From IDEA to PAPER.

Repository for references, analysis codes, useful scripts and related discussions.

Software: AliPhysics@ROOT6

Main environment: AliEn World-wide Computing Grid

Local: R710.star.ustc.edu.cn (Dual-E5520 + 16* Dual-E5504)

Development and post-processing are performed on local environment (vAN-20190328_ROOT6). But for alien, all jobs would run on latest version after debug.

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Author: Yìtāo WÚ

• Resource
• Background & Motivation
  • Reference
• Physical Object
  • Related analysis @ ALICE
• Datasets
  • Luminosity and event normalization
• QA
  • Run-wise
  • Event
  • Track & TPC-ITS
  • Calo Cluster / EMCAL+DCAL+PHOS
  • PID
  • Dielectron
  • Jets
• Signal Extraction
  • Event selection
  • PID for electron
  • $J/\psi$ reconstruction
  • Jet finder
  • Nano AODs
• Correction
  • TPC post-calibration
  • EMCal correction and embedding framework
  • $J/\psi$ acceptance and efficiency
  • Unfolding of jet $p_{T}$ resolution
• Systematic uncertainties
• Preliminary Result
• Publication
• Appendix - Macros & Scripts

Software: AliSW@GitHub | GitLab | AliDoc | Offline Database (OADB, OCDB) | ROOT | HEPforge |

Documents: Twiki | Public & Analysis Note | ALICE Offline | ALICE Official |

• DPG: Twiki | Indico | RunLists | EventNorm | Pileup |

• PID: Twiki | PIDinAnalysis | Performance | Bayesian(Indico, arXiv) | Tender |

• EMCal & Jet: Intro. | Doc. | Trigger | Correction | Embedding |

Service: AliEn | AliMonitor | Glance | JIRA | Vidyo | CERN Account | CERN Service | CA | CERN Mail | e-groups | CERNBox | SWAN |

Tutorial:

• ALICE-analysis-tutorial: Website | Indico | Intro. | DataFlow | LEGO train | NanoAOD | EventInfo | Q-vector | TrackInfo | PID | Flow | EventMixing | Unfolding | Errors | MC, Generator | RIVET | AnaControl | PlotStyle | Visual | QAtools | Git | alice-docs | Doxygen | ML |
• ROOT & C++: Primer | Courses | Example | PEP-ROOT6 | ROOT5to6 | Python | ModernC++ | Pointers | Profiling |
• Others: AliSW tutorial | AliBuild | PCG | Debug@ALICE | Starterkit2018 |
• School & Course: INFN-ESC | Huada-QCD | Geant4-events | GSI-Summer | France-China | CERN-Summer | CERN-Fermi | Euro-Inst. | Trigger-DAQ |

Conference & Meeting: ALICE Conference |

• Indico@CERN: ALICE Week | PWG | EMCal | ITS | TPC |
• Other Indico: Indico@IHEP | Indico@USTC-PNP | Indico@IN2P3 |
• Quark Matter: QM2019 | QM2018 | QM2017 | QM2015 | QM2014 | QM2012 |
• Hard Probe: HP2018 | HP2016 | HP2015 |
• Strangeness Quark Matter: SQM2019 | SQM2017 |
• LHC Physics: LHCP2019 | LHCP2018 | LHCP2017 | LHCP2016 | LHCP2015 | LHCP2014 | LHCP2013 |
• Others: VCI

Publication: InspierHEP | CERN Document Server | HEPData | Google Scholar | Microsoft Academic | SCImago Journal Rank |

To understood the $J/\psi$ production mechanism, and explain the lack of observed polarization in high $p_{T}$ range. Previous results from LHCb and CMS are disagree with NRQCD predictions implemented by Pythia8.

