Helper tasks

Table of contents:


The timestamp task is needed to fill the table with timestamps. Timestamp contains the time of a bunch crossing since the start of the run. This time is often needed to retrieve objects in the CCDB (see Tutorial CCDB).

Since the Timestamps table has an entry per bunch crossing it can be joined with table BC. The join is defined by o2::aod::BCsWithTimestamps (see list of defined joins and iterators).

Event selection

Table of contents:


The main purpose of the event selection framework in O2 is to provide tools to select triggered events and reject pileup, beam-gas and poor quality collisions. Event selection in O2 is based on the concept of derived tables created in dedicated tasks from available AOD contents. o2-analysis-event-selection executable produces two in-memory tables described in EventSelection.h:

  • EvSels table joinable with Collisions table. To be used in analyses based on loops over Collisions, i.e. majority of ALICE analyses.
  • BcSels table joinable with BCs table. To be used in analyses based on loops over BCs table such as muon arm UPCs, luminosity monitoring etc.

The structure of BcSels and EvSels tables is kept the same for Run 2 and Run 3. However, there are conceptual differences between Run 2 and Run 3 workflows:

  • Run 3 setup is significantly different from Run 2 setup, e. g. V0C detector is not available in Run 3 etc. Therefore Run-2 minimum bias trigger based on V0A & V0C is no longer available and is replaced with FT0A & FT0C requirement in Run 3. Many other selection criteria used in Run 2 are not applicable in Run 3 (e. g. tracklet-vs-cluser correlation cut).
  • While in Run 2 there is a unique matching between Collisions and BCs, it is not the case in Run 3. Time resolution for collisions (=primary vertices) is not precise enough to identify corresponding bunch crossing (=25 ns) without ambiguities. The collision time resolution depends on the number of contributed ITS-TPC tracks, availability of TOF-matched tracks and other factors. One of the main goals of event selection task in Run 3 is to find the original bunch crossing for each collision and check for relevant info in forward detectors (FIT, ZDC). Unambiguous association of collisions to bunch crossings might become very nontrivial in high rate environment.

BcSels and EvSels tables contain the following information:

  • fired trigger aliases, see Trigger aliases section
  • offline event selection criteria such as beam-beam and beam-gas decisions from forward detectors (FV0, FT0, FDD, ZDC) and various in-bunch and out-of-bunch pileup checks, see Event selection criteria section

In addition EvSels table contains additional info:

  • event selection decisions (in EvSels table only), i.e. logical combinations of various offline event selection criteria, see Event selection decisions section. For example, sel7 is based on beam-beam decisions in V0A and V0C with additional background, pileup and quality checks
  • indices to found bunch crossings and corresponding FT0 and FV0 entries (in EvSels table only), see Found bunch crossings section.

BcSels and EvSels tables are produced by BcSelectionTask and EventSelectionTask, respectively, see Common/TableProducer/eventSelection.cxx. There are separate process functions for Run 2 and Run 3 in both tasks. One has to use –process-run 2 or –process-run 3 configurables in json files to switch between these process functions, see Configurables section for more details.

Basic usage in user tasks

In general, one has to follow the following steps:

  • add EventSelection.h header:

      #include "Common/DataModel/EventSelection.h""
  • join Collisions and EvSels tables and use corresponding iterator as an argument of the process function:

      void process(soa::Join<aod::Collisions, aod::EvSels>::iterator const& col, ...)
  • check if your trigger alias is fired if you run over Run1 or Run2 data (or future triggered Run3 data):

      if (!col.alias_bit(kINT7)) {

    Bypass this check if you analyse MC or continuous Run3 data.

  • apply further offline selection criteria:
    • for Run 2 data and MC:

        if (!col.sel7()) {
    • for Run 3 data and MC:

        if (!col.sel8()) {

      Note that sel8 selection based on FT0A & FT0C requirement is not mandatory in Run 3 pilot beam data. It might be safer to work with unbiased sample.

  • run your tasks in stack with timestamp and event-selection tasks:

      o2-analysis-timestamp --aod-file AO2D.root -b | o2-analysis-event-selection -b | o2-analysis-user-task -b

    This workflow works for Run 2 data. Special settings are required for MC and Run 3 data, see Configurables section.

    o2-analysis-timestamp task Common/TableProducer/timestamp.cxx is required to create per-event timestamps necessary to access relevant CCDB objects in the event selection task.

    o2-analysis-zdc-converter and o2-analysis-collision-converter might be also necessary for old datasets to account for changes in the data model.

