Qualifying/disqualifying candidates

5.7 Qualifying/disqualifying candidates

Qualifying or disqualifying candidate signals is based both on spectra of noise processes and, usually more crucially, on signal and noise transfer functions. In some cases it is immediately obvious, using the noise transfer function and a single time series, that a stretch of data is noise dominated (large antenna mechanical events for example). In other cases, multiple time series (e.g., the multiple X- and Ka-band signals available with the Cassini observations; see Figure 5

) can be used to qualify candidates.

As an example, candidate periodic and quasiperiodic signals have been disqualified in various data sets (discussed below) based on one or more of these considerations:

Is there a radio wave amplitude variation associated with a Doppler variation (for GWs, there should not be)?
Is there something special about the data taking or the data records where the candidate is observed?
- Is the back-end instrumentation distinguished in some way (e.g., common receiver or receiver rack)?
- Is the candidate observed only at a specific tracking-station? (Is the candidate observed at only one kind of station, e.g., only at the DSN beam-waveguide antennas and not at the DSN “high-efficiency” antennas?)
- Is the signal a plausible alias of a man-made frequency?
- Could a narrow-band feature be due to a mechanical resonance of the antenna?
Does it appear in only part(s) of the tracking record?
Is its frequency modulation consistent with the source being at a fixed point on the sky, given the known earth-spacecraft geometry variation over the observation?
Is the candidate observed in more than one radio band (e.g., X- and Ka-band) simultaneously? Is it demonstrably non-dispersive? Are the individual-band SNRs consistent with a common $Δ$ v/c?
Is the candidate consistent when observed with different resolutions in time and Fourier frequency?
Could this candidate have been introduced into the time series by a faulty calibration?

Each of the above has been used to assess reality or unreality of candidate periodic and quasiperiodic waves in different data sets. Some published examples include the following ones:

Anderson et al. [5 ] observed a chirp that persisted over 10 days. The data were reanalyzed in subsets based on inclusion or exclusion of specific stations, specific transmitter/receiver pairs, and temporal partitions of the data set. The chirp was ultimately disqualified as non-astronomical because it was only observed in the subset of the data involving a particular transmitter/receiver pair.

In another Pioneer spacecraft observation [18 ] the statistical significance of candidate spectral lines was assessed by scrambling the data within the data gaps and reanalyzing. This confirmed analytical work on the false alarm probability and the (lack of) statistical significance of the strongest candidate periodic signals.

In the three-spacecraft coincidence experiment involving the Galileo, Mars Observer, and Ulysses spacecraft [13 ] matched filtering for signals from particular directions that were sinusoidal (except for modulation due to earth-spacecraft motion modulation over the course of the $∼$ 20 day track) gave undistinguished peak SNRs in the Mars Observer and Galileo data sets, but a formally significant SNR in the Ulysses data set. From the modulation over the period of the observations, the inferred direction of arrival was $∘ α ≃ 0$ , $∘ δ ≃ +69$ . Since the position of the candidate source was thus “known”, gravitational wave polarization states [120 , 18 , 16 ] were explored looking for states which could simultaneously couple well to Ulysses but poorly (so as to push a real astronomical signal into the noise) to Mars Observer and Galileo. There was no polarization state which could produce this simultaneously in the three data sets, so this candidate was excluded as a false alarm.

The consistency of multiple, simultaneous data sets can also be used to qualify or disqualify non-periodic waveform candidates. This is complicated by the fact that different data sets have different noise levels and thus different sensitivities to GWs. Figure 17 shows a normalized dynamic spectrum for Cassini two-way Ka-band data on 2003 DOY 008. The strong feature observed in the average spectrum (right panel) comes from a short time interval in the track at about 08:50 UT. Figure 18 shows the dynamic spectrum of two-way X-band for the same track, with no high-contrast feature near (08:50 UT, 0.22 Hz). Subsequent analysis of band pass filtered time series for the X- and Ka-band data showed that the event seen in the Ka-band data, if produced by a real earth-spacecraft velocity, should have also been observable above the noise in the X-band and was not. Such events were observed once per day in the 2002 – 2003 Cassini observing campaign (only – and with varying strength) and are apparently a systematic effect specific to the two-way Ka-band system (perhaps associated with the independence of the Ka-band transmit and receive horns; see Section 4.4 and Figure 5 ).

Figure 17:

As for Figure 16

, but for the Cassini two-way Ka-band track on 2003 DOY 008. The strong features in the dynamic spectrum at about (08:50, 0.22 Hz) have peak local contrast $>$ 100 (and are even marginally visible in the time series in the upper panel).

Figure 18:

As for Figure 17

, but for the Cassini two-way X-band track on 2003 DOY 008. Note the absence of high-contrast features near (08:50 UT, 0.22 Hz).

Qualifying/disqualifying candidate burst waves is slightly different because, by hypothesis, the signal is only “on” for a finite time and some of the above tests do not apply. However a true GW burst must be nondispersive and show the correct three-pulse signature in the time series. Here the three-pulse response [49 ] is very powerful: Whatever the GW waveforms, the signal must show the three-pulse response with correct amplitudes and spacings for a GW from a specific direction relative to the earth-spacecraft line (see Figure 1 ).