack Boor PhysSean Cutchins PhysKshitija Deshpande EEMahmud Harun EEMike Kavic PhysAnthony Lee EEBrian Martin CpEWyatt Taylor EEVivek Venugopal CpE

Pisgah Astronomical Research Institute

AST-0504677

Supported by:

http://www.ece.vt.edu/swe/eta