We describe a method for spectral cleaning and timing calibration of short voltage time series data from individual radio interferometer receivers .
It makes use of the phase differences in Fast Fourier Transform ( FFT ) spectra across antenna pairs .
For strong , localized terrestrial sources these are stable over time , while being approximately uniform-random for a sum over many sources or for noise .
Using only milliseconds-long datasets , the method finds the strongest interfering transmitters , a first-order solution for relative timing calibrations , and faulty data channels .
No knowledge of gain response or quiescent noise levels of the receivers is required .
With relatively small data volumes , this approach is suitable for use in an online system monitoring setup for interferometric arrays . We have applied the method to our cosmic-ray data collection , a collection of measurements of short pulses from extensive air showers , recorded by the LOFAR radio telescope .
Per air shower , we have collected 2 \mathrm { ms } of raw time series data for each receiver .
The spectral cleaning has a calculated optimal sensitivity corresponding to a power signal-to-noise ratio of 0.08 ( or -11 dB ) in a spectral window of 25 kHz , for 2 ms of data in 48 antennas .
This is well sufficient for our application .
Timing calibration across individual antenna pairs has been performed at 0.4 \mathrm { ns } precision ; for calibration of signal clocks across stations of 48 antennas the precision is 0.1 \mathrm { ns } .
Monitoring differences in timing calibration per antenna pair over the course of the period 2011 to 2015 shows a precision of 0.08 ns , which is useful for monitoring and correcting drifts in signal path synchronizations . A cross-check method for timing calibration is presented , using a pulse transmitter carried by a drone flying over the array .
Timing precision is similar , 0.3 ns , but is limited by transmitter position measurements , while requiring dedicated flights .