Trigger aliases

Direct selection on trigger class names in O2 is rather complicated. In contrast to Run 2 AODs, there is no way to get the list of fired classes in a string-like format. Instead one has to check bits corresponding to trigger class ids either in triggerMask column in BCs table or triggerMaskNext50 in Run2BCInfos table (for Run 2 if the trigger class id is larger than 50). This approach is complicated since trigger class ids for the same class vary from run to run.

To simplify trigger checks, we use trigger alias approach. Fired trigger classes are mapped to trigger alias bits in the alias array of BcSels and EvSels tables. Aliases have at least two advantages:

  • several classes based on similar logic can be grouped together into one alias (see kINT7 for example)
  • alias bits do not change from run to run in contrast to trigger class ids

The list of available trigger alises can be found in Common/CCDB/TriggerAliases.h. The mapping between trigger classes (and their indices) and trigger aliases is stored in CCDB run-by-run in dedicated TriggerAliases objects. Current mapping can be checked in upload_trigger_aliases.C macro for Run2:

  mAliases[kINT7inMUON] = "CINT7-B-NOPF-MUFAST";
  mAliases[kCUP8] = "CCUP8-B-NOPF-CENTNOTRD";
  mAliases[kCUP9] = "CCUP9-B-NOPF-CENTNOTRD";
  mAliases[kMUP10] = "CMUP10-B-NOPF-MUFAST";
  mAliases[kMUP11] = "CMUP11-B-NOPF-MUFAST";

and in upload_trigger_aliases_run3.C for Run 3:

  mAliases[kTVXinTRD] = "CMTVX-B-NOPF-TRD,minbias_TVX";
  mAliases[kTVXinEMC] = "C0TVX-B-NOPF-EMC,minbias_TVX_L0";
  mAliases[kTVXinPHOS] = "C0TVX-B-NOPF-PHSCPV,minbias_TVX_L0";

This list of trigger aliases and classes is not complete but it should be enough for tests in various PWGs. New trigger classes and aliases can be added upon request (contact Evgeny Kryshen).

Event selection criteria

Full list of event selection criteria can be found in Common/CCDB/EventSelectionParams.h

enum EventSelectionFlags {
  kIsBBV0A = 0,        // cell-averaged time in V0A in beam-beam window
  kIsBBV0C,            // cell-averaged time in V0C in beam-beam window (for Run 2 only)
  kIsBBFDA,            // cell-averaged time in FDA (or AD in Run2) in beam-beam window
  kIsBBFDC,            // cell-averaged time in FDC (or AD in Run2) in beam-beam window
  kIsBBT0A,            // cell-averaged time in T0A in beam-beam window
  kIsBBT0C,            // cell-averaged time in T0C in beam-beam window
  kNoBGV0A,            // cell-averaged time in V0A in beam-gas window
  kNoBGV0C,            // cell-averaged time in V0C in beam-gas window (for Run 2 only)
  kNoBGFDA,            // cell-averaged time in FDA (AD in Run2) in beam-gas window
  kNoBGFDC,            // cell-averaged time in FDC (AD in Run2) in beam-gas window
  kNoBGT0A,            // cell-averaged time in T0A in beam-gas window
  kNoBGT0C,            // cell-averaged time in T0C in beam-gas window
  kIsBBZNA,            // time in common ZNA channel in beam-beam window
  kIsBBZNC,            // time in common ZNC channel in beam-beam window
  kIsBBZAC,            // time in ZNA and ZNC in beam-beam window - circular cut in ZNA-ZNC plane
  kNoBGZNA,            // time in common ZNA channel is outside of beam-gas window
  kNoBGZNC,            // time in common ZNC channel is outside of beam-gas window
  kNoV0MOnVsOfPileup,  // no out-of-bunch pileup according to online-vs-offline VOM correlation
  kNoSPDOnVsOfPileup,  // no out-of-bunch pileup according to online-vs-offline SPD correlation
  kNoV0Casymmetry,     // no beam-gas according to correlation of V0C multiplicities in V0C3 and V0C012
  kIsGoodTimeRange,    // good time range
  kNoIncompleteDAQ,    // complete event according to DAQ flags
  kNoTPCLaserWarmUp,   // no TPC laser warm-up event (used in Run 1)
  kNoTPCHVdip,         // no TPC HV dip
  kNoPileupFromSPD,    // no pileup according to SPD vertexer
  kNoV0PFPileup,       // no out-of-bunch pileup according to V0 past-future info
  kNoSPDClsVsTklBG,    // no beam-gas according to cluster-vs-tracklet correlation
  kNoV0C012vsTklBG,    // no beam-gas according to V0C012-vs-tracklet correlation
  kNoInconsistentVtx,  // no inconsistency in SPD and Track vertices
  kNoPileupInMultBins, // no pileup according to multiplicity-differential pileup checks
  kNoPileupMV,         // no pileup according to multi-vertexer
  kNoPileupTPC,        // no pileup in TPC
  kIsTriggerTVX,       // FT0 vertex (acceptable FT0C-FT0A time difference) at trigger level
  kIsINT1,             // SPDGFO >= 1 || V0A || V0C
  kNsel                // counter

