$J/\psi$ Production in Jets

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.

For better display of this document, please install browser extension MathJax Plugin for Github (Chrome) or try this external page (temp and not latest). The best solution is to open preview in VS Code with Markdown Preview Enhanced.

Author: Yìtāo WÚ

Contents

• 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

Resource

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 |

Background & Motivation

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.

Reference

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]

Physical Object

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.

Related analysis @ ALICE

1. [AN-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. [AN-876] "Inclusive Jpsi production cross-section in pp collisions at 13TeV", Ingrid Mckibben Lofnes (UBergen, NO), 19 March, 2019
3. [AN-850] "Multiplicity dependence of charged jet production in pp collisions at 13 TeV", Yongzhen Hou (CCNU, CN), 21 September, 2018
4. [AN-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

Datasets

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/.

Run Period	MB	Muon	EMCAL	N runs	INT7	EMCEGA	EG1	EG2	DG1	DG2	Nano
18p	64,723,976	38,946,331	8,206,052	66
18o	27,495,955	14,997,718	3,506,262	26
18n*	3,835,344	21,207		2
18m	134,624,309	60,378,259	15,669,592	142
18l	40,366,370	16,800,730	4,118,681	39
18k	8,764,716	36,793,499	906,769	10
18j	96,797	43,361	9,597	1
18i*	55,845,385	553,722		7
18h	4,251,665	1,834,724	434,065	2
18g*	1,508,180			1
18f	44,009,568	18,923,929	4,653,741	46
18e	52,440,125	21,328,980	6,170,463	38
18d	28,079,632	11,969,409	3,317,779	28
18c*	267,232,152	14,312,085		46
18b*	167,363,075	2,107,216		22
17r	25,356,149	16,306,258	4,554,303	27
17o	101,301,254	61,352,135	17,350,119	128
17m*	97,253,493	15,004,475	4,932,360	93
17l	66,971,443	41,517,736	11,726,035	105
17k	89,488,618	38,583,640	11,652,619	93
17j*	41,314,510	163,093		10
17i*	54,162,665	14,422,733	4,209,950	56
17h*	90,126,837	16,633,082	5,610,223	63
17g*	110,598,542	1,620,926		26
17f*	12,112,114	106,809	3,583	5
17c*	1,962,343	601		4
16p	23,175,087	13,077,528	2,570,124	39
16o*	9,397,646	3,195,337	740,813	17
16l	30,687,176	17,153,082	18,057,636	51
16k	105,446,176	47,974,758	17,241,173	123
16j	53,638,211	14,260,378	3,581,261	33
16i	36,566,073	12,227,075	2,272,121	14
16h*	50,404,063	11,696,888		35
16g*	19,103,014	2,450,982		8
Total	1,619,516,002	438,267,301	124,113,415	1,127

Luminosity and event normalization

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: $\sigma_{V0AND}=57.8~mb\pm5\%~(sys.)$, 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).

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

QA

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

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.

Parameter	Description	Method
$N_{events}$	Number and fraction of events in triggers and event cuts.
$\bar{N}_{Good~tracks}$	Average number of good tracks
$\bar{N}_{SPDtracklets}$	Average number of SPD tracklets
$\bar{Z}_{Vtx}, \sigma(Z)_{Vtx}$	Mean and sigma value of vertex Z or (X/Y/XY)
$\bar{N}_{contributors}^{Vtx}$	Average number of vertex contributors
$<Multiplicity>$	Average value of multiplicity, includes VZERO amp.
$<Q_{n}>$	Average value of event plane Q value
$\bar{N}_{clusters}$	Average number of calo clusters
$Cluster~\eta$-$\phi~map$	Identify bad cells/RCU
$BadCell_{ID}$	Dead or Hot/Warm cells
$<VAR_{tracks}>$	Average value or distribution of selected tracks after selection, basic and PID variables

Event

Trigger and event cut overview histograms, like MB, Pileup, good, $Z_{Vtx}<~10cm, \eta<0.9$, 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)

ALICE Offline Triggers

from AliVEvent.h)

