The LaTeX report
Generated by gurrola on 26 February 2013, 11:27:38
This report has been generated automatically by
MadAnalysis 5.
Please cite:
E. Conte, B. Fuks and G. Serret,
,
MadAnalysis 5, A User-Friendly Framework for Collider Phenomenology
Comput. Phys. Commun.
arXiv:1206.1599 [hep-ph].
184 (2013) 222-256,
To contact us:
http://madanalysis.irmp.ucl.ac.be
[email protected]
Contents
1
2
3
Setup
2
Datasets
5
Histos and cuts
6
1.1
1.2
2.1
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Command history
Conguration
2
4
smur
Histogram
Histogram
Histogram
Histogram
Histogram
Histogram
Histogram
Histogram
5
1
2
3
4
5
6
7
8
6
7
8
9
10
11
12
13
1
1
1.1
Setup
Command history
ma5># set directory where running "./bin/ma5"; set lumi; define the signal significance
ma5>set main.currentdir = /home/gurrola/Desktop
ma5>set main.lumi = 100
ma5>set main.SBratio = 'S/sqrt(S+B)'
ma5># import samples
ma5>import samples/smur.lhe as smur
ma5># define bg and signal samples
ma5>set smur.type = signal
ma5># define weights for the samples
ma5>set smur.weight = 1
ma5># titles for the plots
ma5>set smur.title = "#tilde{#mu}_{R}#tilde{#mu}_{R}jj"
ma5># line styles and colors
ma5>set smur.linecolor = black
ma5>set smur.linestyle = dash-dotted
ma5>set smur.linewidth = 4
ma5># a jet can be from a light quark or b quark
ma5>define jets = j b b
ma5># make plots
ma5>plot PT(jets[1])
ma5>plot ETA(jets[1])
ma5>plot PHI(jets[1])
ma5>plot PT(jets[2])
ma5>plot ETA(jets[2])
ma5>plot PHI(jets[2])
ma5>plot DELTAR(jets[1], jets[2])
ma5>plot M(jets[1] jets[2])
ma5># plot parameters
ma5>set selection[1].xmax = 1000
ma5>set selection[1].xmin = 0
ma5>set selection[1].nbins = 50
ma5>set selection[1].logY = true
ma5>set selection[1].logX = false
ma5>set selection[1].rank = PTordering
ma5>set selection[1].stacking_method = normalize2one
ma5>set selection[1].titleX = "p_{T}[j_{1}] (GeV)"
ma5>set selection[2].xmax = 16
ma5>set selection[2].xmin = -16
ma5>set selection[2].nbins = 320
ma5>set selection[2].logY = false
2
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
ma5>set
selection[2].logX = false
selection[2].rank = PTordering
selection[2].stacking_method = normalize2one
selection[2].titleX = "#eta[j_{1}]"
selection[3].xmax = 3.2
selection[3].xmin = -3.2
selection[3].nbins = 64
selection[3].logY = false
selection[3].logX = false
selection[3].rank = PTordering
selection[3].stacking_method = normalize2one
selection[3].titleX = "#phi[j_{1}]"
selection[4].xmax = 500
selection[4].xmin = 0
selection[4].nbins = 50
selection[4].logY = true
selection[4].logX = false
selection[4].rank = PTordering
selection[4].stacking_method = normalize2one
selection[4].titleX = "p_{T}[j_{2}] (GeV)"
selection[5].xmax = 16
selection[5].xmin = -16
selection[5].nbins = 320
selection[5].logY = false
selection[5].logX = false
selection[5].rank = PTordering
selection[5].stacking_method = normalize2one
selection[5].titleX = "#eta[j_{2}]"
selection[6].xmax = 3.2
selection[6].xmin = -3.2
selection[6].nbins = 64
selection[6].logY = false
selection[6].logX = false
selection[6].rank = PTordering
selection[6].stacking_method = normalize2one
selection[6].titleX = "#phi[j_{2}]"
selection[7].xmax = 30
selection[7].xmin = 0
selection[7].nbins = 150
selection[7].logY = false
selection[7].logX = false
selection[7].rank = PTordering
selection[7].stacking_method = normalize2one
3
ma5>set selection[7].titleX = "#Delta#eta[j_{1},j_{2}]"
ma5>set selection[8].xmax = 10000
ma5>set selection[8].xmin = 0
ma5>set selection[8].nbins = 80
ma5>set selection[8].logY = true
ma5>set selection[8].logX = false
ma5>set selection[8].rank = PTordering
ma5>set selection[8].stacking_method = normalize2one
ma5>set selection[8].titleX = "M[j_{1},j_{2}] (GeV)"
ma5>submit VBFPlots_SmuR
1.2
Conguration
• MadAnalysis version 1.1.5 (2012/11/28).
• Histograms given for an integrated luminosity of 100.0fb−1 .
4
2
Datasets
2.1
smur
• Sample consisting of: signal events.
• Generated events: 50000 events.
• Normalization to the luminosity: 86+/- 1 events.
• Ratio (event weight): 0.0017 .
Path to the event le
Nr. of events
samples/smur.lhe
50000
5
Cross
section
(pb)
0.000866
@
0.16%
Negative
(%)
0.0
wgts
3
Histos and cuts
3.1 Histogram 1
* Plot: PT ( jets[1] )
Table 1.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
29.3596
60.01
0.0
Figure 1.
6
0.0
3.2 Histogram 2
* Plot: ETA ( jets[1] )
Table 2.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
0.0714294
5.258
0.0
Figure 2.
7
0.0
3.3 Histogram 3
* Plot: PHI ( jets[1] )
Table 3.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
0.00849216
1.815
0.0
Figure 3.
8
0.676
3.4 Histogram 4
* Plot: PT ( jets[2] )
Table 4.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
6.57577
12.24
0.0
Figure 4.
9
0.0
3.5 Histogram 5
* Plot: ETA ( jets[2] )
Table 5.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
-0.057858
6.09
0.0
Figure 5.
10 0.0
3.6 Histogram 6
* Plot: PHI ( jets[2] )
Table 6.
Dataset
Integral
Entries
events
smur
1.0
1.0
Statistics table
/
Mean
RMS
Underow Overow
0.00551786
1.811
0.0
Figure 6.
11 0.694
3.7 Histogram 7
* Plot: DELTAR ( jets[1] , jets[2] )
Table 7.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
10.9836
2.734
0.0
Figure 7.
12 0.0
3.8 Histogram 8
* Plot: M ( jets[1] jets[2] )
Table 8.
Dataset
Integral
smur
1.0
Entries
events
1.0
Statistics table
/
Mean
RMS
Underow Overow
1827.32
1290
0.0
Figure 8.
13 0.0