Technically there are three types of criteria:

  • based on flags from bc-joinable aod::Run2BCInfos table (kIsGoodTimeRange, kNoIncompleteDAQ, kNoTPCLaserWarmUp, kNoTPCHVdip, kNoPileupFromSPD, kNoV0PFPileup)
  • based on information from FIT and ZDC detectors (kIsBB…, kIsBG…) and/or additional information stored in aod::Run2BCInfos table (kNoV0MOnVsOfPileup,kNoSPDOnVsOfPileup)
  • based on additional information from aod::Collisions table

Decisions on inidividual selection criteria are stored in selection array BcSels and EvSels tables. E.g. one can check if a given collision passed kIsBBV0A selection:

  bool isBBV0Apassed = col.selection_bit(evsel::kIsBBV0A);

Event selection decisions

Offline event selection decisions (e.g. sel7) are constructed based on a subsample of individual checks stored in selection array. The default list of checks may depend on colliding system, specific run conditions and specific analysis requirements. Default set of checks can be found in Common/CCDB/EventSelectionParams.cxx. The default selectionBarrel masks for pp, pA, Ap and AA are summarized below:

  • default sel7 selection in pp is based on the requirement of beam-beam timing in V0A and V0C and a number of pileup, beam-gas and othe quality checks
    selectionBarrel[kIsBBV0A] = 1;
    selectionBarrel[kIsBBV0C] = 1;
    selectionBarrel[kNoV0MOnVsOfPileup] = 1;
    selectionBarrel[kNoSPDOnVsOfPileup] = 1;
    selectionBarrel[kNoV0Casymmetry] = 1;
    selectionBarrel[kIsGoodTimeRange] = 1;
    selectionBarrel[kNoIncompleteDAQ] = 1;
    selectionBarrel[kNoTPCHVdip] = 1;
    selectionBarrel[kNoPileupFromSPD] = 1;
    selectionBarrel[kNoV0PFPileup] = 1;
    selectionBarrel[kNoSPDClsVsTklBG] = 1;
    selectionBarrel[kNoV0C012vsTklBG] = 1;
  • checks for pA system are similar to pp but in addition they include no beam-gas in ZNA:
    selectionBarrel[kNoBGZNA] = 1;
  • checks for Ap system are similar to pp but in addition they include no beam-gas in ZNC:
    selectionBarrel[kNoBGZNC] = 1;
  • default checks for AA are much simpler compared to pp since hadronic pileup is at per-mile level and can be ignored in the first approximation. Default checks include beam-beam timing in V0A, V0C, ZNA and ZNC detectors and a couple of quality checks.
    selectionBarrel[kIsBBV0A] = 1;
    selectionBarrel[kIsBBV0C] = 1;
    selectionBarrel[kIsBBZAC] = 1;
    selectionBarrel[kIsGoodTimeRange] = 1;
    selectionBarrel[kNoTPCHVdip] = 1;

In addition we define selectionMuonWithPileupCuts and selectionMuonWithoutPileupCuts with reduced set of checks, see Common/CCDB/EventSelectionParams.cxx for more details.

Besides, there are special settings for some run ranges, e.g. we remove checks on out-of-bunch pileup for runs with isolated bunches:

  selectionBarrel[kNoV0MOnVsOfPileup] = 0;
  selectionBarrel[kNoSPDOnVsOfPileup] = 0;
  selectionBarrel[kNoV0Casymmetry] = 0;
  selectionBarrel[kNoV0PFPileup] = 0;

These special settings are stored in CCDB. One can find relevant details in upload_event_selection_params.C macro.