1. Baumgart, Matthew, et al. "Probing quarkonium production mechanisms with jet substructure." JHEP 1411 (2014) 003 [INSPIRE]
2. LHCb Collaboration, "Study of $J/\psi$ Production in Jets", Phys.Rev.Lett. 118 (2017) no.19, 192001 [INSPIRE]
3. Kang, Zhong-Bo, et al. "$J/\psi$ production and polarization within a jet." Phys.Rev.Lett. 119 (2017) no.3, 032001. [INSPIRE]
4. Bain, Reggie, et al. "NRQCD Confronts LHCb Data on Quarkonium Production within Jets." Phys.Rev.Lett. 119 (2017) no.3, 032002. [INSPIRE]
5. CMS Collaboration, "Production of prompt and nonprompt $J/\psi$ mesons in jets in pp collisions at $\sqrt{s}=5.02~TeV$", CMS-PAS-HIN-18-012, [CDS]
6. Dai, Lin, et al. "Quarkonium Polarization and the Long Distance Matrix Elements Hierarchies using Jet Substructure" Phys.Rev. D96 (2017) no.3, 036020 [INSPIRE]
7. Dai, Lin, et al. "Heavy quark jet fragmentation." JHEP 1809 (2018) 109 [INSPIRE]
8. Dai, Lin. "Applications of QCD Effective Theories to the Physics of Jets and Quarkonium Production." PhD Diss. University of Pittsburgh, 2018. [PDF]
9. Konrad Tywoniuk, "Theory Overview - J/psi production in jets" ALICE Jpsi2ee PAG workshop, April 2019 [SLIDES]

The jet fragmentation function $z(J/\psi)$, which is the $p_{T}$ fraction carried by $J/\psi$ in jets.

In proton-proton collision at $\sqrt{s}=13~TeV$, $J/\psi$ mesons are reconstructed using their dielectron channel in central barrel. The dielectron events were selected offline with the L0 trigger system, requiring one leg in EMCal/DCal and exceed the L1 energy threshold. Other analysis cuts deployed for electron identification will be presented in following sections. Prompt and non-prompt $J/\psi$ mesons are seperated by pesudo-proper decay length.

Jets are clustered using the anti-$k_{T}$ algorithm from charged tracks, with R=0.2, which provides by fastjet package.

1. [ANA-646] "J/ψ measurements in pp collisions at s√ = 13 TeV using EMCal-triggered events with ALICE at LHC", Cristiane Jahnke (Munich EC, DE), 29 June, 2017
2. [ANA-876] "Inclusive Jpsi production cross-section in pp collisions at 13TeV", Ingrid Mckibben Lofnes (UBergen, NO), 19 March, 2019
3. [ANA-850] "Multiplicity dependence of charged jet production in pp collisions at 13 TeV", Yongzhen Hou (CCNU, CN), 21 September, 2018
4. [ANA-746] "Inclusive J/psi - hadron correlations at mid-rapidty in MB and high multiplicity pp collisions at sqrt(s) = 13 TeV", Lucas Altenkamper (UBergen, NO), 26 April, 2018
5. [ANA-587] "D mesons in jets in pp collisions at sqrt(s)=7 TeV", Salvatore Aiola (Yale, US), 23 January, 2017

ALICE Run2 pp 13TeV (with EMC), 2016-2018

• From DPG twiki : DataTaking | Production | RunLists-calo |
• From MonALISA: RCT (DET Status Flags - SPD + SDD + SSD + TPC + V0 + EMCal)|
• From LEGO train : DQ_pp_AOD | Jet_EMC_pp |

All datasets used is collected in Datasets/DQ_pp_AOD.C, which can be executed and accessed like DATASETS["16l_pass1"]. If not in LEGO train, the number of jobs will be limited under 1500, about 33 runs. Original run lists from DPG is stored under Datasets/DATA/.

Luminosity is used to normalize the measured spectra for cross section estimation.

$$\frac{d^{2}\sigma}{dp_{T}d\eta}=\frac{1}{\mathscr{L}{int}}\frac{N{phys.obj.}}{\Delta p_{T}\Delta \eta}$$

The determinination of the integrated luminosity is based on the measurement of a reference cross section, $\sigma_{V0AND}$, obtained from a van-der-Meer scan. From result in 2015: $\sigma_{V0AND}=57.8mb\pm5%(sys.)$, while $\mathscr{L}=N_{V0AND}^{corr.}/\sigma_{V0AND}$.

The number of events $N_{V0AND}^{corr.}$ includes events with and without a reconstructed vertex within the vertex requirement $|z|<10~cm$. We assume the events without a reconstructed vertex follow the same vertex distribution as reconstructed events, so the event number can be estimated from the fraction $f_{z}$ (with Gaussian function).

$$N_{corr.}=f_{z}\cdot N_{sel.}=\frac{N_{|VtxZ|<10cm}}{N_{Vtx}}\cdot N_{sel.}$$

To check the status and performance of detectors and the agreement between data and MC. Level: Run, Event, Track, Detector, PID, Phys. Objects

• From DPG and PWG/PAG tutorial.
• From related analysis note.