EOfflineTriggerTypes	BIT(N)	Description
kMB	0	Minimum bias trigger in PbPb 2010-11
kINT1	0	V0A | V0C | SPD minimum bias trigger
kINT7	1	V0AND minimum bias trigger (MB for pp)
kMUON	2	Single muon trigger in pp2010-11, INT1 suite
kHighMult	3	High-multiplicity SPD trigger
kHighMultSPD	3	High-multiplicity SPD trigger
kEMC1	4	EMCAL trigger in pp2011, INT1 suite
kCINT5	5	V0OR minimum bias trigger
kINT5	5	V0OR minimum bias trigger
kCMUS5	6	Single muon trigger, INT5 suite
kMUSPB	6	Single muon trigger in PbPb 2011
kINT7inMUON	6	INT7 in MUON or MUFAST cluster
kMuonSingleHighPt7	7	Single muon high-pt, INT7 suite
kMUSH7	7	Single muon high-pt, INT7 suite
kMUSHPB	7	Single muon high-pt in PbPb 2011
kMuonLikeLowPt7	8	Like-sign dimuon low-pt, INT7 suite
kMUL7	8	Like-sign dimuon low-pt, INT7 suite
kMuonLikePB	8	Like-sign dimuon low-pt in PbPb 2011
kMuonUnlikeLowPt7	9	Unlike-sign dimuon low-pt, INT7 suite
kMUU7	9	Unlike-sign dimuon low-pt, INT7 suite
kMuonUnlikePB	9	Unlike-sign dimuon low-pt in PbPb 2011
kEMC7	10	EMCAL/DCAL L0 trigger, INT7 suite
kEMC8	10	EMCAL/DCAL L0 trigger, INT8 suite
kMUS7	11	Single muon low-pt, INT7 suite
kMuonSingleLowPt7	11	Single muon low-pt, INT7 suite
kPHI1	12	PHOS L0 trigger in pp2011, INT1 suite
kPHI7	13	PHOS trigger, INT7 suite
kPHI8	13	PHOS trigger, INT8 suite
kPHOSPb	13	PHOS trigger in PbPb 2011
kEMCEJE	14	EMCAL/DCAL L1 jet trigger
kEMCEGA	15	EMCAL/DCAL L1 gamma trigger, including high and low trigger classes
kHighMultV0	16	High-multiplicity V0 trigger
kCentral	16	Central trigger in PbPb 2011
kSemiCentral	17	Semicentral trigger in PbPb 2011
kDG	18	Double gap diffractive
kDG5	18	Double gap diffractive
kZED	19	ZDC electromagnetic dissociation
kSPI7	20	Power interaction trigger
kSPI	20	Power interaction trigger
kINT8	21	0TVX trigger
kMuonSingleLowPt8	22	Single muon low-pt, INT8 suite
kMuonSingleHighPt8	23	Single muon high-pt, INT8 suite
kMuonLikeLowPt8	24	Like-sign dimuon low-pt, INT8 suite
kMuonUnlikeLowPt8	25	Unlike-sign dimuon low-pt, INT8 suite
kMuonUnlikeLowPt0	26	Unlike-sign dimuon low-pt, no additional L0 requirement
kUserDefined	27	Set when custom trigger classes are set in AliPhysicsSelection
kTRD	28	TRD trigger
kMuonCalo	29	Muon-calo triggers
kCaloOnly	29	MB, EMCAL and PHOS triggers in CALO or CALOFAST cluster
		Bits 30 and above are reserved for FLAGS
kFastOnly	30	The fast cluster fired. This bit is set in to addition another trigger bit, e.g. kMB
kAny	0xffffffff	to accept any defined trigger
kAnyINT		kMB | kINT7 | kINT5 | kINT8 | kSPI7. to accept any interaction (aka minimum bias) trigger

ALICE Trigger Classes

from ALICE Trigger Coordination and CTP

Trigger Class = Trigger Condition + Trigger Cluster + Trigger Vetoes
Typical trigger class name:
CEMC7EGA-B-NOPF-CENTNOTRD
[Decriptor]-[BC_mask]-[PF_protection]-[Cluster].

