2009.05.03 LDA: varying pt and eta cut

Cut optimization with Fisher's LDA classifier

ROOT implementation for LDA:

Fisher's Linear Discriminant Analysis (LDA)

Application for cuts optimization in the gamma-jet analysis

LDA configuration: default

LDA input parameters Set0:

Set0:
- Energy fraction in 3x3 cluster within a r=0.7 radius:
  E_3x3/E_0.7
- Photon-jet pt balance:
  [pt_gamma-pt_jet]/pt_gamma
- Number of charge tracks within r=0.7 around gamma candidate:
  Ncharge
- Number of Endcap towersL fired within r=0.7 around gamma candidate:
  NtowBarrel
- Number of Barrel towers fired within r=0.7 around gamma candidate
  NtowEndcap
Set1:
- All from Set0
- Shower shape analysis: distance to 80% cut line:
  distance to cut line
Set2:
- All from Set1
- Energy fraction in E_2x1 and E_2x2 witin E_3x3:
  E_2x1/E_2x2 and E_2x2/E_3x3
Set3:
- All from Set2
- Energy in post-shower layer under 3x3 tower patch:
  E_post^3x3

The number of strips in SMD u or v planes is required to be greater than 3

Pre-shower sorting (energy in tiles under 3x3 tower patch):

pre1=0, pre2=0
pre1=0, pre2>0
0 < pre1 < 0.004
0.004 < pre1 < 0.01
pre1 < 0.01
pre1 >= 0.01

Photon pt and rapidity cuts:

pt>7GeV
pt>8GeV
pt>9GeV
pt>10GeV
detector eta <1.4 (pt>7GeV)
detector eta > 1.4 (pt>7GeV)

Figure 0: photon pt distribution for pre-shower1<0.01
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD

LDA Set0

Figure 1: LDA discriminant with Set0: Data to Monte-Carlo comparison (pt>7GeV cut)

Right plot for each pre-shower condition shows the ratio of pp2006 data to sum of the Monte-Carlo samples
Colour coding:
black pp2006 data, red gamma-jet MC, green QCD MC, blue gamma-jet+QCD