ZZ -> nunuqq Analysis - Run 2 (13 TeV)

ZZ -> nunuqq Analysis - Run 2 (13 TeV)
- ANALISIS NOTE : AN-15-220
- PAS NOTE : B2G -16-014
TO DO LIST
STATISTICS (LIMITS)

We are starting this analysis with JETS and Missing Energy (MET) in the final state

ANALISIS NOTE : AN-15-220

Instructions to download and compile the note:

On an lxplus machine, get the document by doing:

> svn co -N svn+ssh://svn.cern.ch/reps/tdr2 myDir # where myDir is a placeholder for a name of your choice
> cd myDir
> svn update utils
> svn update -N notes
> svn update notes/AN-15-220
> eval `./notes/tdr runtime -csh` # for tcsh. use -sh for bash.
> cd notes/AN-15-220/trunk
# (edit the template, then to build the document)
> tdr --style=an b AN-15-220

The last command produce the pdf file:

AN-15-220_temp.pdf

located in :

../myDir/notes/tmp

you can use gnome to open the document:

gnome-open AN-15-220_temp.pdf

Note that the configuration file for subversion is located in:

/afs/cern.ch/user/d/dromeroa/.subversion

To run the pas for now in dropbox

./tdr build B2G -16-999.tex

PAS NOTE : B2G -16-014

cd myDiR

eval `./notes/tdr runtime -sh` 

cd /notes/B2G-16-014/trunk

tdr --style=pas b B2G-16-014

TO DO LIST

TASK	STATUS	EXPECT DEADLINE
Study N-1 cut to test the efficiency of our cut flow	done
Study the correlation between variables to select 2 of them to apply ABCD method	ongoing	2 (after trees prod.)
Estimate the shape of the QCD distribution in MT for signal region (to do shape analysis)	not yet started	2 (after trees prod.)
Do plots of: dR(fatJet, LeadingAk4) and dR(fatJet, SubLeadingAk4) for signal events both: 1 and 2 TeV all in the same plot and normalized	not yet started
Find cross-section for 2TeV RSG samples from PHYS14 (ask Jose???) --then include this samples in your cut-flow table	done
Verify if in the defined regions we have enough statistics to compare the shapes of the distributions --Important check if the new QCD MCs in 7_4_X have better stats than PHYS14 samples (find out the number of events of the samples and if is neccessary talk with the combiners) Watch the exercise from DAS to find out the samples	not yet started	2 (after trees prod.)
Reference Trigger (PFHT475) Do the plot in function of HT Implement Trigger options in the EDBR2 in order to be ready to test with data --define a strategy for trigger efficiency computation in data: in principle we plan reference method --and the reference trigger can be defined using MC (check if is unbiased) --For the paths that consider HT variable, do trigger eff as a function of HT --Then add HT variable in the trees and do control plots for this variable as well. Define it as: HT = Scalar sum of pT for all ak4jets with pT>30GeV	in progress	1 (for new trees)
Include Photon vetoes in the baseline selection (To be tested with PHYS14 samples) now with SPRING15 ---is important to do a study of the mitigation of the process WGamma+Jets after this veto. Then we use the samples from WW semilpetonic analysis (WGamma+Jets) for these studies	in progress	1 (for new trees)
For PHYS14 (for SPRING 15) samples studies...include WGamma+Jets and Z>ll+Jets (see study above)	skip	1 (for new trees)
Migrate to CMSSW_7_4_X with the EDBR2	done
Redo the optimization of Njets and dphi(jet,jet) using 7_4_X (probably include MT>600GeV in the baseline selection)	not yet started	3 (after QCD study)
Investigate the MET no Mu and no electrons	not yet started
Investigate selection to mitigate QCD	not yet started	2 (after trees prod.)
Add new variables for QCD mitigation	done
Think about include b tag veto for the ttbar samples	not yet started	3 (after QCD study)
Do plots for JET loose ID usando todos los backgrounds y usar los cortes de monojet
Do efficieny plots for MET filters, hacer un fit a una constante y obtener el valor de la eficiencia
Indicar que significa loose en el filter, cuales filters se estan usando y cuales HLT estamos usando

NEW FILTERS MET and MET reccomendations

https://twiki.cern.ch/twiki/bin/viewauth/CMS/MissingETOptionalFiltersRun2

https://twiki.cern.ch/twiki/bin/view/CMS/MissingETUncertaintyPrescription#Instructions_for_7_4_X

MET FILTERS CODE

1. HBHENoiseFilter

http://cmslxr.fnal.gov/source/CommonTools/RecoAlgos/plugins/HBHENoiseFilterResultProducer.cc

2. CSCTightHaloFilter

http://cmslxr.fnal.gov/source/RecoMET/METFilters/plugins/CSCTightHaloFilter.cc

http://cmslxr.fnal.gov/source/RecoMET/METFilters/plugins/CSCTightHalo2015Filter.cc

3. EEBadScFilter

cmslxr.fnal.gov/source/RecoMET/METFilters/plugins/EEBadScFilter.cc

4. Primaryvertexfilter

http://cmslxr.fnal.gov/source/RecoMET/METFilters/python/primaryVertexFilter_cfi.py