Run-wise QA is generated from histograms of event, track, cluster and other levels run-by-run. Average value of variables and event counts are frequently used indexes. If possible, use THnSparse to store variables for the convenience of post plotting.

Trigger and event cut overview histograms, like MB, Pileup, good, $Z_{Vtx}&lt;~10cm, \eta&lt;0.9$, ratio of different trigger or cut definition.

Statistics of event selection (vs triggers, and in run-wise): Physics Selection (PS), trigger, event cuts, track cuts (no tracks), filtered in nano AOD (with candidates of physics objects) and corresponding reject reasons.

Event ID = $N_{bunch-crossing}+N_{orbit}\times3564+N_{period}\times\rm{0xFFFFFF}\times3564$

For offline trigger,use AliAnalysisTaskMultiDielectron::SetTriggerMask instead of AliAnalysisTaskSE::SelectCollisionCandidates, if event cuts/filter existed. And trigger class is selected by AliAnalysisTaskMultiDielectron::SetFiredTriggerName. (PWGDQ/dielectron ONLY)

Variables on event level:

• Vertex: Z distribution and XY.
• Multiplicity, centrality and event plane.
• N candidates, $J/\psi, jets, \eta, gamma, \pi_0$, ...
• Clusters: Numbers, Energy distribution, etc.

*spd is the instance of SPD tracklets AliAODTracklets from AliAODEvent::GetMultiplicity(). *Typical values and histogram setup are referred to [AN746] and fully AOD tree.

Track selection and tracking parameters. The getter methods depend on the variable manager in PWG analysis framework, while methods listed blow are standard function from basic class in AliRoot/STEER.

For electron, the particle can hit the first or the second layer of the ITS detector (kAny). This cut helps to remove electrons from gamma conversion.

The cluster QA covers general cluster and cell properties. Official QA Repository, reference task - AliAnalysisTaskClusterQA / AddTask_ClusterQA from Gamma Conversion Group. Be careful with the binning of cluster energy histograms. All histogram can be updated after triggger selection and QA or analysis cuts (pt range, track charge, etc.).

*calo is the instance of AliADOCaloCluster from AliAODEvent::GetCaloCluster(AliAODTrack::GetEMCALcluster()) if AliAODTrack::IsEMCal() or AliAODEvent::GetCaloCluster(AliAODTrack::GetPHOScluster()) if AliAODTrack::IsPHOS(). *cells is the instance of AliAODCaloCells from AliAODEvent::GetEMCALCells()/GetPHOSCells().

To identify bad, dead and warm/hot cells, calibrate energy and timing, caculate EFrac and compare with mean value of neighboring cells.

EFrac (Unit: %) = cells energy fraction of full cluster energy, summed over all events --> turned out to be a much better discriminator than just looking how often cells fired

ADC/Energy Trigger Threshold: Twiki, this value can also be estimated with peaks in cluster energy distribution due to the pre-scale. Considering the energy resolution, energy cuts using for analysis MUST be, about 1 GeV, higher than the configured thresholds.

*From peak value in cluster energy distribution.

Particle identification is performed on track level with detector response for final particles, like electron, muon, pion, kaon and proton (deuton, triton, helium-3, helium-4 for special topics). Traditionally, we deploy rectangular cuts on detector response ($n\sigma_{e/\pi/K/p/\mu}$) to seperate particles. Bayesian approch, TMVA and other machine learning methods have also been studied and developed.

Definition of detector response:

$$\xi=n\sigma=\frac{x_{PID}(signal)-\hat{x}_{PID}(m/z)}{\sigma(Detector Resolution)}$$

Detector response/signal:

• ITS & TPC - dE/dx : Energy loss based on Bethe-Bloch curve.
• EMCal - E/p : Ratio of total energy and momentum.
• TOF - $1/\beta$ : Time of flight.
• TRD - Q : Energy loss and production of transition radiation.
• HMPID - $\beta$ : Cenrenkov angle.

PID framework:

• AliPIDResponse: Common way with $n\sigma$ cuts.
• AliPIDCombined: To manage combination of detector response using a Bayesian approach.

Basic usage: Load macro AddTaskPIDResponse.C. Example can be found in AliAnalysisTaskPIDqa.cxx.