Finally, it is worth mentioning that out-of-bunch pileup cuts as well as ZDC timing checks are disabled in MC eventSelection.cxx#L265:

    if (isMC) {
      applySelection[kIsBBZAC] = 0;
      applySelection[kNoV0MOnVsOfPileup] = 0;
      applySelection[kNoSPDOnVsOfPileup] = 0;
      applySelection[kNoV0Casymmetry] = 0;
      applySelection[kNoV0PFPileup] = 0;

Selection mask applySelection is obtained from CCDB in eventSelection.cxx:

  EventSelectionParams* par = ccdb->getForTimeStamp<EventSelectionParams>("EventSelection/EventSelectionParams", bc.timestamp());

Then sel7 decision is constructed from active checks: Common/TableProducer/eventSelection.cxx

    bool sel7 = 1;
    for (int i = 0; i < kNsel; i++) {
      sel7 &= applySelection[i] ? selection[i] : 1;

Found bunch crossings

One of the main goals of the event selection task in Run 3 is to find the original bunch crossing for each collision. The basic approach is to start from estimated collision bc and search for closest BC containing FT0 entries in a +/-4 sigma window where sigma corresponds to the estimated collision time resolution from col.collisionTimeRes(). Implementation details can be found in eventSelection.cxx#L348.

Users can access found bunch crossings and FT0 entries using foundBC or foundFT0 indices stored in the EvSels table:

if (collision.has_foundBC()) {
  auto bc = collision.foundBC();
  uint64_t globalBC = bc.globalBC();


if (collision.has_foundFT0()) {
  auto ft0 = collision.foundFT0();
  int triggersignals = ft0.triggerMask();

If bunch crossing with FT0 entries is not found, foundBC and foundFT0 indices are set to -1 therefore one has to check collision.has_foundBC() or collision.has_foundFT0() before accessing corresponding info.


Event selection task supports several configurables:

  • muonSelection allows to activate reduced set of checks for muon analyses

    Configurable<int> muonSelection{"muonSelection", 0, "0 - barrel, 1 - muon selection with pileup cuts, 2 - muon selection without pileup cuts"};
  • isMC allows to suppress several checks for Run 2 MC, see Event selection decisions:

    Configurable<bool> isMC{"isMC", 0, "0 - data, 1 - MC"};

    Note that one has to enable isRun2MC flag in the timestamp task in this case:

    o2-analysis-timestamp --aod-file AO2D.root -b --isRun2MC 1 | o2-analysis-event-selection -b --isMC 1 | o2-analysis-user-task -b

In the case of Run 3 processing, one has to set processRun2=false and processRun3=true flags in bc-selection-task and event-selection-task. These configurables cannot be set in the command line. Instead one has to use json files. Typical content of the json file for Run 3 processing:

    "bc-selection-task": {
        "processRun2": "false",
        "processRun3": "true"
    "event-selection-task": {
        "processRun2": "false",
        "processRun3": "true"

One can set other configurables in the json file. This json file has to be provided using --configuration option:

  o2-analysis-timestamp -b | o2-analysis-event-selection --configuration json://config.json -b


  • One has to apply offline selections in O2 explicitly in contrast to AliPhysics where these selections were applied together with trigger alias selection.
  • EvSel table might be also useful in user tasks relying on beam-beam and beam-gas decisions in forward detectors, e.g. in UPC tasks.

Multiplicity and centrality selection in O2

Multiplicity selection concept

The multiplicity and centrality selection in O2 is based on the concept of derived tables created in dedicated tasks from available AOD contents:

  • o2-analysis-multiplicity-table task Common/TableProducer/multiplicityTable.cxx stores relevant multiplicity values (V0A, V0C, ZNA, ZNC) and their dynamic sums (V0M) in Mults table joinable with Collisions table.
  • o2-analysis-multiplicity-qa task Common/Tasks/multiplicityQa.cxx creates multiplicity distributions in minimum bias triggers necessary for centrality calibration.
  • o2-analysis-centrality-table task Common/TableProducer/centralityTable.cxx takes multiplicity values from the Mults table and stores centrality values in Cents table joinable with Collisions table. Relevant cumulative multiplicity distributions are stored in CCDB. The centrality calibration relies on 90% anchor points in Pb-Pb.
  • o2-analysis-centrality-qa task Common/Tasks/centralityQa.cxx creates centrality distributions for minimum bias triggers and can be used for control and QA purposes.

