The STAR experiment

In order to get the most realistic performance possible out of the FGT Slow Simulator, all important physical effects have to be incorporated.

BSMD

The SMDs can be simulated in one of two modes.  In a rewrite of the slow simulator I call them kTestMode and kSimpleMode.  kTestMode doesn't do anything interesting, so let's focus on kSimpleMode.  Here are the steps to generate an ADC:

1. ADC = energy deposit * sampling fraction(eta) / calib
2. ADC += mRandom.Gaus(pedMean, pedRMS)
3. ADC *= mRandom.Gaus(1.0 + calibOffset, calibSpread)
4. force ADC between 0 and maxADC

The energy is then calculated from this ADC is the usual way:  energy = (ADC - pedMean) * calib.  Remember the BEMC calibration coefficients are stored as GeV/ADC.

BTOW - BPRS

The simulators for these two detectors have some additional modes (kPrimaryOnlyMode, kPrimarySecondaryFastMode, kPrimarySecondaryFullMode).  Here's the setup for the kPrimaryMode:

1. photoElectrons = energy deposit * (MIP photo electrons / MIP energy deposit)
2. smear using Poisson distribution
3. ADC = photoElectrons * sampling fraction(eta) * (MIP energy deposit / MIP photo electrons) / calib
4. follow steps 2-4 of simple simulator to add pedestals, smear calibration, and check for valid range

The other two modes also take the secondary photostatistics from the PMT amplification into account.  I'll focus on the full mode, as it's slightly easier to understand:

1. build a set of secondary electron conversion coefficients (1 / dynode) using the relative voltage distribution among the dynodes and an approximate value for the total PMT amplification.
2. Loop through dynodes and at each step
   1. nElectrons = mRandom.PoissonD( mSecondaryCoeff[i] * nElectrons + mDynodeNoise );
3. ADC =  nElectrons * mTotalDynodeGain *sampling fraction(eta) * (MIP energy deposit / MIP photo electrons) / calib
4. follow steps 2-4 of simple simulator above

The mTotalDynodeGain in step 3 is the inverse of the product of the secondary electron conversion coefficients.

sampling fraction -- each sampling fraction is a 2nd order polynomial in eta.  Here are the values currently being used:
BTOW = 14.69 - 0.1022*eta + 0.7484 * eta^2
BPRS = same as BTOW (pams/emc/inc/samplefrac_def.h includes different number)
BSMDE = 118500.0 - 32920.0*eta + 31130.0*eta^2
BSMDP = 126000.0 - 13950.0*eta + 19710.0*eta^2

Here are the numbers for BPRS from pams/emc/inc/samplefrac_def.h:
#define P0BPRS  559.7
#define P1BPRS -109.9
#define P2BPRS -97.81

max ADC value -- BTOW(3500), BPRS(220), SMDs(900)

Calibration offset (0.0) -- shifts all calibration coefficients during generation of ADCs.

Calibration spread (0.0) -- smears ADCs with a Gaussian.  A spread of 0.15 for the BTOW has been used in recent analyses.

The following params are only used by the slow simulator for the BTOW and BPRS:

MIP photo electrons -- BTOW(63.0), BPRS(6.0)
MIP energy deposit -- BTOW(19.8 MeV), BPRS(2 MeV)

The ratio of these two is used to translate the energy deposit from GEANT into a number of photoElectrons which is then fed into the StPmtSignal simulator.

approximate PMT amplification (1.5e+6).  Does NOT affect overall gain, which is a separate input parameter.  Basically only determines some widths in the PMT simulator.

relative dynode voltage distribution {2.,2.,1.,1.,1.,1.,1.,1., 2., 3., 4.}

cathode noise (0.0) A probability for a thermal electron (or the average number of thermal electrons per pulse) to spontaneously emerge from the photocathode within the ADC gate.

dynode noise (0.0) A probability for a thermal electron (or the average number of thermal electrons per pulse) to spontaneously emerge from a dynode within the ADC gate. This probability is assumed to be the same for all dynodes.

