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Monte Carlo Asymmetries

Here are the asymmetries for STD, ZERO, GS-C, and MIN that I get out of PYTHIA with the minbias trigger condition applied:

Figure 1: raw minbias asymmetries

and here’s what I get after applying the pT reweighting:

Figure 2: minbias asymmetries after reweighting jet pT spectrum

Figure 3: jetpatch asymmetries

Figure 4: difference between Figures 3 and 1

Figure 5: difference between Figures 3 and 2

Finally, these four plots show the effect of the minbias pT reweighting on the bias systematic for each scenario:

Reweighting the MB pT spectrum

Subprocess Fractions



Study of ET Correction Factor Single Thrown Particle

ET Correction Factor Study using Single Particles

 

BEMC Trigger Code Test

 BEMC Input Bits for Layers 0-2:

  Left Plots show DSM input from data (L0) or simulation (L1, L2)

JP1 & JP2 Turn on efficiency

I used the 2006 MuDsts to generate the turn on curves for the JP1 and JP2 triggers. Both plots are integrated over all jet patches.

Hermes alignment system

Hermes ref

W Program talk for SPIN 2008

Draft for practice talk at analysis meeting

spin goals run 9 draft talk

Draft of run 9 goals attached

Mean pT in z bins

I looked into the mean transverse momentum for pions and jets in each of my z bins. First, here’s a comparison of data (black points) and Monte Carlo (red lines) for the BJP1 trigger:

It looks good to me, so I went on to compare simulations for jet patch and minimum-bias triggers:

In hindsight, this plot makes perfect sense — the trigger hardens the pT spectrum for the jets, so each JP z bin (which integrates over 10-25 GeV) has a higher average jet p_{T} than the MB version.

Now, this 3 GeV p_{T} shift means that we’re biasing the sample in each z bin towards higher x. This is almost certainly the source of the observed trigger bias in the Monte Carlo asymmetries for π+. So, what’s the next step?