• Descriptor: combination of trigger inputs, L0+L1 (AND, OR, VETO logic is possible).
• BC mask: information about interactions (beam-beam, beam-gas, satellite, etc.).
• Past-Future protection: rejects events with multiple collisions from different bunch crossings
• Cluster: group of detectors to be readout if the trigger conditions is satisfied (ALLNOTRD, CENTNOTRD, ALL, CENT, FAST, MUON)

AOD tree structure

from AliAODEvent.h

AOD (Analysis Object Data) is skimmed data for higher-level analysis, conveted from ESD by AliAnalysisTaskESDfilter.

AODListIndex_t	Name	Description
kAODHeader	header	Event meta information, such as run number, magnetic field, diamond, trigger, etc.
kAODTracks	tracks	Charged tracks array
kAODVertices	vertices	Vertex array
kAODv0	v0s
kAODcascade	cascades
kAODTracklets	tracklets	SPD tracklets
kAODJets	jets
kAODEmcalCells	emcalCells
kAODPhosCells	phosCells
kAODCaloClusters	caloClusters	EMCal, DCal, PHOS clusters array
kAODEMCALTrigger	emcalTrigger
kAODPHOSTrigger	phosTrigger
kAODFmdClusters	fmdClusters
kAODPmdClusters	pmdClusters
kAODHMPIDrings	hmpidRings
kAODDimuons	dimuons
kAODTZERO	AliAODTZERO
kAODVZERO	AliAODVZERO
kAODZDC	AliAODZDC
kAODAD	AliAODAD
kTOFHeader	AliTOFHeader	Event time estimated by TOF
kAODTrdTracks	trdTracks
-	Forward
-	ForwardEP
-	CentralClusters
-	MultSelection
-	UserInfo

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.

Parameter	Description	Std. Method	Typical value	Setup
$Z_{Vtx}$	Z postion (cm) of primary vertex	aod->GetPrimaryVertex()->GetZ()	$0.6\pm4.1$	[-50,50] / 0.1
	X position (cm)	aod->GetPrimaryVertex()->GetX()	$0.074\pm0.006$	[-0.5,0.5] / 0.001
	Y position (cm)	aod->GetPrimaryVertex()->GetY()	$0.367\pm0.004$	[-0.5,0.5] / 0.001
Diamond	Z position and width of the interaction point (IP) (cm)	header->GetDiamondX()/GetSigma2DiamondX()	$0.809\pm\sqrt{24.6}$	-
	X position and width of the interaction point (IP) (cm)	header->GetDiamondX()/GetSigma2DiamondX()	$0.080\pm\sqrt{6.0e-5}$	-
	Y position and width of the interaction point (IP) (cm)	header->GetDiamondX()/GetSigma2DiamondX()	$0.368\pm\sqrt{2.84e-5}$	-
$N_{contributors}^{Vtx}$	Number of primary vertex contributors		$\bar{N}\sim25$	[0,500] / 1
$N_{tracklets}$	Number of SPD tracklets (aod->GetMultiplicity()), by layers	spd->GetNumberOfTracklets()	$\bar{N}\sim28$	[0,200] / 1
$Z_{Vtx}$-$N_{tracklets}$	Correction needed for multiplicity analysis
$N_{ITSclusters}$	Number of ITS clusters, by layers.	spd->GetNumberOfITSClusters(lr)	$\bar{N}\sim540$	[0,20000] / 1
	SPD (pixel), layer 0 and 1		$\bar{N}\sim110$	[0,10000] / 1
	SDD (drift), layer 2 and 3		$\bar{N}\sim70$	[0,3000] / 1
	SSD (strip), layer 4 and 5		$\bar{N}\sim80$	[0,2000] / 1
$N_{SPDfiredChips}$	Number of SPD fired chips (1200 in total), by layers.	spd->GetNumberOfFiredChips(lr)	$\bar{N}\sim115$	[0,1200] / 1
$N_{tracks}$	Number of tracks w/ or w/o track cuts	aod->GetNumberOfTracks()	$\bar{N}_{total}\sim640$	[0,4000] / 1
$A_{VZERO}$	Amplitute of VZERO multiplicity decision as sum of 64 ch. (aod->GetVZEROData())	vzero->GetMultiplicity(ch)	$\bar{A}\sim95$	[0,2000] / 1
	V0A, channel 32 - 63	vzero->GetMultiplicityV0A(ch)	$\bar{A}\sim37$	[0,2000] / 1
	V0C, channel 0 - 31	vzero->GetMultiplicityV0A(ch)	$\bar{A}\sim58$	[0,2000] / 1
Centrality	Estimtor by multiplicity percentiles or impact factor	AddTaskMultSelection.C
Q-vector	Event plane framework	AddTaskFlowQnVectorCorrections.C
$N_{cluster}$	Number of calo clusters	aod->GetNumberOfCaloClusters()	$\bar{N}\sim37$	[0,500] / 1
$E_{cluster}$	Energy distribution of calo clusters (GeV)	calo->E()	$\bar{E}\sim0.5$	[0,300] / 0.1