Note that o2-analysis-multiplicity-qa and o2-analysis-centrality-qa tasks rely on the minimum bias trigger selection therefore one has to run event selection in stack with these tasks.

Multiplicity selection usage in user tasks

One can check o2-analysis-centrality-qa task for example usage: Common/Tasks/centralityQa.cxx. Usually, analysers perform event selection before the centrality selection therefore one has to consider the following steps:

  • add EventSelection.h and Centrality.h headers:

      #include "Common/DataModel/EventSelection.h"
      #include "Common/DataModel/Centrality.h"
  • join Collisions, EvSels and Cents tables and use corresponding iterator as an argument of the process function:

      void process(soa::Join<aod::Collisions, aod::EvSels, aod::Cents>::iterator const& col, ...)
  • check if your trigger alias is fired if you run over Run1 or Run2 data (or future triggered Run3 data):

      if (!col.alias_bit(kINT7))

    Bypass this check if you analyse MC or future continuous Run3 data.

  • apply further offline selection criteria:

      if (!col.sel7())
  • apply centrality selection, for example:

      // analyse 0-20% central events
      if (col.centV0M()>20)
  • run your tasks in stack with timestamp, event-selection, multiplicity and centrality tasks:

      o2-analysis-timestamp --aod-file AO2D.root -b | o2-analysis-event-selection -b | o2-analysis-mulitplicity-table -b | o2-analysis-centrality-table -b | o2-analysis-user-task -b

    o2-analysis-timestamp task is required to create per-event timestamps necessary to access relevant CCDB objects in the event selection and/or centrality tasks.

    o2-analysis-zdc-converter and o2-analysis-collision-converter might be also necessary for old datasets to account for changes in the data model.

Particle identification (PID)

Table of contents:

Here are described the working principles of Particle Identification (PID) in O2 and how to get PID information (expected values, nSigma separation et cetera) in your analysis tasks if you plan to identify particles.


PID is handled in analysis by filling helper tables that can be joined to tracks (propagated or not). The parameterization of the expected detector response (e.g. signal, resolution, separation) is used in the PID tasks to fill the PID tables. These parameterizations are detector specific and handled by the detector experts; usually, they are shipped to the PID helper tasks from the CCDB to match the data-taking conditions. The interface between the detector and the Analysis Framework (i.e. your tracks) is fully enclosed in PIDResponse.h. Here are the defined tables for the PID information for all the detectors.

The filling of the PID tables is delegated to dedicated tasks in Common/TableProducer/PID/ Examples of these tasks can be found in pidTOF.cxx and pidTPC.cxx for TOF and TPC tables, respectively.

Usage in user tasks

Tables for PID values in O2 are defined in PIDResponse.h. You can include it in your task with:

#include "Common/DataModel/PIDResponse.h"

In the process functions, you can join the table to add the PID (per particle mass hypothesis) information to the track. In this case, we are using the mass hypothesis of the electron, but tables for nine (9) stable particle species are produced (El, Mu, Pi, Ka, Pr, De, Tr, He, Al).

  • For the TOF PID as:

      void process(soa::Join<aod::Tracks, aod::pidTOFEl>::iterator const& track) {
  • For the TPC PID as:

      void process(soa::Join<aod::Tracks, aod::pidTPCEl>::iterator const& track) {
  • For both TOF and TPC PID information as:

      void process(soa::Join<aod::Tracks, aod::pidTOFEl, aod::pidTPCEl>::iterator const& track) {
        const float combNSigmaEl = std::sqrt( track.tofNSigmaEl() * track.tofNSigmaEl() + track.tpcNSigmaEl() * track.tpcNSigmaEl());

Task for TOF and TPC PID

In short: O2 tasks dedicated to the filling of the PID tables are called with

  • Filling TOF PID Table


    This requires a helper class for the building of the event time information


    These tasks can be configured according to the needs specified by the detector experts. If you are in doubt, you can contact them or take the configuration of the Hyperloop as a reference.

  • Filling the TPC PID Table


    These tasks can be configured according to the needs specified by the detector experts. If you are in doubt, you can contact them or take the configuration of the Hyperloop as a reference.