HBHE Noise Filter Recipe

https://twiki.cern.ch/twiki/bin/view/CMS/HCALNoiseFilterRecipe

MET RECIPIES

https://twiki.cern.ch/twiki/bin/viewauth/CMS/MissingETRun2Corrections

https://twiki.cern.ch/twiki/bin/view/CMS/MissingETUncertaintyPrescription#Instructions_for_7_4_X

https://twiki.cern.ch/twiki/bin/view/CMS/JECDataMC#Recommended_for_MC

check the git:

https://github.com/UHH2/UHH2/blob/master/core/python/ntuplewriter.py

https://github.com/cms-sw/cmssw/blob/CMSSW_7_4_X/PhysicsTools/PatAlgos/test/corMETFromMiniAOD.py

https://github.com/cms-sw/cmssw/blob/CMSSW_7_4_X/PhysicsTools/PatUtils/python/tools/runMETCorrectionsAndUncertainties.py

For last JEC in db

https://github.com/cmstas/NtupleMaker/wiki/How-to-find-the-latest-JEC-payloads-with-CMS3-tag-CMS3_V07-04-08-and-beyond

Not possible use METnoHF prescription for MC in CMSSW_7_4_12, in data this collection is available, have to wait for new MC, or run in 7_4_11 even data?

https://hypernews.cern.ch/HyperNews/CMS/get/met/411.html

To rerun MET:

https://github.com/UZHCMS/EXOVVNtuplizerRunII/blob/master/Ntuplizer/config_data.py

To check how access some objects in miniAOD

https://github.com/cms-sw/cmssw/blob/CMSSW_7_4_14/DataFormats/PatCandidates/interface/Electron.h

For check what the global tag contains

https://cms-conddb.cern.ch/browser/

MC SAMPLES EXOTICA

https://twiki.cern.ch/twiki/bin/viewauth/CMS/EXOTICAMC

1. Theory

2. Signal Topology

We will search for a heavy resonance X in the channel:

pp -> X -> ZZ -> 2nu2q (1 V-tagged jet) (1 fat jet)

Why this channel? What is interisting on it?

Why only gluon fusion?

In this signal topology, the final state are a fat jet and missing energy.

Cone aperture : R ≈ 2m/pt

By conservation of momentum, the resonance will be created almost at rest, so the Z bosons decay back-to back

3. Signal Models

Several Extensions to the Standard Model predict new massive particles which can decay to heavy boson pairs.

Neutral Resonances (ZZ):

Here we present some of the most popular models that predicts the existence of a new heavy neutral resonance which decay in ZZ:

(We have to include the plots of the branching ratio)

Randall-Sundrum Graviton (RS G*)

Traditional benchmark model with extradimension
Spin-2 KK excitation
qq annihilation and gluon fusion production.
BRs are democratic

Bulk RS graviton (Bulk G*)

Spin 2 KK excitations
Inclusive production is gluon fusion
BRs to massive particles are dominant

Radion

Spin-0 KK excitation
Inclusive production is gluon fusion
BRs to massive particles are dominant

Z boson braching ratio

From PDG:

The Z boson have three decay channels:

• Br(Z -> nunu) ≈ 20 % -> 0.2
• Br(Z –> ll ) ≈ 10 % -> 0.1
• Br(Z -> hadrons ) ≈ 69.91% -> 0.6991

Br(W -> hadrons) ≈ 67.41% -> 0.6741

and for our process:

BF(Z->nunu,Z->qq) = 2* (21/100)*(69/100) ≈ 0.29 = 29%

When we run the program after generator- level selection, we obtain:

For W:

BF(Z->nunu,W->qq) = 2* (20/100)*(67.41/100) ≈ 0,26964 = 26.9 %

4. Main Backgrounds

It will be good to find out which is the level of accuracy of this samples, LO, NLO?

Background	Specific Decay	Observation	Irreductible	DAS Path (PHYS 14 Samples location)
Z + jets	Z -> nu nu			https://cmsweb.cern.ch/das/request?view=list&limit=100&instance=prod%2Fglobal&input=dataset+dataset%3D%2FZJetsToNuNu%2F%2FMINIAODSIM+site%3DT2_BR_SPRACE
W + jets	W -> l + nu	the lepton is not detected/ poorly reconstructed/ the lepton is a tau (tau-jet)		https://cmsweb.cern.ch/das/request?view=list&limit=100&instance=prod%2Fglobal&input=dataset+dataset%3D%2FWJetsToLNu*%2FPhys14DR-PU20bx25_PHYS14_25_V1-v1%2FMINIAODSIM+site%3DT2_BR_SPRACE
ttbar + jets	t -> W +b -> l +nu b			https://cmsweb.cern.ch/das/request?view=list&limit=10&instance=prod%2Fglobal&input=dataset+dataset%3D%2FTTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola%2FPhys14DR-PU20bx25_PHYS14_25_V1-v1%2FMINIAODSIM+site%3DT2_BR_SPRACE
QCD	quarks and gluons (jets) in the final state Multijets?	Possibility to reconstruct fake objects in the detector (jets, leptons and misssing energy)		https://cmsweb.cern.ch/das/request?view=list&limit=10&instance=prod%2Fglobal&input=dataset%3D%2FQCD_HTmadgraph%2FPhys14DR-PU20bx25_PHYS14_25_V1%2FMINIAODSIM
Pair o vector bosons : WW, ZZ, WZ	ZZ -> qqnunu WZ -> qq’nunu WW ->qq’ lnu

5. Variables to discriminate between Signal and Background

Leading Jet pT of the event
Missing trasnverse energy
Number of jets in the event with pT >30 GeV
Azimuthal distance between the leading and subleading jet in the event
Leading jet invariant mass of the event
Jet-MET transverse mass of the event (Graviton transverse mass)

6. Validation Plots

Location in access:

/home/davidromero/ANALISIS_TESIS_Zhad_Znunu/CMSSW_7_2_2_patch1/src/PrivateCode/MiniAnalyzer/python

7. Trigger Studies

Selection:

JETS
- pT > 100 GeV
- abs(eta) < 2.4
- 60 GeV < pruned jet mass < 110 GeV
MET
- pT > 40 GeV

The Efficiency definition:

Inspect over PHYS 14 tsg samples

Tested Trigger Paths:
[1] HLT_AK8PFJet360TrimMod_Mass30_v1
[2] HLT_PFMET170_NoiseCleaned_v1
[3] HLT_PFHT350_PFMET120_NoiseCleaned_v1
[4] HLT_PFHT900_v1
[5] [1] OR [2]
[6] [2] OR [3]

8. JETS

https://twiki.cern.ch/twiki/bin/view/CMS/JetID#Recommendations_for_13_TeV_data

the file we are using to apply the jet ID

http://cmslxr.fnal.gov/source/PhysicsTools/SelectorUtils/interface/PFJetIDSelectionFunctor.h

JEC UNCERTAINTIES

https://hypernews.cern.ch/HyperNews/CMS/get/jes/568/1.html

https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyCorrections#JetCorUncertainties

JET ENERGY RESOLUTION

https://twiki.cern.ch/twiki/bin/viewauth/CMS/JetResolution

https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyResolution

https://github.com/blinkseb/cmssw/blob/jer_fix_76x/JetMETCorrections/Modules/plugins/JetResolutionDemo.cc#L74

To test the implementation

https://twiki.cern.ch/twiki/bin/view/CMS/JERCReference

code:

https://github.com/cms-jet/JMEReferenceTable

For use the Smeared procedure have to add:

git cms-merge-topic blinkseb:smeared_jet_producer

JEC uncertainties sources

https://twiki.cern.ch/twiki/bin/view/CMS/JECUncertaintySources

Mandatory Jet Energy Corrections at CMS.

The minimum correction levels to be applied on any CMS analysis using Monte Carlo and Data are:

Monte Carlo	L1 + L2L3 MC-truth
Data	L1 + L2L3 MC-truth + L2L3Residuals

Any analysis might place higher correction levels if necessary and available. Software instructions for applying these corrections can be found here.

8.1 PILE UP JET ID

https://twiki.cern.ch/twiki/bin/viewauth/CMS/PileupJetID#Information_for_13_TeV_data_anal

Also check:

https://indico.cern.ch/event/450785/contribution/4/attachments/1167544/1683856/PUIntegration.pdf

classes for calculating MVA ID

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/RecoJets/JetProducers/interface/MVAJetPuId.h

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/RecoJets/JetProducers/interface/PileupJetIdAlgo.h

They are called from

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/RecoJets/JetProducers/plugins/MVAJetPuIdProducer.cc

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/RecoJets/JetProducers/plugins/PileupJetIdProducer.cc

Main configuration files:

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/RecoJets/JetProducers/python/PileupJetID_cfi.py

Variables for ID are stored in : PileupJetIdentier.h

https://github.com/cms-sw/cmssw/blob/CMSSW_8_0_X/DataFormats/JetReco/interface/PileupJetIdentifier.h

weights

https://github.com/cms-data/RecoJets-JetProducers

B TAG

1. Reccomendations for 13 TeV

https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation76X

2. Btag POG

https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagPOG

3. Reccomendations and scale factors

https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation

papers:

http://cds.cern.ch/record/2138504/files/BTV-15-001-pas.pdf

http://iopscience.iop.org/article/10.1088/1748-0221/8/04/P04013/pdf

9. LEPTONS IDENTIFICATION

Electrons:

https://twiki.cern.ch/twiki/bin/viewauth/CMS/CutBasedElectronIdentificationRun2

Muons

https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideMuonIdRun2

Taus

https://twiki.cern.ch/twiki/bin/view/CMS/TauIDRecommendation13TeV

How to use weights QCD LHE

https://twiki.cern.ch/twiki/bin/viewauth/CMS/LHEReaderCMSSW#How_to_use_weights

Punzi Significance

Reference http://arxiv.org/pdf/physics/0308063v2.pdf

The Punzi significance is defined by:

Ps = efficiency of the signal / (1 + sqrt (Background events) )

for each cut that we want to optimize.

Where

efficiency of the signal = events that pass the cut / total number of events

For the Background events, we have to take in account that we could have different samples of backgrounds, in that case:

Background events = Sum over samples of : (weight) * (Number of backgrounds evetns after the cut)

where weight is = Luminosity of the data (target) / Luminosity of the Background sample

In our case :

Luminosity of the data = 3 / fb (our target luminosity)

Luminosity of the Backgroubd = Number of events of the sample (total) / cross section of the sample

Note that for the weight we need to use the entire number of events from the background sample (without cut).

