The STAR experiment

At the moment my main focus is the off line calibration of the barrel EMC using neutral pions from 2006.  My (very) rough plan of how to do this is as follows:

where

• L_int_mb is the integrated luminosity seen by the minbias trigger for runs in which the specified trigger was active
• eps_vtx is the vertex finding efficiency for this trigger
• eps_mb is the vertex finding efficiency for mb-triggered events in runs in which the specified trigger was active
• vz is the fraction of events with a reco vertex that had fabs(vz) < 60 cm
• vz_mb is the same quantity for mb triggered events
• L_samp_mb is N_good_vertex_mb / (eps_mb * sigmaBBC))
• < ps > is sum_{runs} (ps_mb * n_mb) / sum_{runs} (ps_trig * n_trig)
• L_sampled is L_samp_mb * < ps >

Data were obtained from the 289 spinTree runs without any additional QA selection.  If I restrict to the 188 runs in the latest jet runlist I get

The vertex finding efficiency for minbias triggers climbs a little bit, but it's still only 51%.  After doing a little digging it seems this is consistent with Jan's findings in his evaluation of PPV for 2006:

http://www.star.bnl.gov/HyperNews-star/protected/get/starspin/2820.html

How much of the large-scale variation is coming from the multiplicity and z-vertex averaging?

I wrote a script that compares each Run 7 BTOW pedestal table with a corresponding L2ped printout. The script queries the RunLog_onl database for the runnumber immediately preceding the table timestamp, then it looks for a log file corresponding to that runnumber in the L2ped directory on online.star.bnl.gov. If it can’t find the precise runnumber, it compares the online pedestals to the next closest L2ped runnumbers both before and after, and then prints the comparison for the better match.

Here’s a summary log printing the number of channels per table where

1. the difference in pedestal value is greater than 4, or
2. the difference in pedestal sigma is greater than 1.5

There are a handful of channels per run with drastic differences. For example, emcOnline will declare a zero pedestal, but l2ped will say there’s a peak at 3500 ADCs. Channels like these are obviously bad. Of greater concern are the runnumbers where several hundred channels do not match. Most of the time it’s because the widths are different, but occasionally there are runs where a few hundred towers will have pedestals that differ by more than 4 ADCs. The full verbose output (listing every tower that fails the cut) can be found here.

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In
this update
a set of results with Adam's suggested momentum cut is added to the pool of analysis results for d+Au.

I've been working with Priscilla on adding her electron candidates to the spin trees.  spinTrees with electrons are now available, and we went ahead and made these plots investigating the properties of jets with identified electrons.  We analyzed BJP1 and L2Gamma triggers and plotted the p_T spectra, track multiplicity, and neutral energy fraction for all jets and for jets associated with an electron candidate (deltaR < 0.7).  Full-size plots available by clicking on each image.

Electron Cuts

• Global dE/dx cut changing with momentum
• nFitPoints >= 15
• nDedxPoints >= 10
• nHits / nPoss >= 0.52
• track Chi2 < 4
• DCAGlobal < 2
• NEtaStrips > 1 && NPhiStrips > 1
• Primary dE/dx cut changing with momentum
• 0.3 < P/E < 1.5
• -0.01287 < PhiDist < 0.01345
• ZDist in [-5.47,1.796] (West) or [-2.706,5.322] (East)

Jet Cuts

• 0 < R_T < 0.99
• -0.7 < detEta < 0.9
• Jet points at fired jet patch (BJP1 only)

Conclusions

• The neutral energy fraction for L2gamma electron jets peaks at the same point as the ridge in the inclusive jet plot.
• There are a significant number of 1-track jets in the L2gamma sample
• In the plot of jet p_T vs. electron p_T we see the maximum of the distribution occurring at a electron pT of ~5.5 GeV and a jet pT of ~11 GeV.  This is in stark contrast to the BJP1 distribution. 

1. QA for BJP0 trigger and runlist generation
2. Confirm vertex and nSigma cuts with systematic studies
3. Run over new PYTHIA simulation, make basic data/simu comparisons
4. Trigger Bias studies
5. Extract A_TT from transverse data
6. Randomized spin pattern studies
7. PID background estimation

Notes

• Adding the L2gamma trigger would improve statistics but we don't have an officially sanctioned simulation of that trigger available yet.
• This checklist assumes the preliminary result is an inclusive measurement using only Run 6 data; other possibilities are to combine Runs 5 and 6 or to pursue the jet+pion measurement.