*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 & TPC-ITS

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.

Track types / status flag

from AliVTrack.h

Enum	BIT(N)	Description
kITSin	0
kITSout	1
kITSrefit	2
kITSpid	3
kTPCin	4
kTPCout	5
kTPCrefit	6
kTPCpid	7
kTRDin	8
kTRDout	9
kTRDrefit	10
kTRDpid	11
kTOFin	12
kTOFout	13
kTOFrefit	14
kTOFpid	15
kHMPIDout	16
kHMPIDpid	17
kEMCALmatch	18
kTRDbackup	19
kTOFmismatch	20
kPHOSmatch	21
kITSupg	22	flag that in the ITSupgrade reco
kSkipFriend	23	flag to skip friend storage
kGlobalMerge	24
kMultInV0	25	assumed to be belong to V0 in multiplicity estimates
kMultSec	26	assumed to be secondary (due to the DCA) in multiplicity estimates
kEmbedded	27	Is a track that has been embedded into the event
kITSpureSA	28
kTRDStop	29
kESDpid	30
kTIME	31
Others	-
kTRDnPlanes	6	Not in BIT
kEMCALNoMatch	-4096
kTOFBCNA	-100

Track bits / pre-defined filter

from AliAODTrack.h, and the details is located in AliAnalysisTaskESDfilter and AliESDtrackCuts. As usual, the track cuts contains TPC cluster number, $\chi^{2}$, DCA, refit requirements and kink rejection:

AODTrkFilterBits_t	BIT(N)	Description	Detail
kTrkTPCOnly	0	Standard TPC only tracks	GetStandardTPCOnlyTrackCuts
kTrkITSsa	1	ITS standalone, require ITS standalone tracks (remove pure SA)	?SetRequireITSStandAlone(kTRUE)?
kTrkITSConstrained	2	Pixel OR necessary for the electrons AND standard track cuts	SetClusterRequirementITS(kSPD, kAny)
kTrkElectronsPID	3	PID for the electrons	SetTPCnSigmaCut(AliPID::kElectron, 3.5)
kTrkGlobalNoDCA	4	standard cuts with very loose DCA (|xy|>2.4, |z|<3.2, 2D=kTRUE)	GetStandardITSTPCTrackCuts2011(kFALSE)
kTrkGlobal	5	standard cuts with tight DCA cut, (|xy|<"0.0105+0.0350/pt^1.1", |z|<2)	GetStandardITSTPCTrackCuts2011(kTRUE)
kTrkGlobalSDD	6	standard cuts with tight DCA but with requiring the first SDD cluster instead of an SPD cluster tracks selected by this cut are exclusive to those selected by the previous cut	GetStandardITSTPCTrackCuts2011(kTRUE), GetClusterRequirementITS(kSPD,kNone), GetClusterRequirementITS(kSDD,kFirst)
kTrkTPCOnlyConstrained	7	Refitted TPC only tracks: TPConly information constrained to SPD vertex in the filter below	GetStandardTPCOnlyTrackCuts, esdFilter->SetTPCOnlyFilterMask(128)
AODTrkBits_t
kIsDCA	14	set if fPosition is the DCA and not the position of the first point
kUsedForVtxFit	15	set if this track was used to fit the vertex it is attached to
kUsedForPrimVtxFit	16	set if this track was used to fit the primary vertex
kIsTPCConstrained	17	set if this track is a SA TPC track constrained to the SPD vertex, needs to be skipped in any track loop to avoid double counting
kIsHybridTPCCG	18	set if this track can be used as a hybrid track i.e. Gbobal tracks with certain slecetion plus the TPC constrained tracks that did not pass the selection
kIsGlobalConstrained	19	set if this track is a global track constrained to the vertex, needs to be skipped in any track loop to avoid double counting
kIsHybridGCG	20	set if this track can be used as a hybrid track i.e. tracks with certain selection plus the global constraint tracks that did not pass the selection