Example:

Suppose we have to samples of Background :

Sample Number of events Cross section

B1 1000 10

B2 20000 20

Suppose we apply a cut call cut1, so after cut 1:

Sample Number of events

B1 300

B2 150

So the Background events = w1* ( 300) + w2 * (150)

where w1 = 3 / (1000/10) = 0.03

where w2 = 3 / (20000/20) = 0.003

Cut Flow table

Table of weights

NEW SAMPLES RunIISpring15DR74 25ns

Have to check, not all valid!

have to update the cross sections from exoVV page and put the xsec and number of events

QCD

dataset= /QCD_HT100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v2/MINIAODSIM

dataset= /QCD_HT200to300_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v2/MINIAODSIM

dataset=/QCD_HT300to500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v2/MINIAODSIM

dataset=/QCD_HT500to700_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/QCD_HT700to1000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset= /QCD_HT1000to1500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v2/MINIAODSIM

dataset=/QCD_HT1500to2000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/QCD_HT2000toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

Z + jets

dataset=/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/ZJetsToNuNu_HT-200To400_13TeV-madgraph/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/ZJetsToNuNu_HT-400To600_13TeV-madgraph/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset= /ZJetsToNuNu_HT-600ToInf_13TeV-madgraph/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

W + jets

dataset=/WJetsToLNu_HT-100To200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/WJetsToLNu_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

dataset=/WJetsToLNu_HT-400To600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v3/MINIAODSIM

dataset=/WJetsToLNu_HT-600ToInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM

TTJets

As a recommendation we have to use the Powheg

dataset=/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v2/MINIAODSIM

RSGravToZZ

dataset=/*RSGravToZZ_kMpl01*/RunIISpring15DR74-Asympt*25*/MINIAODSIM

MINIOAD V2 MC BACKGROUND SAMPLES

Z + Jets

SAMPLE (LO)	X SECTION (pb)	NUMBER OF EVENTS
/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	280.47	5154824
/ZJetsToNuNu_HT-200To400_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	78.36	4998316
/ZJetsToNuNu_HT-400To600_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	10.944	1018882
/ZJetsToNuNu_HT-600ToInf_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v2/MINIAODSIM	4.203	1008333

W + Jets

SAMPLE (NLO?) * (scale factor k = 1.2)	X SECTION (pb)	NUMBER OF EVENTS
/WJetsToLNu_HT-100To200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	1345 ± 1.2	10152718
/WJetsToLNu_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	359.7 ± 0.20	5221599
/WJetsToLNu_HT-400To600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	48.91 ± 0.072	1745914
/WJetsToLNu_HT-600ToInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	18.77 ± 0.10	1039152

QCD

SAMPLE	X SECTION (pb)	NUMBER OF EVENTS
/QCD_HT100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	27540000.0	81637494
/QCD_HT200to300_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	1735000.0	18718905
/QCD_HT300to500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	366800.0	19826197
/QCD_HT500to700_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	29370.0	19664159
/QCD_HT700to1000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	6524.0	15356448
/QCD_HT1000to1500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	1064.0	4963895
/QCD_HT1500to2000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	121.5	3868886
/QCD_HT2000toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	25.42	1912529

TTbar

SAMPLE	X SECTION (pb)	NUMBER OF EVENTS
/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	831.76	19757190

Dibosons

SAMPLE (WW->NNLO), (WZ->NLO), (ZZ->NLO) ** Some scale factor	X SECTION (pb)	NUMBER OF EVENTS
/WW_TuneCUETP8M1_13TeV-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	118.7	993640
/WZ_TuneCUETP8M1_13TeV-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	47.13	978512
/ZZ_TuneCUETP8M1_13TeV-pythia8/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	16.523	996944

*Note: MLM (from madgraphMLM, MLM stand from MLMangano, matching implemented in ALPGEN see : http://arxiv.org/pdf/hep-ph/0206293v2.pdf

)

take from : https://cp3.irmp.ucl.ac.be/projects/madgraph/wiki/Matching

The aim of any parton-jets matching procedure is mainly to avoid overlapping between phase-space descriptions given by matrix-element generators and showering/hadronization softwares in multi-jets process simulation. The motivation for using both at the same time is the following:

The Parton Shower (PS) Monte Carlo programs such as Pythia and Herwig describe parton radiation as successive parton emissions using Markov chain techniques based on Sudakov form factors. This description is formally correct only in the limit of soft and collinear emissions, but has been shown to give a good description of much data also relatively far away from this limit. However, for the production of hard and widely separated QCD radiation jets, this description breaks down due to the lack of subleading terms and interference. For that case, it is necessary to use the full tree-level amplitudes for the heavy particle production plus additional hard partons.
The Matrix Element (ME) description diverges as partons become soft or collinear, while the parton shower description breaks down when partons become hard and widely separated

We can distinguish two different philosophies/method types: either based on shower veto and therefore a event reweighting (CKKW method) or events rejection. The latter is the method adopted in the MLM-based schemes. Note that in the CKKW case, partons are clustered in jets with the $K_{T}$ algorithm while the original MLM method uses a cone algorithm and minimum $P_{T}$ cut. In MadGraph /MadEvent, there are currently three matching schemes implemented, all based on MLM method. They are called cone- and $K_{T}$ -jet MLM and Shower- $K_{T}$ respectively. In all cases the parton shower generator is Pythia.

MINIOAD V2 MC SIGNAL SAMPLES

SAMPLES	X SECTION (pb)	NUMBER OF EVENTS
/BulkGravToZZToZhadZinv_narrow_M-600_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-800_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-1000_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	0.0002421604	96800
/BulkGravToZZToZhadZinv_narrow_M-1200_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-1400_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	2.4680202121xE-5	98400
/BulkGravToZZToZhadZinv_narrow_M-1600_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-1800_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-2000_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM	2.7913818154xE-6	99400
/BulkGravToZZToZhadZinv_narrow_M-2500_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-3000_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-3500_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-4000_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM
/BulkGravToZZToZhadZinv_narrow_M-4500_13TeV-madgraph/RunIISpring15MiniAODv2-74X_mcRun2_asymptotic_v2-v1/MINIAODSIM

