.. _analysiskey: FAQ === ------------------------------------- PID corrections and systematic errors ------------------------------------- Efficiency correction factors and systematic errors for Belle PID were obtained using corresponding MC and data samples. Those studies were done for different PID selections, and possible selections differ for different hypotheses. There are three groups of lookup tables in Belle: * Lepton ID: * Possible cuts for electron: [0.01, 0.10, 0.50, 0.60, 0.80, 0.90] * Possible cuts for muons: [0.10, 0.80, 0.90, 0.95, 0.97] * Kaon/:math:`\pi` ID: * PID cut can be 0.1 - 0.9 * Proton ID: * Possible cuts for proton: [0.6, 0.7, 0.8, 0.9] Details of these tables and how they were studies can be found at `Belle PID Joint Homepage `_. The weights for LID and KID are available in ``basf2`` and can be retrieved via payloads in ``Belle_PID``; naming schemes of each PID payloads are slightly different. .. rubric:: Lepton ID Naming scheme for LID is ``BelleLID_``. Using ``eIDBelle>0.9`` as an example: .. code-block:: python import variables as va import basf2 as b2 b2.conditions.prepend_globaltag("BellePID") lid_table = "BelleLIDe_0.90" va.variables.addAlias('LID_ratio', f"extraInfo({lid_table}_ratio)") va.variables.addAlias('LID_ratio_stat_err', f"extraInfo({lid_table}_ratio_stat_err)") va.variables.addAlias('LID_ratio_syst_err', f"extraInfo({lid_table}_ratio_syst_err)") va.variables.addAlias('LID_ratio_syst_err_from_2photon', f"extraInfo({lid_table}_ratio_syst_err_from_2photon)") va.variables.addAlias('LID_ratio_syst_err_from_JPsi', f"extraInfo({lid_table}_ratio_syst_err_from_JPsi)") va.variables.addAlias('LID_run_bin', f"extraInfo({lid_table}_run_bin)") va.variables.addAlias('LID_kinematic_bin', f"extraInfo({lid_table}_kinematic_bin)") reweighter = b2.register_module('ParticleWeighting') reweighter.param('tableName', lid_table) reweighter.param('particleList', 'e+:all') my_path.add_module(reweighter) .. note:: The lepton ID efficiency correction tables with eIDBelle > 0.01 are only available for experiments 31-65. The corrections for other five selections on eIDBelle are available for all experiments, from 7 to 73. .. note:: The lepton ID fake-rate tables (lepton ID efficiency for pions and kaons) are not prepared at Belle. It can be studied using the :math:`D^{*}` sample (see ~nishida6/public/kid/00dstar_nt.txt on kekcc for details). .. rubric:: Kaon/:math:`\pi` ID Naming scheme for payloads is: ``BelleKID_`` Here, - K or Pi refers to correction information obtained for kaons or pions, accordingly - Eff or Fake refers to efficiency or fake rate corrections - Combined, Plus or Minus refers to the charge of tracks used for the study - PID cut is an integer number from 1 to 9 that corresponds to "cut > 0.number" To append PID correction information for pion efficiency selected as kaons with ``atcPIDBelle(3, 2)>0.6`` cut: .. code-block:: python kid_table = "BelleKIDPiFakePlus_6" va.variables.addAlias('KID_eff_data', f"extraInfo({kid_table}_eff_data)") va.variables.addAlias('KID_eff_data_stat_err', f"extraInfo({kid_table}_eff_data_stat_err)") va.variables.addAlias('KID_eff_data_syst_err', f"extraInfo({kid_table}_eff_data_syst_err)") va.variables.addAlias('KID_eff_mc', f"extraInfo({kid_table}_eff_mc)") va.variables.addAlias('KID_eff_mc_stat_err', f"extraInfo({kid_table}_eff_mc_stat_err)") va.variables.addAlias('KID_fit_flag', f"extraInfo({kid_table}_fit_flag)") va.variables.addAlias('KID_kinematic_bin', f"extraInfo({kid_table}_kinematic_bin)") va.variables.addAlias('KID_ratio', f"extraInfo({kid_table}_ratio)") va.variables.addAlias('KID_ratio_stat_err', f"extraInfo({kid_table}_ratio_stat_err)") va.variables.addAlias('KID_ratio_syst_err', f"extraInfo({kid_table}_ratio_syst_err)") va.variables.addAlias('KID_run_bin', f"extraInfo({kid_table}_run_bin)") kid_weights = ['KID_eff_data', 'KID_eff_data_stat_err', 'KID_eff_data_syst_err', 'KID_eff_mc', 'KID_eff_mc_stat_err', 'KID_fit_flag', 'KID_ratio', 'KID_ratio_stat_err', 'KID_ratio_syst_err', 'KID_run_bin', 'KID_kinematic_bin'] reweighter2 = b2.register_module('ParticleWeighting') reweighter2.param('tableName', kid_table) reweighter2.param('particleList', 'pi+:all') my_path.add_module(reweighter2) .. note:: ``fit_flag`` indicates if the fits failed; ``fit_flag = 0`` is for successful fit, while any non-zero values of fit_flag are for failed fit. Please only use ``fit_flag==0`` when evaluating PID contributions. .. rubric:: Proton ID Naming scheme for proton ID payloads is: ``BellePID_`` Here, - Plus or Minus refers to the charge of tracks used for the study - PID cut can be 0.6, 0.7, 0.8 or 0.9 If you'd like to add the information for proton efficiency selected with ``atcPIDBelle(4, 2)>0.6`` and ``atcPIDBelle(4, 3)>0.6``. .. code-block:: python pid_table = "BellePIDPlus_0.6" va.variables.addAlias("PID_eff_data", f"extraInfo({pid_table}_eff_data)") va.variables.addAlias("PID_eff_data_stat_err", f"extraInfo({pid_table}_eff_data_stat_err)") va.variables.addAlias("PID_eff_mc", f"extraInfo({pid_table}_eff_mc)") va.variables.addAlias("PID_eff_mc_stat_err", f"extraInfo({pid_table}_eff_mc_stat_err)") va.variables.addAlias("PID_ratio", f"extraInfo({pid_table}_ratio)") va.variables.addAlias("PID_ratio_stat_err", f"extraInfo({pid_table}_ratio_stat_err)") va.variables.addAlias("PID_ratio_syst_err", f"extraInfo({pid_table}_ratio_syst_err)") va.variables.addAlias("PID_kinematic_bin", f"extraInfo({pid_table}_kinematic_bin)") va.variables.addAlias("PID_run_bin", f"extraInfo({pid_table}_run_bin)") reweighter3 = b2.register_module('ParticleWeighting') reweighter3.param('tableName', pid_table) reweighter3.param('particleList', 'pi+:all') my_path.add_module(reweighter3) ------------------------- Full Event Interpretation ------------------------- To utilize FEI, the correct prefix of FEI payloads needs to be set: .. code-block:: python import fei basf2.conditions.append_globaltag("analysis_tools_light-2012-minos") configuration = fei.config.FeiConfiguration(prefix='FEI_B2BII_light-2012-minos', cache=0) feistate = fei.get_path(particles, configuration) path.add_path(feistate.path) For more details please see :code:`analysis/examples/FEI/B_converted_apply.py` -------------- Flavour Tagger -------------- To apply flavour tagger in a b2bii analysis, one will need to append the correct global tag. FlavorTagger will call the corresponding payloads in the module. .. code-block:: python import flavorTagger as ft # Flavour Tagger weightfiles = 'B2nunubarBGx1' basf2.conditions.append_globaltag("analysis_tools_light-2012-minos") ft.flavorTagger( particleLists=['B+:sig'], weightFiles=weightfiles, prefix='', path=my_path) ------------------------------------- BeamBackground MVA and FakePhoton MVA ------------------------------------- The weight files for the b2bii case are stored in the ``analysis_b2bii`` global tag. To apply beam background MVA and fake photon MVA in a b2bii analysis, the correct suffix, ``Belle``, should be used for the weight file, and apply them over the premade ``gamma:mdst`` list: .. code-block:: python import modularAnalysis as ma basf2.conditions.append_globaltag(ma.getAnalysisGlobaltagB2BII()) # Apply fake photon MVA ma.getFakePhotonProbability( particleList=['gamma:mdst'], weight="Belle", path=mypath) # Apply beamBackground MVA ma.getBeamBackgroundProbability( particleList=['gamma:mdst'], weight="Belle", path=mypath) .. note:: Details for the training done in Belle MC can be found in the appendix in `BELLE2-NOTE-PH-2023-036`_. .. _BELLE2-NOTE-PH-2023-036: https://docs.belle2.org/record/3754/