Example of tasks that use the PID tables (and how to run them)

  • TOF PID task pidTOF.cxx You can run the TOF spectra task with:

      o2-analysis-pid-tof-qa --aod-file AO2D.root -b | o2-analysis-pid-tof -b | o2-analysis-pid-tof-base -b
  • TPC PID task pidTPC.cxx You can run the TPC spectra task with:

      o2-analysis-pid-tpc-qa --aod-file AO2D.root -b | o2-analysis-pid-tpc -b | o2-analysis-pid-tpc-base -b

Enabling QA histograms

  • QA histograms should come with the PID tasks; they can be enabled by including the QA tasks in your workflow when running locally or with the corresponding QA tasks as in:

    For the TOF QA plots

      ... | o2-analysis-pid-tof-qa | ...

    For the TPC QA plots

      ... | o2-analysis-pid-tpc-qa | ...

    Where by ... we mean the other tasks in your workflow.

Track Selection

The track selection in the O2 analysis framework is provided in form of a stand-alone workflow that you can run in front of your analysis:

o2-analysis-trackselection | o2-analysis-myTask

Based on a set of track quality criteria, the track selection workflow produces a flag. Provided that the user uses the standard supported o2-analysis-trackselection track tables can be filtered, e.g. by requiring global tracks (with cuts on pT and eta), in the following manner:

#include "Analysis/TrackSelectionTables.h"
// NB: this only works provided that you use the supported task o2-analysis-trackselection to perform the track selection
Filter filter1 = requireTrackCutInFilter(TrackSelectionFlags::kTPCNCls); // General filter, defined using flags
Filter filter2 = requireQualityTracksInFilter();                         // Predefined filter, for kQualityTracks tracks
Filter filter3 = requirePrimaryTracksInFilter();                         // Predefined filter, for kPrimaryTracks tracks
Filter filter4 = requireInAcceptanceTracksInFilter();                    // Predefined filter, for kInAcceptanceTracks tracks
Filter filter5 = requireGlobalTrackInFilter();                           // Predefined filter, for kGlobalTrack tracks
Filter filter6 = requireGlobalTrackWoPtEtaInFilter();                    // Predefined filter, for kGlobalTrackWoPtEta tracks
Filter filter7 = requireGlobalTrackWoDCAInFilter();                      // Predefined filter, for kGlobalTrackWoDCA tracks
Filter filter8 = requireTrackWithinBeamPipe();                           // Tracks starting from within the beam pipe

void process(soa::Filtered<soa::Join<aod::Tracks, aod::TrackSelection>>::iterator const& track)

In general it is advised to use the filter decisions only in the filter expressions, so the framework can optimize the processing. However, if really needed you can also access the filtering decisions in the following way:

#include "Analysis/TrackSelectionTables.h"

void process(soa::Join<aod::Tracks, aod::TrackSelection>::iterator const& track)
   // Check single cuts
   track.passedTrackType();             // Passed the track cut: kTrackType
   track.passedPtRange();               // Passed the track cut: kPtRange
   track.passedEtaRange();              // Passed the track cut: kEtaRange
   track.passedTPCNCls();               // Passed the track cut: kTPCNCls
   track.passedTPCCrossedRows();        // Passed the track cut: kTPCCrossedRows
   track.passedTPCCrossedRowsOverNCls();// Passed the track cut: kTPCCrossedRowsOverNCls
   track.passedTPCChi2NDF();            // Passed the track cut: kTPCChi2NDF
   track.passedTPCRefit();              // Passed the track cut: kTPCRefit
   track.passedITSNCls();               // Passed the track cut: kITSNCls
   track.passedITSChi2NDF();            // Passed the track cut: kITSChi2NDF
   track.passedITSRefit();              // Passed the track cut: kITSRefit
   track.passedITSHits();               // Passed the track cut: kITSHits
   track.passedGoldenChi2();            // Passed the track cut: kGoldenChi2
   track.passedDCAxy();                 // Passed the track cut: kDCAxy
   track.passedDCAz();                  // Passed the track cut: kDCAz
   // Check combiation of cuts (filterbit like)
   track.isQualityTrack();       // Passed the combined track cut: kQualityTracks
   track.isPrimaryTrack();       // Passed the combined track cut: kPrimaryTracks
   track.isInAcceptanceTrack();  // Passed the combined track cut: kInAcceptanceTracks
   track.isGlobalTrack();        // Passed the combined track cut: kGlobalTrack
   track.isGlobalTrackWoPtEta(); // Passed the combined track cut: kGlobalTrackWoPtEta
   track.isGlobalTrackWoDCA();   // Passed the combined track cut: kGlobalTrackWoDCA