Information

https://github.com/syuvivida/DibosonBSMSignal_13TeV/tree/master/Spin-2

https://github.com/acarvalh/Cross_sections_CMS/blob/master/WED/bulk_KKgrav_decay.txt

https://github.com/acarvalh/Cross_sections_CMS/blob/master/WED/bulk_KKgrav_LHC13.txt

DATA SAMPLES 76X

1. 2015D

dataset=/MET/Run2015D-16Dec2015-v1/MINIAOD

Number of events: 17996789

python file already in the git

2. 2015C

dataset=/MET/Run2015C_25ns-16Dec2015-v1/MINIAOD

Number of events: 106269

python file already in git

MC SAMPLES 76X

Could be v1 or v2 in RunIIFall15MiniAODv*

QCD

dataset=/QCD_HT*_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv*-PU25nsData2015v1_76X_mcRun2_asymptotic_*/MINIAODSIM

Version 2

1. dataset=/QCD_HT200to300_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 18784379

2. dataset=/QCD_HT300to500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 16909004

3. dataset=/QCD_HT500to700_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 19665695

4. dataset=/QCD_HT700to1000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 15547962

5. dataset=/QCD_HT1000to1500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 5049267

6. dataset=/QCD_HT1500to2000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 3939077

7. dataset=/QCD_HT2000toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 1981228

Z + jets (Z-> nu nu)

dataset=/ZJetsToNuNu_HT*_13TeV-madgraph/RunIIFall15MiniAODv*-PU25nsData2015v*_76X_mcRun2_asymptotic_*/MINIAODSIM

Version2

1. dataset=/ZJetsToNuNu_HT-100To200_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 5240199

2. dataset=/ZJetsToNuNu_HT-200To400_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 5135542

3. dataset=/ZJetsToNuNu_HT-400To600_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 954435

4. dataset=/ZJetsToNuNu_HT-600ToInf_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 1033818

W + jets

dataset=/WJetsToLNu*HT*/RunIIFall15MiniAODv*-PU25nsData2015v1_76X_mcRun2_asymptotic_*/MINIAODSIM

Version2

1. dataset=/WJetsToLNu_HT-100To200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 10205377

2. dataset=/WJetsToLNu_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 4949568

3. dataset=/WJetsToLNu_HT-400To600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 1943664

4. dataset=/WJetsToLNu_HT-600ToInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 1041358

TT Jets

dataset=/TTJets*/RunIIFall15MiniAODv*-PU25nsData2015v*_76X_mcRun2_asymptotic_*/MINIAODSIM

Version2

dataset=/TTJets_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 38475776

dataset=/TTJets_13TeV-amcatnloFXFX-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext1-v1/MINIAODSIM

Number of events: 196937036

Dibosons

dataset=/WW*/RunIIFall15MiniAODv*-PU25nsData2015v*_76X_mcRun2_asymptotic_*/MINIAODSIM

Version2

dataset=/WW_TuneCUETP8M1_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 988418

dataset=/ZZ_TuneCUETP8M1_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 985600

dataset=/WZ_TuneCUETP8M1_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

Number of events: 1000000

Signal Samples (for now)

dataset=/RSGravToZZToZZinv_narrow_M-1000_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM

DATA 2015 - 2016

1. dataset: /MET/Run2015B-PromptReco-v1/MINIAOD

Creation time: 2015-07-10 00:47:38, Dataset size: 579.2MB, Number of blocks: 7, Number of events: 30011, Number of files: 9, Physics group: NoGroup, Status: VALID, Type: data

Runs

251161, 251162, 251163, 251164, 251167, 251168, 251244, 251251, 251252

Config

cmsRun
Creation time: 2015-07-06 20:53:45, Global Tag: 74X_dataRun2_Prompt_v0, Pset hash: GIBBERISH, Release: CMSSW_7_4_6_patch6

2. Dataset: /Jet/Run2015B-PromptReco-v1/MINIAOD
Creation time: 2015-07-08 19:23:49, Dataset size: 17.0MB, Number of blocks: 10, Number of events: 15, Number of files: 14, Physics group: NoGroup, Status: VALID, Type: data

3. dataset=/HTMHT/Run2015B-PromptReco-v1/MINIAOD

Creation time: 2015-07-10 00:44:32, Dataset size: 872.4MB, Number of blocks: 8, Number of events: 43141, Number of files: 9, Physics group: NoGroup, Status: VALID, Type: data

Runs: 251161, 251162, 251163, 251164, 251167, 251168, 251244, 251251, 251252

Config: cmsRun
Creation time: 2015-07-06 20:53:45, Global Tag: 74X_dataRun2_Prompt_v0, Pset hash: GIBBERISH, Release: CMSSW_7_4_6_patch6

4. Dataset : /SingleMuon/Run2015B-PromptReco-v1/MINIAOD

Creation time: 2015-07-10 01:35:03, Dataset size: 62.0GB, Number of blocks: 27, Number of events: 3633477, Number of files: 57, Physics group: NoGroup, Status: VALID, Type: data

5. dataset=/SingleMuon/Run2015B-05Aug2015-v1/MINIAOD

6. dataset= /MET/Run2015B-05Aug2015-v1/MINIAOD

JSON and/or DSCOnly

For ReReco data 76X

https://cms-service-dqm.web.cern.ch/cms-service-dqm/CAF/certification/Collisions15/13TeV/Reprocessing/

Others:

https://cms-service-dqm.web.cern.ch/cms-service-dqm/CAF/certification/Collisions15/13TeV

