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HiggsToBBNtupleProducerTool

ROOT ntuple producer for developing machine learning algorithms to differentiate jets originating from a Higgs boson decaying to a bottom quark-antiquark pair (Hbb) from quark or gluon jets originating from quantum chromodynamic (QCD) multijet production.

Setup

Running the data processing uses CMSSW_8_0_28

cmsrel CMSSW_8_0_28
cd CMSSW_8_0_28/src/
cmsenv
git clone https://github.com/cms-opendata-analyses/HiggsToBBNtupleProducerTool DeepNTuples -b opendata_80X
scram b -j 4
# if using CMS resources (like CRAB, you can set it up after:)
# voms-proxy-init -rfc -voms cms --valid 168:00

Create ROOT ntuples

To run the python config, navigate to the test directory, and run it on an example file, by default root://eospublic.cern.ch//eos/opendata/cms/MonteCarlo2016/RunIISummer16MiniAODv2/BulkGravTohhTohbbhbb_narrow_M-2500_13TeV-madgraph/MINIAODSIM/PUMoriond17_80X_mcRun2_asymptotic_2016_TrancheIV_v6-v1/80000/0A83E4E2-34B6-E611-89A0-549F35AE4FA2.root

cd DeepNTuples/NtupleAK8/test
cmsRun DeepNtuplizerAK8.py

Repeat this for all of the input files in the QCD and Hbb datasets.

Merge outputs (with random mixing of different samples)

  1. First create the file list (in this case we only take up to 3 output ROOT files for each QCD or Hbb sample).
cd DeepNTuples/NtupleAK8/run
export OUTDIR=/path/to/files/
ls $OUTDIR > samples.txt
for i in `cat samples.txt`; 
 do cd ${OUTDIR}/${i}; 
 ls */*/*/output_*.root | head -3 > ${i}_max3files.txt;
 cd -; 
done
  1. Merge the samples (with random mixing)
mergeSamples.py [events per output file] [output dir] [path to the filelist produced in step 1]

e.g.,

export MERGEDIR=/path/to/files/merged_max3files/
mergeSamples.py 200000 ${MERGEDIR} ${OUTDIR}/QCD_Pt_*/QCD_Pt_*max3files.txt ${OUTDIR}/Bulk*/Bulk*max3files.txt
  1. Split into training and testing samples
export TRAINDIR=${MERGEDIR}/train
export TESTDIR=${MERGEDIR}/test
mkdir -p $TRAINDIR $TESTDIR
mv ${MERGEDIR}/ntuple_merged_[.0-8.].root ${TESTDIR}/
mv ${MERGEDIR}/ntuple_merged_*.root ${TRAINDIR}/

Convert ROOT files to HDF5 files using uproot

This step requires a more recent version of CMSSW.

cmsrel CMSSW_10_4_0
cd CMSSW_10_4_0/src/
cmsenv
wget https://raw.githubusercontent.com/cms-opendata-analyses/HiggsToBBNtupleProducerToo/opendata_80X/NtupleAK8/scripts/convert-uproot-opendata.py

Then you can run

python convert-uproot-opendata.py [input file (.root)] [output file (.h5)]

e.g.,

python convert-uproot-opendata.py ${TRAINDIR}/ntuple_merged_10.root ${TRAINDIR}/ntuple_merged_10.h5

which produces HDF5 files with different arrays for each output variable. Note that during this conversion, only the information for up to 100 particle candidates, 60 tracks, and 5 secondary vertices are saved in flattened, zero-padded, fixed-length arrays.

Data set content

Variables are saved to the ROOT trees and HDF5 tables jet by jet.

The reconstructed jets are clustered using the anti-kT algorithm with R=0.8 from particle flow (PF) candidates (AK8 jets). The standard L1+L2+L3+residual jet energy corrections are applied to the jets and pileup contamination is mitigated using the charged hadron subtraction (CHS) algorithm. Features of the AK8 jets with transverse momentum pT > 200 GeV and pseudorapidity |η| < 2.4 are provided. Selected features of inclusive (both charged and neutral) PF candidates with pT > 0.95 GeV associated to the AK8 jet are provided. Additional features of charged PF candidates (formed primarily by a charged particle track) with pT > 0.95 GeV associated to the AK8 jet are also provided. Finally, additional features of reconstructed secondary vertices (SVs) associated to the AK8 jet (within ∆R < 0.8) are also provided.