Some of the track parameters used in the track selection require additional calculation effort and are then stored in a table called TracksExtended which is produced by either the o2-analysis-trackextension task (Run 2) or o2-analysis-track-propagation (Run 3). The quantities contained in this table can also be directly used in the analysis. For instance if you require the distance of closest approach (dca) to the primary vertex, you do not need to (and should not!) re-calculate it in your task. You can simply obtain it by extending the Tracks table in the following way and then access it directly as a property of the track:

void process(soa::Filtered<soa::Join<aod::Tracks, aod::TrackSelection, aod::TracksDCA>>::iterator const& track)

Both the definition of TrackSelection and the TracksDCA tables can be found here: TrackSelectionTables. If you want to have a look at the track parameters after the selection, you can use the o2-analysis-qa-event-track task:

o2-analysis-trackselection | o2-analysis-qa-event-track | ...

At the moment there are two ‘FilterBits' available in the TrackSelection table, which are defined as follows:

Cuts globalTrack globalTrackSDD
min number of crossed rows TPC 70 70
min ratio of crossed rows over findable clusters TPC 0.8 0.8
max chi2 per cluster TPC 4.0 4.0
max chi2 per cluster ITS 36.0 36.0
require TPC refit true true
require ITS refit true true
max DCA to vertex z 2.0 2.0
max DCA to vertex xy 0.0105 * 0.035 / pT1.1 0.0105 * 0.035 / pT1.1
cluster requirement ITS Run 2 (Run 3): at least one hit in SPD (in 3 innermost ITS layers) [*] no hit in SPD and hit in first SDD layer
pT range 0.1 - 1e10 0.1 - 1e10
η range -0.8 - 0.8 -0.8 - 0.8


RUN2 data/MC analyses (isRun3 == false)The default set of global-track selections requires at least 1 hit between the two innermost ITS layers (function getGlobalTrackSelection in TrackSelectionDefaults.h).This is a Run 1, 2 refuse when the SPD was equipped, and currently this is enabled ONLY for analyses on Run2 converted data (isRun3 == false).

RUN3 data/MC analyses (isRun3 == true)The same set of global-track selections, but with different ITS requirements for Run3 data are available in trackselection.cxx. This is possible thanks to the getGlobalTrackSelectionITSMatch in TrackSelectionDefaults.h, which can be enabled with different ITS requirements via the integer configurable itsMatching in trackselection.cxx. The available configurations are the following:

  • itsMatching == 0: at least one hit between the two innermost ITS layers (default for isRun3 == false). IMPORTANT: in case isRun3 == true, then itsMatching == 0 enables itsMatching == 1 automatically (a WARNING is dumped at runtime);
  • itsMatching == 1: at least one hit among the three innermost ITS layers (Run3ITSibAny, default for isRun3 == true);
  • itsMatching == 2: at least one hit among all the ITS layers (Run3ITSallAny);
  • itsMatching == 3: one hit on all the ITS layers (Run3ITSall7Layers);

The goal of the track selection task is to provide the most common selections for all analyses. If you really require a completely different set of tracks not covered by the standard filter bits, you can create your own TrackSelection object (see TrackSelectionTables.h and trackselection.cxx) :

TrackSelection myTrackSelection()
  TrackSelection selectedTracks;
  selectedTracks.SetPtRange(0.1f, 1e10f);
  selectedTracks.SetEtaRange(-0.8f, 0.8f);
  selectedTracks.SetRequireHitsInITSLayers(1, {0, 1}); // one hit in any SPD layer
  selectedTracks.SetMaxDcaXYPtDep([](float pt) { return 0.0105f + 0.0350f / pow(pt, 1.1f); });
  return selectedTracks;


// member of analysis task
TrackSelection mySelection;


// in init()
mySelection = myTrackSelection();


// in process()
bool isSelected = mySelection.IsSelected(track)

Remarks on track selection

Please note that this documentation only represents the status quo of the track selection implementation and many things can and will change. In particular the cut values will most likely change with the ‘new' detector in Run 3 and some of the legacy cuts will be removed or only available for converted Run 2 data. In case of questions or suggestions don't hesitate to contact us.