To select some specific run ranges over the json files, see:

https://twiki.cern.ch/twiki/bin/view/CMSPublic/SWGuideGoodLumiSectionsJSONFile#printJSON_py

In our case:

filterJSON.py --min 251161 --max 251562 Cert_246908-254349_13TeV_PromptReco_Collisions15_JSON_v2.txt --output golden17Jul2015.json

filterJSON.py --min 251604 --max 253620 Cert_246908-254349_13TeV_PromptReco_Collisions15_JSON_v2.txt --output goldenpropreco.json

Combining the "17Jul2015 (run<=251562)" + PromptReco dataset (run>251562)

for MET sample, the runs:

(17JUL2015)

251161, 251162, 251163, 251164, 251167, 251168, 251244, 251251, 251252, 251521, 251522, 251559, 251560, 251561, 251562

(PROMOT-RECO)

251161, 251162, 251163, 251164, 251167, 251168, 251244, 251251, 251252, 251493, 251496, 251497, 251498, 251499, 251500, 251521, 251522, 251548, 251559, 251560
251561, 251562, 251604, 251612, 251638, 251642, 251643, 251721, 251781, 251883, 252102, 252116, 252126, 252488, 252496, 252499, 252501, 253620

acoording with the recipe of MET filter twiki.

Brilcalc

http://cms-service-lumi.web.cern.ch/cms-service-lumi/brilwsdoc.html

How install each time we need:

1. export PATH=$HOME/.local/bin:/afs/cern.ch/cms/lumi/brilconda-1.0.3/bin:$PATH

2. If we dont have, need to install brilconda (check before, we have already install)

wget https://cern.ch/cmslumisw/installers/linux-64/Brilconda-1.0.3-Linux-x86_64.sh

bash Brilconda-1.0.3-Linux-x86_64.sh3. pip uninstall -y brilws

4. pip install --install-option="--prefix=$HOME/.local" brilws

Then use.

How use:

brilcalc lumi --normtag /afs/cern.ch/user/c/cmsbril/public/normtag_json/OfflineNormtagV1.json -i anyjson.txt -u /pb

If want you can change : -u /pb -> -u /fb

If want to include a HLT path use for example:

brilcalc lumi --hltpath "HLT_PFMETNoMu90_JetIdCleaned_PFMHTNoMu90_IDTight_v*" --normtag /afs/cern.ch/user/c/cmsbril/public/normtag_json/OfflineNormtagV1.json -i myjson26Nov.txt -u /fb

For Moriond

brilcalc lumi --hltpath "HLT_PFMETNoMu90_JetIdCleaned_PFMHTNoMu90_IDTight_v*" --normtag /afs/cern.ch/user/l/lumipro/public/normtag_file/moriond16_normtag.json -i myjson26Nov.txt -u /fb

edmDumpeventContent

For MET data:

edm::TriggerResults "TriggerResults" "" "HLT"
HcalNoiseSummary "hcalnoise" "" "RECO"
L1GlobalTriggerReadoutRecord "gtDigis" "" "RECO"
double "fixedGridRhoAll" "" "RECO"
double "fixedGridRhoFastjetAll" "" "RECO"
double "fixedGridRhoFastjetAllCalo" "" "RECO"
double "fixedGridRhoFastjetCentralCalo" "" "RECO"
double "fixedGridRhoFastjetCentralChargedPileUp" "" "RECO"
double "fixedGridRhoFastjetCentralNeutral" "" "RECO"
edm::SortedCollection<EcalRecHit,edm::StrictWeakOrdering<EcalRecHit> > "reducedEgamma" "reducedEBRecHits" "RECO"
edm::SortedCollection<EcalRecHit,edm::StrictWeakOrdering<EcalRecHit> > "reducedEgamma" "reducedEERecHits" "RECO"
edm::SortedCollection<EcalRecHit,edm::StrictWeakOrdering<EcalRecHit> > "reducedEgamma" "reducedESRecHits" "RECO"
edm::TriggerResults "TriggerResults" "" "RECO"
edm::ValueMap<float> "offlineSlimmedPrimaryVertices" "" "RECO"
pat::PackedTriggerPrescales "patTrigger" "" "RECO"
reco::BeamSpot "offlineBeamSpot" "" "RECO"
vector<l1extra::L1EmParticle> "l1extraParticles" "Isolated" "RECO"
vector<l1extra::L1EmParticle> "l1extraParticles" "NonIsolated" "RECO"
vector<l1extra::L1EtMissParticle> "l1extraParticles" "MET" "RECO"
vector<l1extra::L1EtMissParticle> "l1extraParticles" "MHT" "RECO"
vector<l1extra::L1HFRings> "l1extraParticles" "" "RECO"
vector<l1extra::L1JetParticle> "l1extraParticles" "Central" "RECO"
vector<l1extra::L1JetParticle> "l1extraParticles" "Forward" "RECO"
vector<l1extra::L1JetParticle> "l1extraParticles" "IsoTau" "RECO"
vector<l1extra::L1JetParticle> "l1extraParticles" "Tau" "RECO"
vector<l1extra::L1MuonParticle> "l1extraParticles" "" "RECO"
vector<pat::Electron> "slimmedElectrons" "" "RECO"
vector<pat::Jet> "slimmedJets" "" "RECO"
vector<pat::Jet> "slimmedJetsAK8" "" "RECO"
vector<pat::Jet> "slimmedJetsPuppi" "" "RECO"
vector<pat::Jet> "slimmedJetsAK8PFCHSSoftDropPacked" "SubJets" "RECO"
vector<pat::Jet> "slimmedJetsCMSTopTagCHSPacked" "SubJets" "RECO"
vector<pat::MET> "slimmedMETs" "" "RECO"
vector<pat::MET> "slimmedMETsPuppi" "" "RECO"
vector<pat::Muon> "slimmedMuons" "" "RECO"
vector<pat::PackedCandidate> "lostTracks" "" "RECO"
vector<pat::PackedCandidate> "packedPFCandidates" "" "RECO" vector<patN::Photon> "slimmedPhotons" "" "RECO" vector<pat::Tau> "slimmedTaus" "" "RECO"
vector<pat::TriggerObjectStandAlone> "selectedPatTrigger" "" "RECO"
vector<reco::CATopJetTagInfo> "caTopTagInfosPAT" "" "RECO"
vector<reco::CaloCluster> "reducedEgamma" "reducedEBEEClusters" "RECO"
vector<reco::CaloCluster> "reducedEgamma" "reducedESClusters" "RECO"
vector<reco::Conversion> "reducedEgamma" "reducedConversions" "RECO"
vector<reco::Conversion> "reducedEgamma" "reducedSingleLegConversions" "RECO"
vector<reco::GsfElectronCore> "reducedEgamma" "reducedGedGsfElectronCores" "RECO"
vector<reco::PhotonCore> "reducedEgamma" "reducedGedPhotonCores" "RECO"
vector<reco::SuperCluster> "reducedEgamma" "reducedSuperClusters" "RECO"
vector<reco::Vertex> "offlineSlimmedPrimaryVertices" "" "RECO"
vector<reco::VertexCompositePtrCandidate> "slimmedSecondaryVertices" "" "RECO"