Data variable Type Description
event_no UInt_t Event number
npv Float_t Number of reconstructed primary vertices (PVs)
ntrueInt Float_t True mean number of the poisson distribution for this event from which the number of interactions in each bunch crossing has been sampled
rho Float_t Median density (in GeV/A) of pile-up contamination per event; computed from all PF candidates of the event
sample_isQCD Int_t Boolean that is 1 if the simulated sample corresponds to QCD multijet production
label_H_bb Int_t Boolean that is 1 if a Higgs boson is matched and at least two b quarks are found within the AK8 jet
label_QCD_bb Int_t Boolean that is 1 if no resonances are matched and at least two b quarks are found within the AK8 jet
label_QCD_b Int_t Boolean that is 1 if no resonances are matched and only one b quark is found within the AK8 jet
label_QCD_cc Int_t Boolean that is 1 if no resonances are matched and at least two c quarks are found within the AK8 jet
label_QCD_c Int_t Boolean that is 1 if no resonances are matched and only one c quark is found within the AK8 jet
label_QCD_others Int_t Boolean that is 1 if no resonances are matched and no b or c quarks are found within the AK8 jet
fj_doubleb Float_t Double-b tagging discriminant based on a boosted decision tree calculated for the AK8 jet (see CMS-BTV-16-002)
fj_eta Float_t Pseudorapidity η of the AK8 jet
fj_gen_eta Float_t Pseudorapidity η of the generator-level, matched heavy particle: H, W, Z, top, etc. (default = -999)
fj_gen_pt Float_t Transverse momentum of the generator-level, geometrically matched heavy particle: H, W, Z, t, etc. (default = -999)
fj_isBB Int_t Boolean that is 1 if two or more b hadrons are clustered within the AK8 jet (see SWGuideBTagMCTools)
fj_isNonBB Int_t Boolean that is 1 if fewer than two b hadrons are clustered within the AK8 jet (see SWGuideBTagMCTools)
fj_nbHadrons Int_t Number of b hadrons that are clustered within the AK8 jet (see SWGuideBTagMCTools)
fj_ncHadrons Int_t Number of c hadrons that are clustered within the AK8 jet (see SWGuideBTagMCTools)
fj_isH Int_t Boolean that is 1 if a generator-level Higgs boson and its daughters are geometrically matched to the AK8 jet
fj_isTop Int_t Boolean that is 1 if a generator-level top quark and its daughters are geometrically matched to the AK8 jet
fj_isW Int_t Boolean that is 1 if a generator-level W boson and its daughters are geometrically matched to the AK8 jet
fj_isZ Int_t Boolean that is 1 if a generator-level Z boson and its daughters are geometrically matched to the AK8 jet
fj_isQCD Int_t Boolean that is 1 if none of the above matching criteria are satisfied (H, top, W, Z)
fj_label Int_t Integer label: Invalid=0, Top_all=10, Top_bcq=11, Top_bqq=12, Top_bc=13, Top_bq=14, W_all=20, W_cq=21, W_qq=22, Z_all=30, Z_bb=31, Z_cc=32, Z_qq=33, H_all=40, H_bb=41, H_cc=42, H_qqqq=43, QCD_all=50, QCD_bb=51, QCD_cc=52, QCD_b=53, QCD_c=54, QCD_others=55
fj_labelJMAR Int_t Alternative integer label from the CMS Jet/MET and Resolution (JMAR) group: Default=0, Top=1, W=2, Z=3, H=4
fj_labelLegacy Int_t Alternative (legacy) integer label: Default=0, Top=1, W=2, Z=3, H=4
fj_jetNTracks Float_t Number of tracks associated with the AK8 jet
fj_nSV Float_t Number of SVs associated with the AK8 jet (∆R < 0.7)
fj_n_sdsubjets Float_t Number of soft drop subjets in the AK8 jet (up to 2)
fj_mass Float_t Ungroomed mass of the AK8 jet
fj_phi Float_t Azimuthal angle ϕ of the AK8 jet
fj_pt Float_t Transverse momentum of the AK8 jet
fj_tau1 Float_t N-subjettiness variable for a 1-prong jet hypothesis
fj_tau2 Float_t N-subjettiness variable for a 2-prong jet hypothesis
fj_tau3 Float_t N-subjettiness variable for a 3-prong jet hypothesis
fj_tau21 Float_t N-subjettiness variable for 2-prong vs 1-prong jet discrimination (fj_tau2/fj_tau1)
fj_tau32 Float_t N-subjettiness variable for 3-prong vs 2-prong jet discrimination (fj_tau3/fj_tau2)
fj_sdmass Float_t Soft drop mass of the AK8 jet
fj_ptDR Float_t Transverse momentum times the ΔR between the two soft drop subjets
fj_relptdiff Float_t Absolute relative difference between the transverse momenta of the two softdrop subjets