For these 2 detectors one can choose between simple, fast, and full simulators.  The only difference between full and fast is that at high numbers of photo electrons the PMT simulator switches to Gaussians instead of Poisson distributions.  We've been inadvertently running the fast simulator for some time now due to a flip in indices in the simulator code, but according to V.Rykov's documentation the two are in good agreement.  The simple simulator is the one used by the SMDs; no accounting for secondary photostatistics is done, so the ADC value is basically (energy deposited * sampling fraction * gain), plus pedestal noise and calibration smearing.

I continue using HIJING and trying to understand the energy-momentum-conservation
and acceptance effects in d+Au.

The PDSF FAQ can be found under http://wiki.nersc.gov ->STAR->FAQ. Here is an echo of the FAQ as of 8/22/2007.

Data

Monte Carlo

Instead of producing lots of 2007EmcMbStatus runs to generate pre-production status tables, I thought we should look into using the compressed tower spectra Jan saved in the l2ped monitoring program.  I wrote a script to regenerate histograms from these ASCII lines and then asked Matt Cervantes to run the CSMStatusUtils code on them.  I also asked him to run the status code on standard histograms produced by analyzing MuDsts for a single test run (8141062).  I compiled some stats on the differences between the  offline status and the l2 status:

offline == 1, L2 != 1:  76 towers
offline != 1, L2 == 1:  8 towers
Both bad, but different reasons:  40 towers

Some comments:

• L2 status had trouble catching stuck bits (220, 1143, 1612, 2188) as well as recognizing cold towers (187, 4595).  These two scenarios accounted for pretty much all of the cases where L2 marked a tower good and offline didn't.
• L2 marked a bunch of towers with high pedestals as "cold", since there are zero counts above 60.  Most of the differences in Case 1 are due to this problem.
• Generally very good agreement -- less than 2% of towers were different if all you care about is status==1.

We'd like to tweak things a little to see if we can catch the few differences we have.  In particular, marking all those towers as cold could hurt the vertex-finding efficiency a little bit (that's all we really care about in this pass). 

Detailed status codes and histograms are available at the bottom of the post.

• xgrid.pm can submit and cancel jobs successfully, haven't tested "poll" since the server is running WS-GRAM.
• Xgrid SEG module monitors jobs successfully.  Current version of Xgrid logs directly to /var/log/system.log (only readable by admin group), so there's a permissions issue to resolve there.  My understanding is that the SEG module can run with elevated permissions if needed, but at the moment I'm using ACLs to explicitly allow user "globus" to read the system.log.  Unfortunately the ACLs get reset when the logs are rotated nightly.
• CVS is up-to-date, but I can't promise that all of the Globus packaging stuff actually works.  I ended up installing both Perl module and the C library into my Globus installation by hand.
• Current test environment uses SimpleCA, but I've applied for a server certificate at pki1.doegrids.org as part of the STAR VO.

Important Outstanding Issues

• streaming stdout/stderr and stagingOut files is a little tricky.  Xgrid requires an explicit call to "xgrid -job results", otherwise it  just keeps all job info in the controller DB.  I haven't yet figured out where to inject this system call in the WS-GRAM job life cycle, so I'm asking for help on gram-dev@globus.org.
• Need to decide how to do authentication.  Xgrid offers two options on the extreme ends of the spectrum.  On the one hand we can use a common password for all users, and on the other hand we can use K5 tickets.  Submitting a job using WS-GRAM involves a roundtrip user account -> container account -> user account via sudo, and I don't know how to forward a TGT for the user account through all of that.  I looked around and saw a "pkinit" effort that promised to do passwordless generation of TGTs from grid certs, but it doesn't seem like it's quite ready for primetime.

I am using FTPC hits to study the hit density in the forward region. For this I use files from run 7145009, where the BBC coincidence rates were around 500 kHz.

While I am waiting for my simulation jobs to finish, I am switching gears for a moment and looking at real data.  I am trying to finalize the mass window I use for my asymmetry measurement.