ANALISIS NOTE

1. Go to

http://cms.cern.ch/iCMS/jsp/iCMS.jsp?mode=single&block=publication

2. start a note

Title : Search for new VV resonances in jet + MET final states at √s = 13~TeV
Author : Exotica diboson group
Abstract : We discuss the search for heavy BSM resonances in the V+MET channel, where V stands for a hadronically-decaying W/Z boson.
CMS AN-2015/220

3. Ask to George.Alverson@cern.ch to create an SVN for AN-2015/220"

4. The manual to use the note repository

https://svnweb.cern.ch/cern/wsvn/tdr2/utils/trunk/general/notes_for_authors.pdf

As soon as it is ready, start documenting everything there

CODE IN GIT

https://github.com/dromeroa/EDBR2_Znu

git guide

http://rogerdudler.github.io/git-guide/

Ubication of the code

1. To make the plots in access:

/home/davidromero/LAST_FRAMEWORKS/EDBR2_JOINT_TEST4_JeC/CMSSW_7_4_11_patch1/src/ExoDiBosonResonances/PlottingMacro

2. Location of the data trees in lxplus:

/afs/cern.ch/work/d/dromeroa/private/EDBR_CRAB3_SEP30/CMSSW_7_4_13/src/ExoDiBosonResonances/EDBRTreeMaker/data

3. Location of the trees (2015D 25 ns) in acces:

/home/davidromero/LAST_FRAMEWORKS/EDBR2_JOINT_TEST4_JeC/CMSSW_7_4_11_patch1/src/ExoDiBosonResonances/EDBRTreeMaker/test/trees_2015D_25ns

4. Jets Plots ID

/home/davidromero/LAST_FRAMEWORKS/EDBR2_TEST_JETID/CMSSW_7_4_14/src/ExoDiBosonResonances/EDBRscriptToPlotJetID

5. Location of the plots no jet id in my pc

/home/david/ANALISISTESIS2/PlotsCollections/first_plots_data/nojetid_Oct7

5. Trigger studies

/home/davidromero/Trigger_2015/CMSSW_7_4_7_patch2/src/Trigger_studies/Trigger_test/plugins

6. MetNoHF

/home/davidromero/LAST_JEC/CMSSW_7_4_11_patch1/src/met_jec/jec

7. To use bricalc

/afs/cern.ch/work/d/dromeroa/private/bricalc/CMSSW_7_4_15/src

Do:

export PATH=$HOME/.local/bin:/afs/cern.ch/cms/lumi/brilconda-1.0.3/bin:$PATH

pip install --install-option="--prefix=$HOME/.local" brilws

pip install --upgrade pip

Instructions to run

brilcalc lumi --normtag /afs/cern.ch/user/c/cmsbril/public/normtag_json/OfflineNormtagV1.json -i YOURJSON.txt

8. Git Location in lxplus

/afs/cern.ch/work/d/dromeroa/private/EDBR_CRAB3_SEP29_GIT/CMSSW_7_4_13/src/

From here do:

git init

9. Treemaker for PHYS14

/home/davidromero/EDBR2_torun_here/CMSSW_7_2_4/src/ExoDiBosonResonances

10. The scripts to make the cuts

/home/davidromero/ANALISIS_TESIS_Zhad_Znunu/CMSSW_7_2_2_patch1/src/ExoDiBosonResonances_original/EDBRCommon/python/simulation/script_tree

cuts_script_3.C

11. New crab 20 Oct with ak4jets:

/afs/cern.ch/work/d/dromeroa/private/EDBR2_ak4_20_Oct/CMSSW_7_4_15/src/ExoDiBosonResonance

12. Alpha Method Background estimation

/afs/cern.ch/work/d/dromeroa/private/ALPHAMETHOD_Enero25/CMSSW_8_0_0_pre5/src/ALPHA_METHOD/

then do:

cmsenv

root -b

gSystem->Load("../PDFs/HWWLVJRooPdfs_cxx.so")

gSystem->Load("../PDFs/PdfDiagonalizer_cc.so")