fj_sdn2 Float_t Fraction of second subjet transverse momentum times ∆R squared
fj_sdsj1_axis1 Float_t First axis of the first subjet
fj_sdsj1_axis2 Float_t Second axis of the first subjet
fj_sdsj1_csv Float_t Combined secondary vertex (CSV) b-tagging discriminant for the first subjet
fj_sdsj1_eta Float_t Pseudorapidity η of the first subjet
fj_sdsj1_mass Float_t Mass of the first subjet
fj_sdsj1_mult Float_t Particle multiplicity of the first subjet
fj_sdsj1_phi Float_t Azimuthal angle ϕ of the first subjet
fj_sdsj1_pt Float_t Transverse momentum of the first subjet
fj_sdsj1_ptD Float_t ptD variable, defined as the square root of the sum in quadrature of the transverse momentum of the subjet constituents divided by the scalar sum of the transverse momentum of the subjet constituents, for the first subjet (see CMS-PAS-JME-13-002)
fj_sdsj2_axis1 Float_t First axis of the first subjet
fj_sdsj2_axis2 Float_t Second axis of the first subjet
fj_sdsj2_csv Float_t Combined secondary vertex (CSV) b-tagging discriminant for the first subject
fj_sdsj2_eta Float_t Pseudorapidity η of the second subjet
fj_sdsj2_mass Float_t Mass of the second subjet
fj_sdsj2_mult Float_t Particle multiplicity of the second subjet
fj_sdsj2_phi Float_t Azimuthal angle ϕ of the second subjet
fj_sdsj2_pt Float_t Transverse momentum of the second subjet
fj_sdsj2_ptD Float_t ptD variable, defined as the square root of the sum in quadrature of the transverse momentum of the subjet constituents divided by the scalar sum of the transverse momentum of the subjet constituents, for the second subjet (see CMS-PAS-JME-13-002)
fj_z_ratio Float_t z ratio variable as defined in CMS-BTV-16-002
fj_trackSipdSig_0 Float_t First largest track 3D signed impact parameter significance (see CMS-BTV-16-002 )
fj_trackSipdSig_1 Float_t Second largest track 3D signed impact parameter significance (see CMS-BTV-16-002 )
fj_trackSipdSig_2 Float_t Third largest track 3D signed impact parameter significance (see CMS-BTV-16-002 )
fj_trackSipdSig_3 Float_t Fourth largest track 3D signed impact parameter significance (see CMS-BTV-16-002 )
fj_trackSipdSig_0_0 Float_t First largest track 3D signed impact parameter significance associated to the first N-subjettiness axis
fj_trackSipdSig_0_1 Float_t Second largest track 3D signed impact parameter significance associated to the first N-subjettiness axis
fj_trackSipdSig_1_0 Float_t First largest track 3D signed impact parameter significance associated to the second N-subjettiness axis
fj_trackSipdSig_1_1 Float_t Second largest track 3D signed impact parameter significance associated to the second N-subjettiness axis
fj_trackSip2dSigAboveCharm_0 Float_t Track 2D signed impact parameter significance of the first track lifting the combined invariant mass of the tracks above the c hadron threshold mass (1.5 GeV)
fj_trackSip2dSigAboveBottom_0 Float_t Track 2D signed impact parameter significance of the first track lifting the combined invariant mass of the tracks above b hadron threshold mass (5.2 GeV)
fj_trackSip2dSigAboveBottom_1 Float_t Track 2D signed impact parameter significance of the second track lifting the combined invariant mass of the tracks above b hadron threshold mass (5.2 GeV)
fj_tau0_trackEtaRel_0 Float_t Smallest track pseudorapidity ∆η, relative to the jet axis, associated to the first N-subjettiness axis
fj_tau0_trackEtaRel_1 Float_t Second smallest track pseudorapidity ∆η, relative to the jet axis, associated to the first N-subjettiness axis
fj_tau0_trackEtaRel_2 Float_t Third smallest track pseudorapidity ∆η, relative to the jet axis, associated to the first N-subjettiness axis
fj_tau1_trackEtaRel_0 Float_t Smallest track pseudorapidity ∆η, relative to the jet axis, associated to the second N-subjettiness axis
fj_tau1_trackEtaRel_1 Float_t Second smallest track pseudorapidity ∆η, relative to the jet axis, associated to the second N-subjettiness axis
fj_tau1_trackEtaRel_2 Float_t Third smallest track pseudorapidity ∆η, relative to the jet axis, associated to the second N-subjettiness axis
fj_tau_vertexMass_0 Float_t Total SV mass for the first N-subjettiness axis, defined as the invariant mass of all tracks from SVs associated with the first N-subjettiness axis