Parameter	Description	Std. Method	Typical Value	Setup
$p/p_{T}$-$\eta$-$\phi$	Basic kinematic variables
	$p_{T}$, transverse momentum (GeV/c)	trk->Pt()	$\bar{p_{T}}\sim0.66$	[0,50] / 0.05
$\eta$-$\phi$ map	$\eta$, pesudo-rapidity; $\phi$, plane angle	trk->Eta()/Phi()	$\bar{\eta}\sim0.5$, $\bar{\phi}\sim0.0$	[-1.0,1.0] / 0.002, [0,$2\pi$] / 0.006
DCA	Distance of the closest approach, also known as impact parameter.
	Large $DCA_{xy}$ is related to the subsequent decays or photon conversions. (cm)	trk->DCA()	$0.00\pm0.19$	[-50,50] / 1000
	$DCA_z$ can be used for rejection of out-of-bunch pileup tracks.	trk->ZAtDCA()	$0.00\pm0.15$	[-50,50] / 1000
$N_{cls.}^{TPC}$	TPC cluster number, 160 (max.)	trk->GetTPCncls()	$\bar{N}\sim100$	[0,161] / 1
$N_{clsF}^{TPC}$	TPC findable cluster number	trk->GetTPCnclsF()	$\bar{N}\sim105$	[0,161] / 1
$N_{clsS}^{TPC}$	TPC shared cluster number	trk->GetTPCnclsS()	$\bar{N}\sim24$	[0,161] / 1
$N_{x.rows}^{TPC}$	Crossed rows in TPC.	trk->GetTPCNCrossedRows()	$\bar{N}\sim109$	[0,161] / 1
$N_{cls.}^{ITS}$	ITS cluster number, sum by layers [0,6]	trk->GetITSNcls()	$\bar{N}\sim5$	[0,7] / 1
$\chi^{2}/N_{clusters}^{TPC}$	TPC cluster tracking/fitting, $\chi^{TPC}_{TPC}=fChi2perNDF\times(N_{cls.}^{TPC}-5)$	trk->GetTPCchi2()/trk->GetTPCncls()	$\bar{\chi^{2}}\sim1.2$	[0,100] / 0.1
$\chi^{2}_{ITS}$	ITS cluster tracking/fitting	trk->GetITSchi2()	$\bar{\chi^{2}}\sim1.7$	[0,100] / 0.1
Kink	Tracks which does not have a continous particle trajectories, but show deviations due to decays or the emission of Bremsstrahlung.
Refit	Fitting method, include ITS and TPC

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.

Calo Cluster / EMCAL+DCAL+PHOS

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.).