  //input hander
  AliAnalysisManager *man=AliAnalysisManager::GetAnalysisManager();
  AliInputEventHandler *inputHandler=dynamic_cast<AliInputEventHandler*>(man->GetInputEventHandler());
  //pid response object
  fPIDResponse=inputHandler->GetPIDResponse();
  // detector response
  double kaonSignal = fPIDResponse->NumberOfSigmasTPC(track, AliPID::kKaon);
  double pionSignal = fPIDResponse->NumberOfSigmasTPC(track, AliPID::kPion);
  double protonSignal = fPIDResponse->NumberOfSigmasTPC(track, AliPID::kProton);

Bayesian approach: AliPIDCombined

// SETTING methods - UserCreateOutputObjects
// specify the detectors you want (OR syntax)
void SetDetectorMask(Int_t mask);
// specify lists of species (default: AliPID::kSPECIES=5)
void SetSelectedSpecies(Int_t selectedSpecies);
// load default TPC priors
void SetDefaultTPCPriors();
// load your own priors as a function of pt
void SetPriorDistribution(AliPID::EParticleType type,TH1F *prior);

// EXEC method - UserExec
// compute probabilities, return mask of detectors used and prob. array in bayesProbabilities
UInt_t ComputeProbabilities(const AliVTrack *track, const AliPIDResponse *response, Double_t* bayesProbabilities,Double_t* priorsOwn=NULL) const;
// Retrieve the probability to have a TPC-TOF mismatch
static Float_t GetTOFmismatchProb();

// OTHER
// Retrieve the priors used for a given track and a given detector mask
void GetPriors(const AliVTrack *track,Double_t* priors,Float_t centrality=-1,Bool_t isPPB=kFALSE) const;
// Return TPC priors histo for a given species
TH1* GetPriorDistribution(AliPID::EParticleType type)  const;

PID tender only for ESD: AddTaskTender.C

// all paramerters are boolean variables
AddTaskTender(kV0, kTPC, kTOF, kTRD, kPID, kVTX, kT0, kEMCal, kPtFix);

QA Task for general purpose: AddTaskPIDqa.C

Signal or $n\sigma$ vs $p/p_{T}/\eta/\phi$

Hybrid signal: TPC-TOF, TPC-EMCal, TRD-?

Standard setup for invariant mass spectrum: $[1.5, 5.0]/0.04(GeV/c^{2})$, pre-defined $J/\psi$ range: $[2.92, 3.16](GeV/c^2)$

*VAR is the instance of AliDielectronVarManager supported by PWG/dielectron

In ALICE offline software, jets are reconstructed with external package - fastjet. The input parameters contain clustering algorithm (anti-kT), jet cone raidus (R=0.2 for pp), recombination scheme ($p_{T}$-scheme) and ghost area units (0.01) with selected tracks ($p_{T}&gt;0.15~GeV/c$) for $|\eta_{jet}|&lt;0.9-R$. The density of jet is defined as $\rho=median{p_{T,i}/A_{i}}$, and $p_{T}$ corrected after subtraction of UE (underlying-event) is: [AN-850:2.6]

$$p_{T,jet}^{corr.}=p_{T,jet}-\rho\cdot Area_{jet}$$

RC is random cone method for the fluctuation of jet UE density.

In PWGDQ, there are two analysis frameworks, dielectron and reducedTree, for general purpose of dielectron and quarkonium physics.

The core class of dielectron is AliDielectron, which consists of event processing methods, analysis cuts/filters (event, track, leg and pair), arrays of tracks and pair candidates, histogram manager, correction framework manager, event mixing handler, track rotator and MC signal. Physics variables are processed and stored in AliDielectronVarManager.

// To load dielectron framework
#include "AliDielectron.h"
#include "AliDielectronVarManager.h"
#include "AliDielectronVarCuts.h"
#include "AliDielectronEventCuts.h"
#include "AliDielectronPairLegCuts.h"
#include "AliDielectronTrackCuts.h"
#include "AliDielectronCF.h"

class AliDielectron;
class AliDielectronVarManager;
class AliDielectronVarCuts;
class AliDielectronEventCuts;
class AliDielectronPairLegCuts;
class AliDielectronTrackCuts;
class AliDielectronCF;

The reducedTree was created as restructuring of dielectron package in Oct 2015. With AliAnalysisTaskReducedTreeMaker, original AOD tree woud be filtered and converted to dst tree.

