Impact Paramiter Dependance For Au + Au Cllisions at 20AGev at RHIC in The STAR Detector
50-70%

Midrapidity Yeilds
Negative Pion Assumption Positive Pion Assumption Negative Kaon Assumption Positive Kaon Assumption Proton Assumption
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:6-34
bad mt-m0 bins:1-5,35-40

Problems:

bins 6-8 undercount due
to odd hist shape.

Kaon confused with pions
in bins 35-40 		Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:5-31
bad mt-m0 bins:1-4,31-40

Problems:
Ka contamination of Pi 31-40 		Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
Is dedx.gif okay? Fits are good
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:2-27
bad mt-m0 bins:1-3,27-40

Problems:







.2 Rapidity Yeilds
Negative Pion Assumption Positive Pion Assumption Negative Kaon Assumption Positive Kaon Assumption Proton Assumption
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:




.4 Rapidity Yeilds
Negative Pion Assumption Positive Pion Assumption Negative Kaon Assumption Positive Kaon Assumption Proton Assumption
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:
Can't get good fits
Problems:




-.2 Rapidity Yeilds
Negative Pion Assumption Positive Pion Assumption Negative Kaon Assumption Positive Kaon Assumption Proton Assumption
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
Can't get good fits
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
Can't get good fits
Details
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:




-.4 Rapidity Yeilds
Negative Pion Assumption Positive Pion Assumption Negative Kaon Assumption Positive Kaon Assumption Proton Assumption
Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
Can.t get good fits
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:
 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:

Problems:

 Counts Per Event

dedx vs beta*gamma

Good mt-m0 Bins:
bad mt-m0 bins:
Can.t get good fits
Problems:




General Problems With Yeilds:

Plot yeilds is not working, gives incomplete plot then frezes root4star

lots of high pts just above centroids

First few mt-m0 bins have terrible fits with wide gaussians.

In genareal, all gausians seem too small.





Ratios

y0 pions
y.2 pions
y.4 pions
y-.2 pions (I did not feel that I got good fits on pi+s)
y-.4 pions

y0 kaons
y.2 kaons (ratios will not plot)
y.4 kaons
y-.2 kaons(I did not feel that I got good fits on ka-s)
y-.4 kaons(I did not feel that I got good fits on ka-s)

Questions:

Can I use good yeilds from other mass assumptions to improve accuracy where centroids are close?

Should all the CPE graphs look the same for same (my-mo,y) but different species? guess: no because dedx is qualitative.

How does one adjust upper left box on plots? Compare plots side by side?

Is dedx.cal fit done in two parts? it seems to have an elbow





Ideas for better results:

Instead (or in addition too) minimising chi^2, minimise difference between area of gaussians and sum of ordinate values. (Why chi^2? could this lead to sytematic error?)

Show both linear and log scale simultaneously. Each is good for looking at different characteristics.

Further autimate by automaticaly decreasing all del by .1