NanoBPM Status and Multibunch

WP 4.2 Meeting – 13/09/06

Mark Slater, Cambridge University

NanoBPM Results – Calibration Stability (1)

I have spent some time looking at the stability of the calibrations at ATF, specifically, the frequencies and decay constants of the cavitiesFirst, I examined the short term stability (~12mins) by fitting the waveform of BPM X1 (thus avoiding cross talk) with an unknown frequency and decay constantThe dependence with time and amplitude is shown:

NanoBPM Results – Calibration Stability (2)

In order to try to understand the systematic drift and RMS of the distribution, I produced some Monte Carlo pulses and ran the same fitting code over themAfter adding in a band pass filter to the Monte Carlo, I could get the generated distributions to produce similar systematics and RMS valuesHowever, both the systematic drift and RMS were very dependent on the shape and size of the filterMore investigation would be required to fully recreate the observed distributions

NanoBPM Results – Calibration Stability (3)

I have also looked at the long term stability of the frequencies and decay constants over the period of an entire shiftBy taking average values of from the fit over 100 pulses, taken from runs throughout the shift, the long term behaviour of the cavities could be seen:

NanoBPM Results – Calibration Stability (4)

The decay constant seems to just have random variation during this time period but the frequency decreases with time during the shiftBelieving this may be associated with temperature, I examined the data recorded by the temperature sensors and a similar trend was found:

NanoBPM Results – Calibration Stability (5)

The recorded temperature change (~0.25C) produces a change in frequency of ~65kHz which agrees reasonably well with that predicted by AlexeiThis frequency change alters the scale factor by ~0.5% as the DDC is no longer extracting the same amount of powerAssuming a typical offset of 10m, this indicates a systematic change of ~50nm

NanoBPM Results – Resolutions with Tilt

In June, Stewart and Alexei took data at ATF where the BPMs were tiltedThe first check was to see if the resolution changed between tilt setting of -1000rad and +1000radIt was found that there was no significant difference in resolution between the two settings:

NanoBPM Results – Calibrations with Tilt

However, the calibrations changed significantly with the different tilt settingsUsing the data taken during the whole day, I could plot the change in IQ Phase and Scale vs. tilt settingIt appears that there is a gradual change in IQ Phase through the day (Note: the data was taken in the order it was plotted – hence the discontinuity)There is however, a noticeable dependence of the scale on the tiltThe reason for this systematic effect is not currently known

Multibunch (1)Also in June, Stewart and Alexei took some multibunch data (3 bunchs, 150ns separation)Unfortunately, it was almost impossible to steer down and so the data is too saturated to be usedConsequently, I have done preliminary analysis of some simulated multibunch dataI first tried to fit bunches from the beginning of a bunch train and see if, by subtracting the fitted waveform, the amplitudes and phases could be adequately extracted from each bunchThis seemed to be possible with only a slight addition of noise:

Multibunch (2)The algorithm shows good linear behaviour until the saturation becomes too much and there aren't enough unsaturated points to perform a good fitDoing a crude calculation using just an exponential decay and assuming the fit requires 10 points to perform an accurate fit indicates that the maximum amplitude for a 300ns bunch spacing is ~15000 countsThe fit was also performed in the middle of the bunch train to see if it was possible to recover resolution/accuracy after a significantly saturated pulseIt appears that it requires only a single bunch to return to a stable state, though this is assumed to be amplitude dependentThere is a slight systematic drift over the next 5 or so bunches as the system settles down

Multibunch (3)On the request of Alexei, I then started to look at the energy build up in the cavities during long bunch trainsBy summing a series of exponentials and applying a phase factor (assuming that the start of a bunch will be the maximum amplitude), it was possible to find the steady state reached by the BPM as a fraction of the actual set amplitudeI investigated the response as a function of decay constant and phase for both 300ns and 150ns bunch spacings

Multibunch (4)Finally, I investigated what effect altering the coupling would have on the resolutionAltering the coupling will not only change the decay constant but also the amplitude as the amount of power extracted is the sameIt turns out that the amplitude is proportional to the square root of the decay constantThere does seem to be a significant dependence of the RMS (which is effectively resolution) on the coupling, but it remains to be seen whether the larger dynamic range gained from a shorter decay time would outweigh the loss in sensitivty

Multibunch ConclusionsThe fitting algorithm seems able to extract both amplitude and phase from a multibunch waveform with minimal addition of noiseThis seems to be the case even when starting the fit from the middle of the bunch trainThere is a limitation on the dynamic range as a result of the saturation and the number of unsaturated points required to fit the waveformOne of the significant issues still to be resolved is that of t0 fittingIf the bunch spacing is constant to within a few ns, the initial t0 can be found from fitting and then then the t0's of the following bunches found just by the addition of this bunch spacingIf this is not the case though, a more complicated system would need to be developed as the trigger pulse for multibunch is no longer a simple rising and falling curveI still need to try a similar analysis using the DDC algorithm