The events are required to pass the physics selection (Task) and have a reconstructed vertex. For $J/\psi$ analysis in high $p_{T}$ range, we select EMCal triggered electron (single) for dielectron reconstruction and use MB events to determine the rejection factor (RF) of EMCal.

Primary vertex can be reconstructed from tracks, TPC, SPD, and GetPrimaryVertex() returns the best available.

There are two types of pileup that have to be considered:

• in-bunch: the overlap of several events from the same bunch-crossing at the interaction point (IP).
• out-of-bunch: readout time of detectors (TPC:100us, SPD:300ns) are slow than the bunch spacing (25ns).

Tools and methods for pileup rejection: Physics Selection / Past-future protection, Multi-vertexer, AliVEvent::IsPileupFromSPD.

Use track cuts to select good tracks as electron candidates, including kinematic variables, tracking result, TPC parameters and PID limitations.

• Basic strategy: Using dE/dx (from TPC) and E/p (from EMCal) to indentify electrons, and only TPC signal to exclude kaons and protons.
• Hybrid method: To add TOF or/and TRD as complements.

EMCal cuts for ANY leg: $E/p\in[0.8,1.3]$

Out-of-bunch pileup tracks are removed according to the $DCA_z$, hits requirement in any of the two SPD layers and a track refit using the ITS.

For brief, RF is the ratio of event number in MB and selected triggers.

• Using ADC spectrum of the maximum energy in EMCal EG patches (4x4 towers) obtained in kINT7 (MB) events. To calculate event fraction above ADC threshold which would trigger the EMCal.
• Using EMCal cluster energy distribution. (without track cuts?)

Results from C. Jahnke's report:

1. Invariant mass spectrum. Fit with Crystal-Ball (signal) + pol2 (background) function.
2. Combination of EMCal L1 trigger classes (EG1/EG2/DG1/DG2).
3. Cross section vs $p_{T}$, after normalized with EMCal rejection factor.
4. Pseudo-proper decay length $\ell_{J/\psi}=L_{xyz}m_{J/\psi}c/|p_{ee}|$, for the seperation of prompt and non-prompt $J/\psi$. $L_{xyz}$ is the distance between the primary and dielectron vertices. The prompt, non-prompt and background components are parameterized using data and MC events after unfolded with $_{s}\mathcal{P}lot$ technique.

Source: QA/AddTaskJetQA.C

• Refer:
  • LEGO train - JE_EMC_pp
  • runEMCalJetSampleTask.C
• Configuration:
  • JetFinder: AddTaskEmcalJet, Wagon-PPJetFinder_charged_AKT_02/04

• Rho: AddTaskRhoSparse, Wagon-HMRhoTask_02/04
  • ? Parameter in wagon can not match current macro
  • runEMCalJetSampleTask.C, AliAnalysisTaskRho::AddTaskRhoNew
• DeltaPt: AddTaskDeltaPt, Wagon-HMDeltaPt_02/04

Main task:

• SpectraQA: AliAnalysisTaskEmcalJetSpectraQA
  • Trigger: kINT7 (to compare results with AN-850)
  • Wagon: JetSpectra/ppJetSpectra13TeV_QA
• PWGJEQA: AliAnalysisTaskPWGJEQA
  • Trigger: kEMCEGA (to perform QA for $J/\psi$ filter)
  • Wagon: QA/JE_QA_pp_Leticia (Last run: 20190507)

Source: NanoAOD/YatoJpsiFilterTask.h/cxx

• Refer:

  • DielectronFilter: Use AliDielectron to select events with candidates
  • NanoAODFilter/Replicator: Copy and save objects/branch in AliAODEvent.

