MachineLearningapproachestotop-quark

taggingINFIERISummerSchool,Wuhan

12-26May2019

LisaBenato(1),PatrickConnor(2),GregorKasieczka(1),DirkKrücker(2),MareikeMeyer(2) (1)UniversitätHamburg;(2)DESY �1

● Ifyouarehere,mostlikelyyouhaveattendedthefirstpartofthislab…● ...or,youalreadyhavesomeexperiencewithmachinelearning(ML)● Weassumeyouhaveabasicknowledgeofpython,andthatyouknowthe

meaningoftrainingandtestingtheperformancesofaneuralnetwork(ifnot,ask)

● Inthislab,wewillapplymachinelearningtechniquestosolveonehigh-energyphysicsproblem

● Thispresentationisjustaquickoverview:youwillfindverydetailedexplanationsintheexercise’snotebooks

● WehaveorganizedaMLchallenge● Everybodyiswelcometoparticipate:rulesexplainedinthenextslides● Thewinnersofthechallengewillpresenttheirsolutionatthepostersession!

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Introduction

Particlephysicsinanutshell

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● TheStandardModelofparticlesisourpresentknowledgeofthemicroscopicworld

● Itdescribesthematterconstituents(quarksandleptons)andtheirinteractions(mediatedbybosons)

● Mostrecentsuccess:discoveryoftheHiggsbosonin2012byATLASandCMSexperimentsatLHC(Geneva)

● Butsomequestionsarestillopen!● Wearetryingtoanswerwithprecision

measurementsandsearchingfor“newphysics”...

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Startingfromthetop● Topquarkistheheaviestknownparticle

(massof172.5GeV)● Veryshortlifetime(10-25seconds):wecan

onlyseeitsdecayproducts● Discoveredin1995atD0andCDF

experimentsatFermilab(Chicago)● Keyparticletosearchesfornewphysics

beyondtheStandardModelandtoprecisionmeasurements

● Mostchallenging(andinteresting)topquarkdecay:“hadronic”t→Wb→qq’b

Inthisexample,qandq’areadownandanupquark

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Howtofindatopquark(I)1. Produceit→takeanhadroncollider,LHC

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Howtofindatopquark(II)1. Produceit→takeanhadroncollider,LHC2. Detectitsdecayproducts→takeadetector,

suchasCMS,thatreconstructstheenergyandpositionofeachparticle

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Howtofindatopquark(III)1. Produceit→takeanhadroncollider,LHC2. Detectitsdecayproducts→takeadetector,

suchasCMS,thatreconstructstheenergyandpositionofeachparticle

3. Combinethereconstructedparticlesinhigherlevelobjects→usededicated“jet”algorithms

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Howtofindatopquark(IV)1. Produceit→takeanhadroncollider,LHC2. Detectitsdecayproducts→takeadetector,

suchasCMS,thatreconstructstheenergyandpositionofeachparticle

3. Combinethereconstructedparticlesinhigherlevelobjects→usededicated“jet”algorithms

4. Distinguishtopdecayproductsfrombackgroundevents→useyourphysicalknowledgetounderstandthedifferences

https://arxiv.org/abs/1011.2268

https://arxiv.org/abs/1011.2268

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Howtofindatopquark(V)1. Produceit→takeanhadroncollider,LHC2. Detectitsdecayproducts→takeadetector,

suchasCMS,thatreconstructstheenergyandpositionofeachparticle

3. Combinethereconstructedparticlesinhigherlevelobjects→usededicated“jet”algorithms

4. Distinguishtopdecayproductsfrombackgroundevents→useyourphysicalknowledgetounderstandthedifferences

5. Improveresultswithmachinelearningtaggers!

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Whyistoptaggingcomplicated?(I)● Duetothenatureofstronginteraction,quarks

donottravelfree● Theyareforcedtobe"confined"intohadrons

("combination"ofquarksthatisneutralunderthestronginteraction)

● Quarksarenotdetectedassingleisolatedparticles,butasajetofparticles

● Jetalgorithmsareabletoclustertogethertheparticlescomingfromaquark

● Designedsuchinawaythatthemomentumoftheclusteredjetisproportionaltotheinitialenergyofthequark

Fromquarkstojets

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Whyistoptaggingcomplicated?(II)● Producingtopquarksis“difficult”● Topquarkproductionisarelatively

“rare”phenomenon(topquarkproductionhasasmallcross-section)

● Otherprocessesinitiatedbystronginteraction(QCD)occurwaymoreoften

● Theyproducelighterquarks(up,down,strange,...)

● Theylooksimilartotopquarksandtheyhappenenormouslymoreoften

● Fightingagainstthisbackgroundisahugechallenge! Interestingevent

Signal Rareevent

UninterestingBackground Frequentevent

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Physicallymotivatedapproach: jetsubstructure

Top→3-pronged QCD→1-pronged

● Veryintuitiveidea:○ topquarkdecaysproduce3quarks○ stronginteractionprocessinvolves

(usually)1quark● n-subjettiness:distinguisheshowmany

"sub-jets"areincludedinajet○ Top→3-prongedjet○ QCD→1-prongedjet

● Jetinvariantmassisalsoagooddiscriminator

● ThesepropertiescanbelearnedbyMLapproaches!

