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mcasym-gg

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

 

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?