fj_tau_vertexMass_1 Float_t Total SV mass for the second N-subjettiness axis, defined as the invariant mass of all tracks from SVs associated with the second N-subjettiness axis
fj_tau_vertexEnergyRatio_0 Float_t SV vertex energy ratio for the first N-subjettiness axis, defined as the total energy of all SVs associated with the first N-subjettiness axis divided by the total energy of all the tracks associated with the AK8 jet that are consistent with the PV
fj_tau_vertexEnergyRatio_1 Float_t SV energy ratio for the second N-subjettiness axis, defined as the total energy of all SVs associated with the first N-subjettiness axis divided by the total energy of all the tracks associated with the AK8 jet that are consistent with the PV
fj_tau_flightDistance2dSig_0 Float_t Transverse (2D) flight distance significance between the PV and the SV with the smallest uncertainty on the 3D flight distance associated to the first N-subjettiness axis
fj_tau_flightDistance2dSig_1 Float_t Transverse (2D) flight distance significance between the PV and the SV with the smallest uncertainty on the 3D flight distance associated to the second N-subjettiness axis
fj_tau_vertexDeltaR_0 Float_t Pseudoangular distance ∆R between the first N-subjettiness axis and SV direction
n_pfcands Int_t Number of particle flow (PF) candidates associated to the AK8 jet with transverse momentum greater than 0.95 GeV
npfcands Float_t Number of particle flow (PF) candidates associated to the AK8 jet with transverse momentum greater than 0.95 GeV
pfcand_VTX_ass Int_t PV association quality for the PF candiate: UsedInFitTight=7, the track is used in the PV fit and the weight is above 0.5; UsedInFitLoose=6, the track is used in the PV fit and the weight is below 0.5; CompatibilityDz=5 the track is not used in fit but is very close in dz to the PV (dzsig < 5 or dz < 300 um); CompatibilityBTag=4, the track is not compatible with the PV but it is close to the nearest jet axis starting from the PV (distance to jet axis < 700 um); NotReconstructedPrimary=0, the track is not associated to any PV and is compatible with the beam spot hence it is likely to be originating from an interaction for which we did not reconstruct the PV (beam spot compatibility: dxysig < 2 and dxy < 200 um); OtherDeltaZ=1, none of the above criteria is satisfied, hence the closest in dz vertex is associated)
pfcand_charge Float_t Electric charge of the PF candidate
pfcand_deltaR Float_t Pseudoangular distance ∆R between the PF candidate and the AK8 jet axis
pfcand_drminsv Float_t Minimum pseudoangular distance ∆R between the associated SVs and the PF candidate
pfcand_drsubjet1 Float_t Pseudoangular distance ∆R between the PF candidate and the first soft drop subjet
pfcand_drsubjet2 Float_t Pseudoangular distance ∆R between the PF candidate and the second soft drop subjet
pfcand_dxy Float_t Transverse (2D) impact paramater of the PF candidate, defined as the distance of closest approach of the PF candidate trajectory to the beam line in the transverse plane to the beam
pfcand_dxysig Float_t Transverse (2D) impact paramater significance of the PF candidate
pfcand_dz Float_t Longitudinal impact parameter, defined as the distance of closest approach of the PF candidate trajectory to the PV projected on to the z direction
pfcand_dzsig Float_t Longitudinal impact parameter significance of the PF candidate
pfcand_erel Float_t Energy of the PF candidate divided by the energy of the AK8 jet
pfcand_etarel Float_t Pseudorapidity of the PF candidate relative to the AK8 jet axis
pfcand_phirel Float_t Azimuthal angular distance ∆ϕ between the PF candidate and the AK8 jet axis
pfcand_ptrel Float_t Transverse momentum of the PF candidate divided by the transverse momentum of the AK8 jet
pfcand_fromPV Float_t Integer indicating whether the PF candidate is consistent with the PV: PVUsedInFit=3, if the track is used in the PV fit; PVTight=2 if the track is not used in the fit of any of the other PVs and is closest in z to the PV, PVLoose=1 if the track is closest in z to a PV other then the PV; NoPV=0 if the track is used in the fit of another PV