Parameter	Description	Std. Method	Typical Value	Setup
$N_{fired}$	Number of cells fired in cluster	calo->GetNCells()	$\bar{N}\sim1.6$	[0,100] / 1
$E_{cell}$	Energy (GeV) vs Cell ID (0~16888)	calo->GetCellAmplitudeFraction(i) * E, calo->GetCellAbsId(i)
$Time_{cell}$	Timing ($\mu s$) vs Cell ID	cells->GetCellTime(id)	$\bar{T}\sim0.7$	[-0.5,1.5] / 0.01
$\eta$-$\phi$	Geomtry map of cluster.	Same as Track
	EMCal: $\eta\in[-0.7,0.7]$, $\phi\in[1.396,3.2637] (107^{\circ})$
	DCal: $\eta\in[-0.7,-0.2] \& [0.2,0,7]$, $\phi\in[4.677,5.707](60^{\circ})$
	PHOS: $\eta\in[-0.12,0.12]$, $\phi\in[3.840,5.585](100^{\circ})$
$D_{cluster}$	Distance of cluster to others withing the same time window - cluster overlap
$E_{cluster}$	Cluster energy	calo->E()	$\bar{E}\sim0.5$	[0,300] / 0.1
M02, M20, dispersion	Shower shape parameters
	M02, 2-nd moment along the main eigen axis.	calo->GetM02()	$\bar{M_{02}}\sim0.46$	[0,20] / 0.1
	M20, 2-nd moment along the second eigen axis.	calo->GetM20()	$\bar{M_{20}}\sim0.017$	[0,10] / 0.01
	Cluster dispersion, for shape analysis.	calo->GetDispersion()	$\bar{Dp}\sim0.2$	[0,10] / 0.01
NLM	Number of local maxia, vs Ncells/E
$\Delta\eta$-$\Delta\phi$	Cluster track matching
$D_{trk-cls}$	Distance of track to cluster, vs $\Delta\eta$-$\Delta\phi$	calo->GetTrackDx/Dz()
$p_{T}$	Pt dependent matching variable, vs $\Delta\eta$-$\Delta\phi$

*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

Definition	EFrac	Range of EFrac or mean
Dead cell	EFrac < mean/3	mean>80
-	EFrac < mean/5	40 < mean < 80
-	EFrac < mean/8	10 < mean < 40
-	EFrac < mean/10	mean < 10
Warm/Hot cell	EFrac > 80	EFrac > 2 *mean
-	EFrac > 20	EFrac > 3 *mean
-	EFrac > 8	EFrac > 4 *mean
-	EFrac > 5	EFrac > 5 *mean

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.

Period	L0 ADC	$E_{th}$/GeV	L1-EG1/DG1	$E_{th}$/GeV	L1-EG2/DG2	$E_{th}$/GeV
16i-k	132	2.5	115	9	51	4
16l?	132	2.5	78	6	51	4
16o-p	132	2.5	115	9	51	4
*17g-r	-	-	-	~11	-	~5

*From peak value in cluster energy distribution.

PID

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-?

Dielectron

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

Parameter	Description	Std. Method	Typical Value	Setup
$m_{e^{+}e^{-}}$	Invariant mass of selected dielectron pair. $(GeV/c^{2})$	VAR::kM	-	[1.5,5.0] / 0.04
$p_{T}$-$\eta$-$\phi$	Basic kinematic variables	VAR::kPt	$\bar{p}_{T}\sim2E_{th}$
$m_{e^{+}e^{-}}$ vs $p_{T}$	Dielectron momentum spectrum
$\ell_{J/\psi}$	Pseudoproper-decay-length (cm)	VAR::kPseudoProperTime	$0.016\pm0.03$	[-0.5,0.5] / 0.0001
$N_{candidates}$	Event level, number of dielectron after cuts	VAR::kPairs	$\bar{N}\sim0.5$	[0,20] / 1
$N_{J/\psi}/<N_{J/psi}>$ vs $N_{trackles}/<N_{tracklets}>$	Event level, ratio of candidates and multiplicity

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

Jets

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 ($p_{T}>0.15~GeV/c$) for $|\eta_{jet}|<0.9-R$. 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]

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

Parameter	Description	Std. Method	Typical Value	Setup
$p_{T}$-$\eta$-$\phi$	Jet basic kinematic variables	JetFinder		[0,200] / 0.2
Area	Jet area	JetFinder	$\bar{A}\sim R\pm0.1$	[0,5R] / 0.01
$p_T$ vs A	Use cut of 0.6*R to reduce fake jet contamination
$\rho$	Jet UE density, Event level	RhoTask	$\bar{\rho}\sim R$	[0,10] / 0.01
$\rho$ vs $p_{T,jet}^{leading}$	Jet UE density vs $p_{T}$ of leading hadron, Event level
$\rho$ vs $N_{tracks}$	Jet UE density vs multiplicity, Event level		$\bar{\rho}/N_{trk.}\sim0.05$
$\delta p_{T}$ vs prob. density	Density fluctuation, $\delta p_{T}=\sum_{RC}^{i}p_{T,i}^{track}-\rho\cdot A_{RC}$

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

Signal Extraction

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.

