-- HiroakiHibi - 2020-09-10



This page provides an explanation of Low pT calo tag muon algorithm.
The target in the algorithm is low pt muon below 5 GeV .
To use this algorithm, information of calo sampling is needed.

Low pT calo tag muon can de used in

  • 1< pT[GeV] < 5
  • |eta| <1.6

Useful link

Slides in MCP meeting

Other TWiki page

Derivation framework

You can add this decorator to your Derivation. (example: SUSY19)

available DAOD files

user.hhibi:user.hhibi.MUON1.data18_13TeV.periodC.physics_Main.PhysCont_EXT0 user.hhibi:user.hhibi.MUON1.data18_13TeV.periodK.physics_Main.PhysCont.v5_EXT0 user.hhibi:user.hhibi.MUON1.data18_13TeV.periodO.physics_Main.PhysCont.v5_EXT0

user.gsumi:user.gsumi.MUON1.data17_13TeV.periodE.physics_Main.PhysCont_EXT0 user.gsumi:user.gsumi.MUON1.data17_13TeV.periodB.physics_Main.PhysCont_EXT0 user.gsumi:user.gsumi.MUON1.data17_13TeV.periodI.physics_Main.PhysCont_EXT0

user.hhibi:user.hhibi.MUON1.data16_13TeV.periodB.physics_Main.PhysCont_EXT0 user.hhibi:user.hhibi.MUON1.data16_13TeV.periodG.physics_Main.PhysCont.v2_EXT0 user.hhibi:user.hhibi.MUON1.data16_13TeV.periodE.physics_Main.PhysCont_EXT0 user.hhibi:user.hhibi.MUON1.data16_13TeV.periodB.physics_Main.PhysCont_EXT0


command example

please check AMIConfig tag in AMIConfig tag TWiki

pathena --trf "Reco_tf.py --inputAODFile %IN --outputDAODFile %OUT.DAOD.pool.root --reductionConf MUON1 --maxEvents -1 --AMIConfig p*****" --inDS NameOfinDS --outDS NameOfoutDS --nFilesPerJob 10

How to use

Input variables

30 inputs are needed to DNN

Where is the input variable in AOD?

The track is in the InDetTrackParticles container.
deposit energy is in the CaloCalTopoClusters container.

Where is the input variable in DAOD?

TrackAssociatedCaloSampleDecorator adds information on position matched sampling to a low pT track.
You can add this decorator. (example: SUSY19)

sampling information is saved as an aux-data for position matched track.

How to calculate low pt calo tag muon score from input variables

Lwtnn ( link to Github of lwtnn )

The DNN was trained by Keras(python).
lwtnn is used to implement the DNN in Athena (mainly c++).
input type should be store into std::map<std::string, double> .

Table of input name.

Input name (std::string)  
trackEta Eta of the track
trackPt Pt of the track in MeV
caloClus1_00 PreSamplerB
caloClus1_01 EMB1
caloClus1_02 EMB2
caloClus1_03 EMB3
caloClus1_04 PreSamplerE
caloClus1_05 EME1
caloClus1_06 EME2
caloClus1_07 EME3
caloClus1_08 HEC0
caloClus1_09 HEC1
caloClus1_10 HEC2
caloClus1_11 HEC3
caloClus1_12 TileBar0
caloClus1_13 TileBar1
caloClus1_14 TileBar2
caloClus1_15 TileGap1
caloClus1_16 TileGap2
caloClus1_17 TileGap3
caloClus1_18 TileExt0
caloClus1_19 TileExt1
caloClus1_20 TileExt2
caloClus1_21 FCAL0
caloClus1_22 FCAL1
caloClus1_23 FCAL2
caloClus1_24 MINIFCAL0
caloClus1_25 MINIFCAL1
caloClus1_26 MINIFCAL2
caloClus1_27 MINIFCAL3


Definition of WP

Progress of calibration development

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Topic revision: r5 - 2020-12-07 - HiroakiHibi
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