pfcand_hcalFrac Float_t Fraction of energy of the PF candidate deposited in the hadron calorimeter
pfcand_isChargedHad Float_t Boolean that is 1 if the PF candidate is classified as a charged hadron
pfcand_isEl Float_t Boolean that is 1 if the PF candidate is classified as an electron
pfcand_isGamma Float_t Boolean that is 1 if the PF candidate is classified as an photon
pfcand_isMu Float_t Boolean that is 1 if the PF candidate is classified as an muon
pfcand_isNeutralHad Float_t Boolean that is 1 if the PF candidate is classified as a neutral hadron
pfcand_lostInnerHits Float_t Integer with information related to inner silicon tracker hits for the PF candidate: validHitInFirstPixelBarrelLayer=-1, if the track has a valid hit in the first pixel barrel layer; noLostInnerHits=0 if it does not have such hit because of geometrical or detector inefficiencies (i.e. the hit wasn't expected to be there); oneLostHit=1, if the track extrapolation towards the beam line crosses an active detector but no hit is found there; moreLostHits=2 if there are at least two missing expected inner hits
pfcand_mass Float_t Mass of the PF candidate
pfcand_puppiw Float_t Pileup per-particle identification (PUPPI) weight indicating whether the PF candidate is pileup-like (0) or not (1)
n_tracks Int_t Number of tracks associated with the AK8 jet
ntracks Float_t Number of tracks associated with the AK8 jet
trackBTag_DeltaR Float_t Pseudoangular distance ∆R between the track and the AK8 jet axis
trackBTag_Eta Float_t Pseudorapidity η of the track
trackBTag_EtaRel Float_t Pseudorapidity ∆η of the track relative the AK8 jet axis
trackBTag_JetDistVal Float_t Minimum track approach distance to the AK8 jet axis
trackBTag_Momentum Float_t Momentum of the track
trackBTag_PPar Float_t Component of track momentum parallel to the AK8 jet axis
trackBTag_PParRatio Float_t Component of track momentum parallel to the AK8 jet axis, normalized to the track momentum
trackBTag_PtRatio Float_t Component of track momentum perpendicular to the AK8 jet axis, normalized to the track momentum
trackBTag_PtRel Float_t Component of track momentum perpendicular to the AK8 jet axis
trackBTag_Sip2dVal Float_t Transverse (2D) signed impact paramater of the track
trackBTag_Sip2dSig Float_t Transverse (2D) signed impact paramater significance of the track
trackBTag_Sip3dSig Float_t 3D signed impact parameter significance of the track
trackBTag_Sip3dVal Float_t 3D signed impact parameter of the track
track_VTX_ass Float_t PV association quality for the track: UsedInFitTight=7, the track is used in the PV fit and the weight is above 0.5; UsedInFitLoose=6, the track is used in the PV fit and the weight is below 0.5; CompatibilityDz=5 the track is not used in fit but is very close in dz to the PV (dzsig < 5 or dz < 300 um); CompatibilityBTag=4, the track is not compatible with the PV but it is close to the nearest jet axis starting from the PV (distance to jet axis < 700 um); NotReconstructedPrimary=0, the track is not associated to any PV and is compatible with the BeamSpot hence it is likely to be originating from an interaction for which we did not reconstruct the PV (beam spot compatibility: dxysig < 2 and dxy < 200 um); OtherDeltaZ=1, none of the above criteria is satisfied, hence the closest in dZ vertex is associated)
track_charge Float_t Electric charge of the charged PF candidate
track_deltaR Float_t Pseudoangular distance (∆R) between the charged PF candidate and the AK8 jet axis
track_detadeta Float_t Track covariance matrix entry (eta, eta)
track_dlambdadz Float_t Track covariance matrix entry (lambda, dz)
track_dphidphi Float_t Track covariance matrix entry (phi, phi)
track_dphidxy Float_t Track covariance matrix entry (phi, xy)
track_dptdpt Float_t Track covariance matrix entry (pT, pT)
track_dxydxy Float_t Track covariance matrix entry (dxy, dxy)
track_dxydz Float_t Track covariance matrix entry (dxy, dz)
track_dzdz Float_t Track covariance matrix entry (dz, dz)
track_drminsv Float_t Minimum pseudoangular distance ∆R between the associated SVs and the charged PF candidate
track_drsubjet1 Float_t Pseudoangular distance ∆R between the charged PF candidate and the first soft drop subjet
