With all calibrations now done, I wanted to see whether running with the Prepass was still any better than running using the 1-second RICH scalers which are, as of the Run 7 data, fully available through the DAQ and production software chains (a follow up may be necessary to decide whether the 1-second scalers are truly available for the entire run as there may have been some outages).

I analyzed two samples:

• 5067 low luminosity minbias events from run 8120055
• 1890 btag events from run 8141106

I processed each dataset twice: with a Prepass and without. For the btag, it took between 3 and 9 events for the PrePass, and all the files had 200 or more events. For the minbias data, it was between 5 and 15, with over 500 events per file. So I will look at the performance of the prepassed events (i.e. the first 9 or 15 events), and then the performance of events after that for the signed DCA distributions at the primary vertex. These are presented here for two fits to the DCA distributions: a single Gaussian over +/-0.4cm, and a double Gaussian over +/-0.6cm, which more accurately describes the data because tracks with silicon hits tend to have a much narrower DCA distribution:

We learn that the distributions look pretty much identical for whether a Prepass is used, regardless of the case that those events determine the Prepass calibration ("Prepass events"). The only point here possibly favoring of using a Prepass is the mean of the broad (i.e. TPC-only tracks) DCA distribution in the double Gaussian fit for the btag events which were Prepass events. But this argument is countered by the same quantity being slightly better without Prepass for the low luminosity minbias events.

An additional check of relative performance can be made by simply counting tracks reconstructed:

It should be noted here that the FTPC was in the btag sample, but not the minbias sample. So the btag sample is shown for all tracks, and for those excluding the FTPC (i.e. mid-rapidity, they must have a TPC hit). Note that the btag sample has more primaries and globals per event most likely both as a trigger bias versus the minbias sample and due to pileup/multiple vertices as the btag was recorded at a high luminosity, while the slight decrease in primaries/global at mid-rapidity for the btag is probably due to pileup or backgrounds in the higher luminosity running contributing more to the globals. The important comparison is whether the Prepass makes any impact, and this appears to not be the case with any noticeable significance. I will note for the record that all three of these track samples (minbias, btag, btag mid-eta) there were approximately 0.05+/-0.08% more primary tracks for the production without the Prepass.

Getting closer to the physics level, we can see how the Prepass affects reconstructing Lambdas.
Here is the Lambda invariant mass spectrum from the entire btag V0 sample (no further cuts) with
no Prepass (left, or first), and with Prepass (right, or second):

Using amplitude times width: (7.61553*1.96626)/(7.69510*1.95244) = 0.996664 as many Lambdas in the Prepass as in the no Prepass sample.
So I see a very small amount more in the No Prepass (about 0.3%) , and a narrower width (about 0.1%), but both statements are within error of the
two mass spectra being equivalent.

Conclusion

From these samples, there is no significant benefit to using the Prepass. I recommend not to use it in further reconstruction of the Run 7 (2007) AuAu200 dataset.

Gene Van Buren