MachineLearningatLHCbMikhailHushchyn

NationalResearchUniversityHigherSchoolofEconomicsMoscowInstituteofPhysicsandTechnology

Yandex SchoolofDataAnalysis

onbehalfoftheLHCb collaboration

ICPPA2017,3-6October,Moscow

TheLHCb Detector• LHCb isasingle-armforwardspectrometer.• ThemaingoalofthedetectoristosearchforindirectevidenceofnewphysicsinCPviolationandraredecaysofbeautyandcharmhadrons.

Int.J.Mod.Phys.A30(2015)1530022

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WhydoweuseML?

40 MHz bunch crossing rate

450 kHzh±

400 kHzµ/µµ

150 kHze/γ

L0 Hardware Trigger : 1 MHz readout, high ET/PT signatures

Software High Level Trigger

12.5 kHz (0.6 GB/s) to storage

Partial event reconstruction, select displaced tracks/vertices and dimuons

Buffer events to disk, perform online detector calibration and alignment

Full offline-like event selection, mixture of inclusive and exclusive triggers

LHCb 2015 Trigger Diagram

TheLHCb detectorgeneratestoomuchdatatokeepitall.MachinelearningisneededforefficientselectionofthemostinterestingeventsinSoftwareHighLevelTrigger.

MLisalsousedin:• Trackpatternrecognition• Faketracksrejection• Particleidentification• Jetsidentification• DQmonitoring• Optimizeuseofstoragecapacity

TrackPatternRecognition

VELO track Downstream track

Long track

Upstream track

T track

VELOTT

T1 T2 T3

• VELOtracking:vertexreconstruction• Longtracks:usedinmajorityofanalyses(B/Ddecays)• Downstreamtracks:daughtersoflonglivedparticles• Averagetrackingefficiency>96%• Momentumresolutionvariesfrom0.5%atlowmomentumto1.0%at

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LongTracksReconstruction

StartingfromseedsintheVELO,tracksaresearchedinTstations:• SearchwindowinTstationsdefinedbyVELOtrack.• Projectx-hitintoreferenceplane.• Fit4-layer-x-cluster andremoveoutliers.• Addandfittrackwithstereohits.

TwoDeepNeuralNetworks:• Firstofthemistunedforrejectionofbad4-layer-x-clusters.• Secondoneistrainedforcandidatesselectionafterstereofit.

LHCb-PROC-2017-013

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DownstreamTracksReconstruction

ThealgorithmisseededbytracksreconstructedinTstations.

FindmatchingTThits

Results:3- 5%improvementinfaketrackrejectionandincreaseinsignalefficiency

Rejectionofabout40%offakeT-SeedsusingBosaiBDT[JINST 8 P02013]

VELO track Downstream track

Long track

Upstream track

T track

VELOTT

T1 T2 T3

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TMVAMLP

FakeTrackRejection

]2 [MeV/cπKm1800 1850 1900

)2ca

ndid

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LHCbpreliminary

π K→0all Dfakes

NNsaretrainedforbackgroundrejectionatgiven(97to99%)efficiency.Faketrack(ghost)probabilitybasedontheDNNoutputallowstoreducefakerate. Results:• Increasedefficiency• Reducedfakerate(22%→ 14%)

efficiency0 0.5 1

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LHCbpreliminary

ghost probability

/dof2χtrack fit

UsingDNNs

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ParticleIdentification• Problem:identifyparticletypeassociatedwithatrack.• Particletypes:Ghost,Electron,Muon,Pion,Kaon,Proton.• LHCb subdetectors:RICH,ECAL,HCAL,MuonChambersandTrack

observables• Differentparticletypeshasdifferentresponsesinthesubdetectors.• Theproblemcanbeconsideredasmulticlassclassificationproblemin

machinelearning.

LHCb RICH

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Int.J.Mod.Phys.A30(2015)1530022

ParticleIdentification• ThefirstmachinelearningalgorithmsusedforthePIDinLHCb isone-

hidden-layerneuralnetwork(TMVAMLP).• EachparticletypehasitsownbinaryNNtrainedinone-particle-vs-

restmode.

Σ# → 𝑝𝜇#𝜇& Σ# → 𝑝𝜇#𝜇&

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Plots:usingdatasidebands forbackgroundsandMonteCarlosimulation forthesignal

ParticleIdentification

• Onemodelforallparticletypes.

• ROCAUCs≈ 0.91 − 0.99 fordifferentparticletypes.

6xProbNNstrainedinone-vs-restmodeareconsideredasbaseline.

FurtherPIDperformanceimprovementisdoneusingdifferentmulticlassmodels:deepneuralnetworks(DNN)andBDTs(XGBoost andCatBoost):

Tracking System

ECAL & HCAL

RICH

Muon Chambers

!"#$%Ghost

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LHCb Simulation,preliminary

ParticleIdentificationSeveralDBTmodelswithflatefficienciesalong𝑷, 𝑷𝒕, 𝜼 and𝒏𝑻𝒓𝒂𝒄𝒌𝒔areprovided.Themodelsaretrainedwithspeciallossfunctiondescribedin[JINST10(2015)T03002].