track_drsubjet2 Float_t Pseudoangular distance ∆R between the charged PF candidate and the second soft drop subjet
track_dxy Float_t Transverse (2D) impact parameter of the track, defined as the distance of closest approach of the track trajectory to the beam line in the transverse plane to the beam
track_dxysig Float_t Transverse (2D) impact parameter significance of the track
track_dz Float_t Longitudinal impact parameter, defined as the distance of closest approach of the track trajectory to the PV projected on to the z direction
track_dzsig Float_t Longitudinal impact parameter significance of the track
track_erel Float_t Energy of the charged PF candidate divided by the energy of the AK8 jet
track_etarel Float_t Pseudorapidity ∆η of the track relative to the jet axis
track_fromPV Float_t Integer indicating whether the charged PF candidate is consistent with the PV: PVUsedInFit=3, if the track is used in the PV fit; PVTight=2 if the track is not used in the fit of any of the other PVs and is closest in z to the PV, PVLoose=1 if the track is closest in z to a PV other then the PV; NoPV=0 if the track is used in the fit of another PV
track_isChargedHad Float_t Boolean that is 1 if the charged PF candidate is classified as a charged hadron
track_isEl Float_t Boolean that is 1 if the charged PF candidate is classified as an electron
track_isMu Float_t Boolean that is 1 if the charged PF candidate is classified as a muon
track_lostInnerHits Float_t Integer with information related to inner silicon tracker hits for the track: validHitInFirstPixelBarrelLayer=-1, if the track has a valid hit in the first pixel barrel layer; noLostInnerHits=0 if it does not have such hit because of geometrical or detector inefficiencies (i.e. the hit wasn't expected to be there); oneLostHit=1, if the track extrapolation towards the beam line crosses an active detector but no hit is found there; moreLostHits=2 if there are at least two missing expected inner hits
track_mass Float_t Mass of the charged PF candidate
track_normchi2 Float_t Normalized χ2 of the track fit
track_phirel Float_t Azimuthal angular distance ∆ϕ between the charged PF candidate and the AK8 jet axis
track_pt Float_t Transverse momentum of the charged PF candidate
track_ptrel Float_t Transverse momentum of the charged PF candidate divided by the transverse momentum of the AK8 jet
track_puppiw Float_t Pileup per-particle identification (PUPPI) weight indicating whether the PF candidate is pileup-like (0) or not (1)
track_quality Float_t Track quality: undefQuality=-1; loose=0; tight=1; highPurity=2; confirmed=3, if track found by more than one iteration; looseSetWithPV=5; highPuritySetWithPV=6, discarded=7 if a better track found; qualitySize=8
n_sv Int_t Number of secondary vertices (SV) associated with the AK8 jet (∆R < 0.8)
nsv Float_t Number of secondary vertices (SV) associated with the AK8 jet (∆R < 0.8)
sv_chi2 Float_t χ2 of the vertex fit
sv_ndf Float_t number of degrees of freedom of the vertex fit
sv_normchi2 Float_t χ2 divided by the number of degrees of freedom for the vertex fit
sv_costhetasvpv Float_t Cosine of the angle cos(θ) between the SV and the PV
sv_d3d Float_t 3D flight distance of the SV
sv_d3derr Float_t 3D flight distance uncertainty of the SV
sv_d3dsig Float_t 3D flight distance significance of the SV
sv_dxy Float_t Transverse (2D) flight distance of the SV
sv_dxyerr Float_t Transverse (2D) flight distance uncertainty of the SV
sv_dxysig Float_t Transverse (2D) flight distance significance of the SV
sv_deltaR Float_t Pseudoangular distance ∆R between the SV and the AK8 jet
sv_erel Float_t Energy of the SV divided by the energy of the AK8 jet
sv_etarel Float_t Pseudorapidity ∆η of the SV relative to the AK8 jet axis
sv_mass Float_t Mass of the SV
sv_ntracks Float_t Number of tracks associated with the SV
sv_phirel Float_t Azimuthal angular distance ∆ϕ of the SV relative to the jet axis
sv_pt Float_t Transverse momentum of the SV
sv_ptrel Float_t Transverse momentum of the SV divided by the transverse momentum of the AK8 jet

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