.x higgscross_shapeAnalysis.C("EHP"); (for example)

13. LAST PLOTS (3 Marzo)

on access : /home/davidromero/LAST_FRAMEWORKS/PLOTS_14Ene_2016/CMSSW_7_4_16_patch2/src/ExoDiBosonResonances/PlottingMacro/

vim loopPlot.C

14. Git for 76x

lxplus : /afs/cern.ch/work/d/dromeroa/private/GIT_76X_EDBR2/CMSSW_7_6_3_patch1/src/ExoDiBosonResonances

From here do:

git init

remember that we are in 76X so do:

git push origin 76X

15. Trigger Report 76X (To find the paths name in data and MC)

access : /home/davidromero/TRIGGERS_74X/CMSSW_7_4_0_pre9/src/trigger74_trigger_report/triggertest

CRAB3

To send CRAB3 produce many trees at the same time

1. Create a CRAB config file for each datasample.

2. Submit the jobs

3. To see the status just do :

crab status EDBR_crab_projects/crab_QCD_700to1000_25ns (for exmple)

STATISTICS (LIMITS)

1. Higgs Combination tool

https://twiki.cern.ch/twiki/bin/viewauth/CMS/SWGuideHiggsAnalysisCombinedLimit

2. CMS DAS : An Introduction to the Statistics Tools RooFit, RooStats, and combine

https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchoolLPC2016Statistics

3. CMS DAS : Statistics

https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchoolStatistics2015LPC

4. WW CMS DAS

https://github.com/kkousour/cmsdas2014

5. Higgs Combination and Properties

https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchool2014HiggsCombPropertiesExercise

6. Discovery of a Higgs boson in ZZ to 4 leptons

https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchoolHZZ4lSearchExercise

7. Statistic Committee

https://twiki.cern.ch/twiki/bin/viewauth/CMS/StatisticsCommittee

8. Statistics FAQ

https://twiki.cern.ch/twiki/bin/viewauth/CMS/StatCom-FAQ

9. Statistics Reference

https://twiki.cern.ch/twiki/bin/viewauth/CMS/StatisticsReferences

10. Statistics committee reccomendations

https://twiki.cern.ch/twiki/bin/viewauth/CMS/StatComRec-Selection

11. Reccomendations for search for new physics

https://twiki.cern.ch/twiki/bin/view/CMS/SearchProcedures

12. Reccomendations for sensitivity and reach of the analysis

https://twiki.cern.ch/twiki/bin/view/CMS/SearchProcedures

STORAGE

To see the files in the storage

srmls srm://osg-se.sprace.org.br:8443/pnfs/sprace.org.br/data/cms/store/user/dromeroa/TT_TuneCUETP8M1_13TeV-powheg-pythia8/crab_EDBR_TTbar_powheg_25ns/151126_215013/0000/...

To remove files from the storage, have to use:

./rmdir.sh srm://osg-se.sprace.org.br:8443/srm/managerv2?SFN=/pnfs/sprace.org.br/data/cms/store/user/dromeroa/ZZJets100to200/...

the rmdir script is in the git

TO DO FITS

1. Go to

/afs/cern.ch/work/d/dromeroa/private/ALPHAMETHOD_Enero25/CMSSW_8_0_0_pre5/src/ALPHA_METHOD/TESTNEWCODE

2. Do "cmsenv" to load root6

3. root -b

4. gSystem->Load("../PDFs/HWWLVJRooPdfs_cxx.so")

5. To make the fits for HP normalization:

.x test01abril.C("HP")

6. To make the fits for LP normalization :

.x test11mayoLP.C("LP")

To make fits and plots for signal

testvectsignalHP.C

testvectsignalLP.C

testWprimeHP.C

testWprimeLP.C

PROBLEMS

1. When use an EDFilter in the main cfg.py have to include the boolean option (filter = cms.bool(True))

process.goodMET = cms.EDFilter("PATMETSelector",
                             src = cms.InputTag('slimmedMETs'),
                             cut = cms.string("pt >40"),
                             filter = cms.bool(True)
                             )

2. In case we have problems with TFileService, always check that:

  edm::Service<TFileService> fs;

The service header is included with:

#include "FWCore/ServiceRegistry/interface/Service.h"
#include "CommonTools/UtilAlgos/interface/TFileService.h"

The BuildFile.xml needs the following dependencies:

<use name="PhysicsTools/UtilAlgos"/>
<use name="FWCore/ServiceRegistry"/>

quota in lxplus

fs listquota

To load the library in root

.x myMacro.cxx+

-- davidromero

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who
png	boostjet.png	r1	manage	522.3 K	2015-03-31 - 02:06	UnknownUser
png	feynman.png	r1	manage	638.2 K	2015-03-31 - 01:54	UnknownUser
pdf	met_poster.pdf	r1	manage	15.4 K	2015-04-20 - 01:43	UnknownUser
png	result.png	r1	manage	6.4 K	2015-04-18 - 15:37	UnknownUser
png	table_cuts.png	r1	manage	77.6 K	2015-06-14 - 15:14	UnknownUser
png	table_weight.png	r2 r1	manage	87.5 K	2015-06-14 - 15:43	UnknownUser
png	trri.png	r2 r1	manage	12.8 K	2015-04-20 - 01:36	UnknownUser

Topic revision: r91 - 2016-07-02 - davidromero

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