RFI Identification and Mitigation Using Simultaneous Dual Station Observations

Ramesh BhatMIT Haystack Observatory

J. Cordes (NAIC/Cornell), S. Chatterjee (NRAO), J. Lazio (NRL)

Arecibo-Greenbank ObservationsGoals of the observations:Demonstrator observations for SKA-like instrumentsDevelopment of RFI ident’n and mitigation algorithmsSome science (eg. Giant pulse studies)

Data taken on:The Crab PulsarM33 (several beams)UGC galaxies

ARECIBO Multi-WAPP 4 x 100 MHz

1120-1520 MHz

GREENBANKSPIGOT800 MHz

1100-1900 MHz

Identical resolutions in time, freq.

T_samp=82 us, deltaF=0.78 MHz

Identification and Mitigation of RFI: Techniques & Applications

• Filtering, Thresholding & Flagging• Spectroscopy (e.g. HI emission from galaxies)• Time series (e.g. Giant pulse detection)• Transient seeking:

– Matched filtering– “friends-of-friends”

No magic bullets or a universal method!

Data in the time-frequency plane -- i.e. Dynamic spectra

Fast-sampled (e.g. 10 – 100 us), high spectral resolution (~1 K channels)

RFI Environment: L-band data

WAPP and SPIGOT bands: short integrations (deltaT= 1 sec, deltaF=0.78 MHz)

RFI Environment: L-band data

WAPP and SPIGOT bands: short integrations (deltaT= 1 sec, deltaF=0.78 MHz)

RFI Identification: Dual-station data

RFI Identification and Rejection SchemeRaw Data (time-frequency plane)

Apply 2-d filtering to capture RFIAverage all the

data, interpolate where necessary

Filtering and thresholding(1d and 2d) in stages: 1st, 2nd, and 3rd

RFI Identification: Filtering Scheme

Apply a 2-d median filtering to the time-freq data (sequence of 1-s spectra)

RFI Identification: Filtering &Thresholding

RFI Excision: Thresholding & Flaging

RFI Excision: clean and flag’d data

21cm line emission from the Galaxy 1420.4 MHz

H I emission from UGC 2339, exp’dat ~1346.5 MHz

RFI Identification: dual-site data

GREENBANK

ARECIBO

RFI Identification: Time Series

CRAB Pulsar

GBT/SPIGOT

L-band Data

(1100-1900 MHz)

Impulsive RFI or giant pulses?

Transient “event” detection schemeDynamic spectra data

Full resolution data in time and frequency

~100 us, ~1 K chans

Dedispersion: single DM or many DMsTime series hanning, smoothing (optionally), thresholding, grouping

Compare the event lists from two sites, or two sub-bands; Examine the t-fsignature, strength

Dispersion: Pulsed signals

Dedispersion

single DM (for known objects)

many trial DMs (GP search)

undedispersed (DM=0) data

Dedispersed time series of the Crab pulsar

Giant pulse from the Crab pulsar (Arecibo)

Transient (“Events”) Detection Scheme

RFI?

Giant Pulses

GBT Crab pulsar data (L band)

Characterize “events” by- signal strength- location- spatial extent

RFI vs “real” events: Filtering Schemeusing multiple sub-bands

Bursty, strong RFI can potentially mask even the strongest giant pulses!

Use of multiple sub-bands to filter out RFI-rich vs. “clean” bands

No. of events detected is a strong function of RFI-contamination in the band

4 WAPPs, 1120-1520 MHz, 100 MHz chunks

Detection of “real” events : sub-band approach

S1 AND S2

S1 AND S3

S1 AND S4

S1

S1

S2

S3

S4

Uncorrelated events? vs. effects of ISS?

Detection of real “events”: Dual-station approach

GBT/SPIGOT

AO/WAPPsExtend the analysis to ‘event lists’ from the two sites (between the respective sub-bands, or more complex criteria)

Application to M33 data

ARECIBO (1320-1420 MHz)

GREENBANK (1300-1400 MHz)

GBT beam ~3 times AO beam

AO gain ~ 5 times GBT gain

Data taken several beam areas on M33

Dedispersion/event detection over a range of DMs

Exp’d DM toward M33 ~ 60-80 pc/cc (NE2001)

Possible giant-pulse detetctions from Arecibo 430 MHz observations (McLaughlin & Cordes 2003)

Slide courtesy: K. Becker

Slide courtesy: K. Becker

Summary

• RFI identification/mitigation in 2d t-f data• Applications to data from AO and the GBT • Different techniques for RFI identification• Filtering, Thresholding, flag’ng schemes for

fast-sampled, high res’n data in time-freq. • Transient “event” detection schemes • Dual-site, sub-bands approach to filter out “real”

events from impulsive RFI in time series data• Still work in progress…..