Track Propagation

The Run 3 AO2D stores the tracks at the point of innermost update. For a track with ITS this is the innermost (or second innermost) ITS layer. For a track without ITS, this is the TPC inner wall or for loopers in the TPC even a radius beyond that. In the AO2D.root the trees are therefore called O2tracks_IU and O2tracksCov_IU (IU = innermost update). The corresponding O2 data model tables are TracksIU and TracksCovIU, respectively. If your task needs tracks at the collision vertex it will fail because it looks for O2tracks and O2tracksCov.

In order to propagate the tracks to the collision vertex, include the task o2-analysis-track-propagation into your workflow. This task produces the tables Tracks and TracksCov (in order to get the latter, please enable processCovariance through the json configuration).


This task also produces the TrackExtended table needed for o2-analysis-track-selection, therefore o2-analysis-trackextension does not need to be added to the workflow at the same time.

This task is not needed for Run 2 converted data where the tracks are already propagated to the collision vertex.

The overall table flow is illustrated here:


The TrackTuner class (Common/Tools/TrackTuner.h) allows to smear the reconstructed track parameters. Such tool is primarely conceived to smear the parameters of tracks reconstructed in MC simulations according to the discrepancy between data and MC of the dcaXY and dcaZ.


This task was called improver-task in the Run 2 jargon

The smearing is done on the y, z parameters of each reconstructed track in MC evaluated at the associated particle production point. The smearing is based on the discrepancy between resolution, mean and pull ratio of dcaXY, dcaZ w.r.t. primary vertex measured in data and MC. The performance of such parameters is evaluated vs. global-track pt and stored into .root files, which can be read from CCDB at runtime.

An instance of the TrackTuner class is present as data-member in the trackPropagation workflow, and it can be enabled via

Configurable<bool> useTrackTuner{"useTrackTuner", false, "Apply Improver/DCA corrections to MC"};

The TrackTuner can be enabled only if the processCovarianceMc process function in the trackPropagation workflow is used

This object can be configured through the Configurable<std::string> trackTunerParams in the trackPropagation workflow. This configuration std::string must define the following parameters:

  • bool debugInfo: flag to switch on/off some debug outputs
  • bool updateTrackDCAs: flag to switch on/off the smearing of the dcaXY, dcaZ
  • bool updateTrackCovMat: flag to enable the update of the track covariance matrix, propagating the scaling on the dca resolution
  • bool updatePulls: flag to enable the update of the track covariance matrix updating also the pulls (if updateTrackCovMat == true)
  • std::string pathInputFile: path to browse to find the correction file for the dca smearing
  • std::string nameInputFile: name of the correction file for the dca smearing
  • bool isInputFileFromCCDB: the pathInputFile/nameInputFile is searched in CCDB if this flag is true, otherwise in the local file system (debug purposes)
  • bool usePvRefitCorrections: if this flag is true, the track smearing is performed using mean, resolution and pulls parametrizations vs. pt of dcaXY, dcaZ calculated w.r.t. primary collision vertex refitted w/o the current track, if this was originally a PV contributor

    In pp collisions, one should use usePvRefitCorrections == trueThis is not relevant in Pb-Pb collisions.

  • std::string pathFileQoverPt: path to browse to find the correction file for the q/pt smearing
  • std::string nameFileQoverPt: name of the correction file for the q/pt smearing
  • bool updateCurvature: flag to enable the update of the track curvature, i.e. q/pt, at the particle production point
  • bool updateCurvatureIU: flag to enable the update of the track curvature, i.e. q/pt, at the innermost update (IU) point
  • float oneOverPtMC (MC) and float oneOverPtData (data): the ratio oneOverPtData/oneOverPtMC defines the scaling factor to the q/pt residual to smear the track pt
  • fillTrackTunerTable: flag to enable the filling of a new table containing for each track the smeared q/pt at the IU point
  • The TrackTuner allows also to smear the q/pt if only one between updateCurvature and updateCurvatureIU is true
  • By default, the variables oneOverPtData and oneOverPtMC are initialized to -1
  • If at least one betweenqOverPtMCq and OverPtData is negative, the q/pt correction is done wuering the file from CCDB. Otherwise, the input values of qOverPtMC and qOverPtData are used to defined the factor oneOverPtData/oneOverPtMC, which is a constant factor flat in transverse momentum.

The string trackTunerParams must follow the format: <variable_name>=<value>|<variable_name>=<value> (see the default configuration here as reference).

The dcaXY, dcaZ parametrization currently available are the following: