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Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Top Mass Reconstructionin
Semi Electronic tt̄ Events
Maryam Zeinali
IPM Weekly Meeting
February 2 2010
1 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Outline
OverviewGolden Channel
Event SelectionSoftware, Sample and Selection Cuts25 GeV: Too Tight?
Jet-Parton MatchingAlgorithmPerformance
Signal and Background DistributionsMonitoring VariablesTraining VariablesTesting the MethodHow MVA Methods work?
2 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Outline
OverviewGolden Channel
Event SelectionSoftware, Sample and Selection Cuts25 GeV: Too Tight?
Jet-Parton MatchingAlgorithmPerformance
Signal and Background DistributionsMonitoring VariablesTraining VariablesTesting the MethodHow MVA Methods work?
2 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Outline
OverviewGolden Channel
Event SelectionSoftware, Sample and Selection Cuts25 GeV: Too Tight?
Jet-Parton MatchingAlgorithmPerformance
Signal and Background DistributionsMonitoring VariablesTraining VariablesTesting the MethodHow MVA Methods work?
2 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Outline
OverviewGolden Channel
Event SelectionSoftware, Sample and Selection Cuts25 GeV: Too Tight?
Jet-Parton MatchingAlgorithmPerformance
Signal and Background DistributionsMonitoring VariablesTraining VariablesTesting the MethodHow MVA Methods work?
2 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal?
To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How?
Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio?
19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature?
An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?
? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,
? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,
? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,
. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!
? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Overview
I Goal? To measure the top quark mass
I How? Using tt̄ events, semi electronic decay channel
I Branching ratio? 19 ∗
69 ∗ 2 = 4
27 ≈ 15%
I Event signature? An energetic electron, four jets thattwo of them are b jets and missing transverse energy
I Backgrounds to suppress?? W+jets,? QCD,? single top,. . . but non of them are really matters!? The main background to top mass measurementcomes from the signal itself!
3 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Golden Channel: Lepton + Jets Final State
I Leptonic side is used for triggering purposesI Four undistinguishable reconstructed jets at final state:
12 possible ways to reconstruct hadronic top 4-vector!4 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection Cuts
I CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is used
I TTbar PYTHIA is skimmed at GenLevel to select onlysemi electronic events
I At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic events
I At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829
Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925
>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587
== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?
5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Software, Sample and Selection CutsI CMSSW 3 1 4 is usedI TTbar PYTHIA is skimmed at GenLevel to select only
semi electronic eventsI At least 4 jets are required to pass ET > 25GeV and|η| < 2.4
I Exactly one electron is asked to have these criteria:? pt > 20GeV and |η| < 2.4 excluding the gap? identified as RobustTight? reliso which is defined as the sum of all isolationvalues in a cone of 0.3 over electron ET should be lessthan 0.1
I Cut flow table:
Initial tt̄ events 485829Initial semi ele events 70925>= 4 Jet 46587== 1 Electron 21822
⇒ 66% pass cuts on jets. Cuts are abnormal?5 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Distribution of Partons’pt in Semi ElectronicEvents
Leading jet pt Next leading jet pt
Next to next leading jet pt Next to next to next leading jet pt
6 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Algorithm
1. All events survived the selection cuts are fed to thejet-parton matching algorithm
2. Among four hard scattered partons, take the one withthe hardest pt .
3. Make a loop over all jets in the event. No cut is put onjets in the loop.
4. The jet with the minimum distance to the parton isflagged.
5. If ∆r < 0.3, jet is called as matched and removed fromthe list of jets.
6. Take the next leading parton and go to stp 2.
7 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background
? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,
? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.
? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.
? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Jet-Parton Matching Performance
I How the method works?
I Let’s define right and wrong jet combinations: Signaland Background? Take four leading jets in the event,? If all of them are matched to the partons, only oneamong 12 possible combinations contribute to signal.The other 11 possible ways take part in backgrounds.? If not all four leading jets matched to the partons,then all 12 combinations are considered as backgrounds.? For illustration: consider the number of initial eventsto be N and number of events with exactly 4 leadingjets have matched to the partons is S . Then thenumber of background contributions, B, can becalculated: B = S ∗ 11 + (N − S) ∗ 12.
I Now to see how good matching algorithm is, we cancompare Signal combination to the Gen Info.
8 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Mass of hadronic W− right jet combination
− Gen Info
9 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Mass of hadronic W: LOG scale− right jet combination
− Gen Info
10 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Pt of hadronic W− right jet combination
− Gen Info
11 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Mass of hadronic top− right jet combination
− Gen Info
12 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Mass of hadronic top: LOG scale− right jet combination
− Gen Info
13 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Pt of hadronic top− right jet combination
− Gen Info
14 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
∆φ(1st lightjet, 2nd lightjet)− right jet combination
− Gen Info
15 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
∑i p
lightjetit
− right jet combination
− Gen Info
16 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
∆φ(leptonicB , electron)− right jet combination
− Gen Info
17 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
∆φ(hadronicB , hadronicW )− right jet combination
− Gen Info
18 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
∆φ(leptonicB , hadronicW )− right jet combination
− Gen Info
19 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Monitoring variables that can discriminatebetween right and wrong jet combinations
20 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Monitoring variables that can discriminatebetween right and wrong jet combinations
21 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Monitoring variables that can discriminatebetween right and wrong jet combinations
22 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Correlation of variables for right jet combinations
23 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Correlation of variables for wrong jetcombinations
24 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Training Variables with TMVA: LikelihoodMethod
25 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Testing of the MVA Method: Likelihood
26 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Testing of the MVA Method: Likelihood
26 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Evaluation of the Results: Likelihood Method
27 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Evaluation of the Results: PEDRS Method
28 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Background Rejection Vs Signal Efficiency:Comparing Likelihood and PEDRS Methods
29 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Likelihood Method
Projective likelihood estimator (PDE approach)
I Builds a model out of probability density functions
I Reproduces the input variables for signal and background
I For a given event, the likelihood for being of signal type is
obtained by multiplying the signal probability densities of all
input variables, which are assumed to be independent, and
normalising this by the sum of the signal and background
likelihoods
I Because correlations among the variables are ignored, this
PDE approach is also called naive Bayes estimator
Classifier
I The likelihood ratio for event i , yL(i), is defined by:
yL(i) = LS (i)LS (i)+LB (i) where LS(B)(i) =
∏nvark=1 pS(B),k(xk(i))
? nvar is the number of input variables used
30 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Likelihood Method
Projective likelihood estimator (PDE approach)
I Builds a model out of probability density functions
I Reproduces the input variables for signal and background
I For a given event, the likelihood for being of signal type is
obtained by multiplying the signal probability densities of all
input variables, which are assumed to be independent, and
normalising this by the sum of the signal and background
likelihoods
I Because correlations among the variables are ignored, this
PDE approach is also called naive Bayes estimator
Classifier
I The likelihood ratio for event i , yL(i), is defined by:
yL(i) = LS (i)LS (i)+LB (i) where LS(B)(i) =
∏nvark=1 pS(B),k(xk(i))
? nvar is the number of input variables used
30 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
PDERS MethodMultidimensional likelihood estimator (PDE range-search approach)
I A generalization of the projective likelihood classifer to nvardimensions
I If the multidimensional PDF for signal and background were
known, this classifer would exploit the full information
contained in the input variables, and would hence be optimalI The PDE for a given test event (discriminant) is obtained by
counting the (normalised) number of training events that
occur in the ”vicinity” of the test eventI The classifcation of the test event may then be conducted on
the basis of the majority of the nearest training events
Classifier
I PDERS method takes the ratio yPDERS(i ,V ) =nS (i,V )
NSnS (i,V )
NS+
nB (i,V )
NB
as the estimator by defining a volume V around the test
event, i , and by counting the number of signal, nS(i ,V ) and
background events nB(i ,V ) obtained from the training
sample in that volume while NS(B) is the total number of
signal (background) events in the training sample
31 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
PDERS MethodMultidimensional likelihood estimator (PDE range-search approach)
I A generalization of the projective likelihood classifer to nvardimensions
I If the multidimensional PDF for signal and background were
known, this classifer would exploit the full information
contained in the input variables, and would hence be optimalI The PDE for a given test event (discriminant) is obtained by
counting the (normalised) number of training events that
occur in the ”vicinity” of the test eventI The classifcation of the test event may then be conducted on
the basis of the majority of the nearest training events
Classifier
I PDERS method takes the ratio yPDERS(i ,V ) =nS (i,V )
NSnS (i,V )
NS+
nB (i,V )
NB
as the estimator by defining a volume V around the test
event, i , and by counting the number of signal, nS(i ,V ) and
background events nB(i ,V ) obtained from the training
sample in that volume while NS(B) is the total number of
signal (background) events in the training sample
31 / 32
Top MassReconstruction
inSemi Electronic tt̄
Events
Maryam Zeinali
Overview
Golden Channel
Event Selection
Software, Sample andSelection Cuts
25 GeV: Too Tight?
Jet-PartonMatching
Algorithm
Performance
Signal andBackgroundDistributions
Monitoring Variables
Training Variables
Testing the Method
How MVA Methodswork?
Outlook and To Do List
I Although by applying event selection cuts thebackgrounds to the signal can be suppressed, top massreconstruction is suffering from a huge contribution ofcombinatorial backgrounds.
I The results reported are obtained by running TMVApackage based on ROOT. There is anotherimplimintaion of MVA methods based on CMSSWenvironment.
I The final analysis has to be moved to CMSSW framework sooner or later. Then it needs to synchronize thecode accordingly.
32 / 32