LHCb Simulation,preliminary

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𝝅𝟎 − 𝜸 separationSignal:singlephoton𝛾.Background:photonsfrom𝜋= → 𝛾𝛾 decay.Problem: separatesignalandbackgroundclustersintheelectromagneticcalorimeter.

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𝝅𝟎 − 𝜸 separationBaselinesolution:• Clustersshapeandsymetryaredescribedbysetoffeatures.• 2-layersMLPistrainedtoseparatesignalandbackgroundclusters.

𝐵= → 𝐾∗=𝛾

MLPresponse>0.6𝜀BCD~98%,𝜀HID~55%

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𝝅𝟎 − 𝜸 separationNewapproach:• Responsesin5x5cellclustersforECALandpre-showerdetectorsare

consideredasnewfeatures.• SeveralNNandBDTmodelsaretrainedonthese2x25inputfeatures.• BDTmodelshowsbetterperformance.• Promisingpossibilityofaggressivebackgroundsuppressionis

demonstratedonsimulateddata.

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JetTaggingProblem:identifybandcjetswithasmallmisidentificationprobabilityoflight-parton jets.Theidentificationof(b,c)jetsisperformedusingSVsfromthedecaysof(b,c)hadrons.

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JetTaggingTwoBDTmodelsareconsidered:DBT(𝑏𝑐|𝑢𝑑𝑠𝑔)istrainedtoseparate𝑏𝑐 and𝑙𝑖𝑔ℎ𝑡 jets,BDT(𝑏|𝑐)istrainedtoseparate𝑏 and𝑐 jets.BothBDTsaretrainedonsimulatedsamplesofb,candlight-parton jets.10kinematicobservablesofSVsareusedasinputs.

LHCb-PAPER-2015-016

W+jet events

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JetTagging

The(b,c)-jetefficienciesversusthemistag probabilityoflight-partonjetsobtainedbyincreasingtheDBT(𝑏𝑐|𝑢𝑑𝑠𝑔)cut.

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TopologicalTrigger• ThegoalofHLT2topologicaltriggerisefficientselectionofanyB(and

D)decaywithatleast2chargeddaughters.• Itisdesignedtohandlethepossibleomissionofchildparticles.• InRun1,asimpleBDTwasusedtodefineinterestingSVs.• InRun2,thealgorithmisreoptimized usingseveralMLmodels.

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TopologicalTriggerHLT-1trackislookingforonesuperhighPTorhighdisplacedtrack.HLT-1trackMVAclassifierislookingfortwotracksmakingavertex.HLT-2topologicalclassifierusesfullreconstructedeventtolookfor2,3,4andmoretracksmakingavertex.KinematicobservablesofSVsareusedastheclassifiersinputs.

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J.Phys.:Conf.Ser.664(2015)082025

HLT-1track:90kHZ HLT-1trackMVA:40kHZ

TopologicalTrigger• SeveralMLmodelsareconsideredduringthetriggerreoptimization:

BDTs(MatrixNet),NeuralNetworks,LogisticRegression.• ROCcurveinaregionwithsmallFalsePositiveRateisoptimized.

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J.Phys.:Conf.Ser.664(2015)082025

LHCb Simulation,preliminary

LHCb Simulation,preliminary

TopologicalTrigger• Mostn-bodyhadronicBdecays(n≥3)areonlytriggeredon

efficientlyinLHCb bythetopologicaltrigger.• Gain50%..80%efficiencyfordifferentchannels.

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J.Phys.:Conf.Ser.664(2015)082025

𝜀VWX(𝑅𝑢𝑛2)/𝜀VWX(𝑅𝑢𝑛1)

JetTagging&TOPOTrigger• ThetopologicaltriggeralgorithmusesSVsthatsatisfysimilarcriteria

tothoseusedintheSV-taggeralgorithmtobuildtwo-,three- andfour-trackSVs.

• TheSVusedbytheTOPOtotriggerrecordingoftheeventcanalsobeusedtotagabjet.

• TheBDTusedintheTOPOalgorithmusessimilarinputsasjet-taggerBDTmodels.

The“loose”labelfortheTOPOreferstotheBDTrequirementusedinthetrigger forSVsthatcontainmuoncandidates.

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LHCb-PAPER-2015-016

DQMonitoringRobo-Shifter

• Robo-shifterismachine-learningbasedsystemdesignedtoassiststheDQshifter.

• Givenrundataitcanpredictprobabilityofrunbeinggoodorbad.

• Providespotentialproblemsourcesextractedfromdecisiontrees.

• Thefirstversionofrobo-shifteriscurrentlybeingtestedbytheDQshifters.

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MachinelearningiseverywhereatLHCb helpingtoimprovethedetectoroperationanddataprocessing.

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