Event selection

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.

Variable	Cut / Value
Offline Trigger	MB/kINT7
-	EMCal L1/kEMCEGA (EG1/EG2/DG1/DG2)
Vertex type	GetPrimaryVertex()
$N_{contrib.}^{Vtx}$	>1
|$Z_{Vtx}$|	< 10 cm
Physics Selection	True
Pileup	False

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.

Track selection

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.

Variable	Cut / Value - AN646	AN746	AN876
$p_{T}$ (GeV/c)	>1.0	>1.0	>1.0
$\eta$	[-0.9,0.9]	[-0.9,0.9]	[-0.9,0.9]
$DCA_z$ (cm)	[-3,3]	[-3,3]	[-2,2]
$DCA_{xy}$ (cm)	[-1,1]	[-1,1]	[-0.5,0.5]
$N_{cls.}^{TPC}$	85	70	70
$\chi^2/N_{cls.}^{TPC}$	[0.0,4.0]	[0.0,4.0]	[0.0,4.0]
$\chi^2/N_{cls.}^{ITS}$	-	[0.0,30.0]	[0.0,30.0]
TPC $N_{cls.}$ track segments	-		$\geq6$
TPC $N_{cls.}$ bits fired	-	[6,9]	-
TPC $N_{cls.}$ shared	-		$\leq1$
TPC $N_{cls.}$ shared ratio	-	$\notin[0.3,2.0]$	-
TPC $N_{crossed-rows}/N_{find-cls.}$	-	[0.8,2.0]	-
Reject kink	True	True	True
SPD cluster	Any	Any	Any
ITS+TPC refit	True	True	True
$n\sigma_e^{TPC}$	[-2.25,3.0]	[-3.0,3.0]	[-3.0,3.0]
$n\sigma_p^{TPC}$	-	> 3.5	> 3.0
$n\sigma_{\pi}^{TPC}$	-	> 3.5	> 3.0

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.

Rejection Factor

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:

	16i	16k	16l	16o
EG2	$500\pm2$	$494\pm1$	$505\pm2$	$496\pm4$
EG1	$6317\pm96$	$5736\pm42$	$1818\pm13$	$6739\pm193$
DG2	$1352\pm10$	$1223\pm4$	$1132\pm6$	$820\pm8$
DG1	$15916\pm386$	$8392\pm95$	$10287\pm175$	$8608\pm279$

$J/\psi$ reconstruction

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.

Jet finder

Source: QA/AddTaskJetQA.C

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

Parameter	Value	Description
nTrack	"usedefault"	name of track branch
nClusters	""	name of cluster branch
jetAlgo	antikt_algorithm	AliJetContainer::EJetAlgo_t
radius	0.2 / 0.4	Radius of jet cone on $\eta-\phi$ plane
jetType	kChargedJet	AliJetContainer::EJetType_t
minTrPt	0.15	Track pt cut (GeV/c)
minClPt	0.30	Cluster energy cut (GeV)
ghostArea	0.01
reco	pt_scheme	AliJetContainer::ERecoScheme_t
tag	"Jet"	Jet name in container
minJetPt	1.
lockTask	kFALSE
bFillGhosts	kFALSE

• 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)

Nano AODs

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)

Correction

TPC post-calibration

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]

Variable	Cut value
$\chi^2$	< 10
$\cos{\theta_{pointing}}$	[0.0, 0.05]
DCA	[0.0, 0.10] cm
$r_{decay}$	[-3.0, 90.0] cm
$\Psi$	[0.0, 0.2]
$m_{ee}$	$<0.1~GeV/c^{2}$
ITS refit	yes
TPC refit	no

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.

EMCal correction and embedding framework

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.

$J/\psi$ acceptance and efficiency

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

Unfolding of jet $p_{T}$ resolution

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 $J/\psi$.

Systematic uncertainties

Preliminary Result

Publication

Appendix: Tasks and Scripts

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