• Output: AliAOD.Dielectron.root

  • AliAODOutputHandler: header, AliTOFHeader, AliAODVZERO
  • Filter: tracks, vertices, caloClusters, tracklets, emcalTrigger, phosTrigger, emcalCells, phosCells, AliAODZDC, AliAODAD, AliAODTZERO
  • NEW: dielectrons, jets02, jets04

  // Access jet container by formatted name
  TString jetName = AliJetContainer::GenerateJetName(jetType, jetAlgo, recoScheme, radius, partCont, clusCont, tag);
  // TODO: AddJetContainer by user
    // jets02 = "Jet_AKTChargedR020_tracks_pT0150_pt_scheme"
    // jets04 = "Jet_AKTChargedR040_tracks_pT0150_pt_scheme"
  TClonesArray* jets = dynamic_cast<TClonesArray*>(aodEv->FindListObject(jetName));

• Selection
  • Trigger: AliVEvent::kEMCEGA
  • Cuts definition: EMCal_loose
  • Pari mass: $[1.0, 5.0]~(GeV/c^2)$ (for background estimation)
  • Jet: no cuts (TODO: set by user)

1. Replace daughters with $J/\psi$ pseudo track.
2. Reclustering with new track array.
3. Find $J/\psi$ track in jets and calculate $p_{T}$ ratio (z).

For periods which do not have dedicated TPC splines and show a greater mismatch between data and MC. The post calibration is performed using a clear electron sample from tagged electrons from photon conversions. QA plots shows $n\sigma_{e}^{TPC}$ as a function of p, $\eta$. The conversion electron selection is made using cuts similar with $J/\psi$ electrons but as a lot of conversions happen in the ITS material, the requirement for SPD refit is removed. [AN876:4.4]

The calibration centers the electron band at 0, with a width of 1. The calibration maps (2D bin-by-bin) are used to re-calculate the TPC $n\sigma_{e}$:

$$n_{\sigma}^{calib}(p_{in},\eta)=\frac{n_{\sigma}(p_{in},\eta)-n_{0}(p_{in},\eta)}{w(p_{in},\eta)}$$

where $n_{0}$ and $w$ are the mean and witdth of the uncalibrated electron band.

Reference: alidoc | PCG-tutorial | ALICE-analysis-tutorial (Correction, Embedding) |

• Bad channel map, energy calibration, time calibration, cell-level crosstalk.
• Cluster exotics, enery non-linearity, track matching, hadronic correction and PHOS tender.

• Detector geometrical coverage and acceptance.
• Trigger, tracking reconstruction, electron identification and selection efficiency of $e^{+}e^{-}$ pairs.

The procedure is carried out in two dimensions, z and jet $p_{T}$ with D'Agostini's iterative algorithm, which is avalaible in RooUnfold package. In this step, we should produce a four-dimensional detector-response (MC & data) matrix for prompt and non-prompt $J/\psi$.

With different cut definitions.

Matching efficiency

PID cuts for electron, electron from conversion, electron efficiency, hadron rejection ...

Fitting method and errors.

Branching ratio, luminosity ...

Analysis Task|Description|Configuration|Full Path| -|-|-|-|-| -|runAnalysis.C| Physics Selection|Selects collision candidates from data runs, applying selection cuts on triggers and background rejection based on the content of the ESD|-|OADB| MultSelection|New centrality estimator||OADB| CDB Connect|Task just allowing connection to CDB (no lock)|-|PWGPP| EMCal Correction|Perform correction on bad channel, energy, timing and clusterizer.|YAML-PWGHF|PWG/EMCAL| PID response|Basic PID task|-|AliRoot PID QA|QA for PID result on full AOD|-|AliRoot -|QA/AddTaskJetQA.C| Emcal Jet|Jet finder, create jet container|kCharged, R=0.2/0.4, anti_kt|AddTaskEmcalJet| Rho|Estimate jet background by kt_algorithm||AddTaskRhoNew| DeltaPt|Jet Pt correction|RCperEv=100, MinRC2LJ=R|PWGJE/EMCALJetTasks| PWGJEQA|Performs basic QA relevant to the jet group, by run-wise, event, track, cluster and cells. AliDoc|EMCEGA|PWGJE/EMCALJetTasks| Spectra QA|Perform QA on jet spectra by multiplicity.|INT7, Jets_EMC_pp -|QA/AddTaskJpsiQA.C| MultiDielectron|Basic Analysis task for Dielectron by single event analysis with multiple cuts definition (AliDielectron) |ConfigJpsi_cj_pp.C (INT7/EG1/EG2/DG1/DG2, EMCal_strict+RAW)|QA/AddTask_cjahnke_JPsi| -|NanoAOD/AddTaskJPSIFilter.C| Jpsi Filter|Filter AOD with dielectron candidates on event level; Create branch for dielectron pairs and jets|kEMCEGA + EMCal_loose|NanoAOD/YatoJpsiFilterTask|