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Machinelearningformulation

signal

● Wemustsolveabinaryclassificationproblem● class0:background(QCD)● class1:signal(top)

● Wecanusejetconstituentsasinputs● Wemustbuildagoodarchitecture:

● capturetheimportantdetails● notovercomplicated(reasonable

trainingtimes)● abletogeneralize(nooverfitting)● goodperformances(ROCcurve)

background

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FullyConnectedNeuralNetworks● Verygenericstructures,thatcan

beappliedinmanydifferentclassificationproblems

● Excellentasastartingpoint● Sometimestheyprovide(too)

manyweights● Theycanbequiteinefficient

https://arxiv.org/pdf/1704.02124.pdf

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TopTagging_1:jetconstituents● Youwillusethe4-momentaoftheparticlesclusteredintojetsasinput

featuresofyournetwork● E,px,py,pzof200jetconstituentsarestoredinpandasDataFrames● Constituentsaresortedbytheirtransversemomentum(thefist

constituentsisthemostenergetic)● Aflag(1fortopevents,0forbackground)iskeptforeachjet.Itiscalled

“is_signal_new”● Thestartingpointisafullyconnectedarchitecturebutyoucantry

somethingelse

● Youwillbeguidedtounderstandthedatacontent,toevaluateperformancesandtounderstandthemeaningofaROCcurve

● Youwillfindsomehintstoimproveyourresults

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ConvolutionalNeuralNetworks● Usedintechnologyforimage

recognition● Basicidea:filtersreducethesize

oftheinputimage,“summarizing”theimportantfeaturesofapicture

● Networklearnstheelementsofthefilters

● Filtersoperateasmatricesmultiplications

● Designedtodetectedgesorparticularpatterns

● Firstweneedto“transform”jetsintoimages!

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TopTagging_2:jetimages(I)● ShapeofCMSdetector→acylinder● Thecylindricalsurfacecanbeunrolledalong

theradialandthelongitudinalcoordinates● Thissurface,thatwillbearectangle,can

thenbedividedinto"pixels".● Theparticleenergydepositscanbe

convertedinto"colourintensities"withineachpixel

● Themoredenseandthemoreenergetictheparticles,themorecolordensityinoneparticularpixel

● Wewillworkinb&whttps://arxiv.org/abs/1612.01551

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TopTagging_2:jetimages(II)https://arxiv.org/abs/1612.01551

● Theenergydepositsofthejetsconstituentsaretransformedinto"intensities"ofa2Dblackandwhiteimage

● Imagerecognitionalgorithmscanbeappliedtoahigh-energyphysicsproblem!

signal

background

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TopTagging_2:jetimages(III)

● Howdoesonejetimagelooklike?

● Theyarerathersparse

● Canyoutellwhichoneissignalandwhichoneisbackground?

● …noteasy!

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TopTagging_2:jetimages(III)● The4-momentaoftheparticlesclusteredintojetsaretransformedinto

40x40pixelatedimages● Thecontentofthese1600pixelsarestoredascolumnsinapandas

DataFrame● Aflag(1fortopevents,0forbackground)iskeptforeachjet.Itiscalled

“is_signal_new”● Thistimeyouwillbeusingconvolutionalneuralnetworksandmore

advancedconcepts(suchaspooling)

● Youwillbeguidedtounderstandandvisualizethejetimages,toevaluateperformancesandtounderstandthemeaningofaROCcurve

● Youwillfindsomehintstoimproveyourresults

● Exercisesareprovidedinjupyternotebooks● TheenvironmentissetintoAmazonWebServices(Chinaversion–expect

differencesinEU,USA,Japan,Korea,…)● Weprovidealargedatasamplefortrainingandtestingyournetwork● WewilluseKerasandTensorflowmachinelearninglibraries

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Instructions(I)

● Savethepem-key(hkwas.pem)youreceivedviamailandtakenoteofthemachinename

● Onyourcomputer:chmod 400 hkwas.pem ssh -S tmp -i hkwas.pem ec2-user@AWS_MACHINE_NAME.amazonaws.com -L localhost:1087:localhost:8888

● OnAWS(AmazonWebService)cd exercise jupyter notebook

● Youwillgetalinktocopyandpasteinyourbrowserforaccessingthenotebook(youmightneedtomodifythelocalhostnumber)

● AWSaretemporarymachines.Everythingwilldisappearattheendoftheexercise.scpeverythingyouwanttokeeptoasafeplace!

● Windowsuser?Seebackupslides

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Instructions(II)

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Scoringperformances● Performancemeasurementforbinary

classification:receiveroperatingcharacteristiccurve,orROCcurve

● Itcompareshowoftenthenetworkpredictsasignaloutcome,whentheinputissignal(truepositiverate)vshowoftenthenetworkpredictsasignaloutcome,whentheinputisbackground(falsepositiverate)

● Thehighertheareaunderroccurve(AUC),thebettertheperformanceoftheclassifier

Model2isdoingbetter!

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Publicdatasetandtopscores

● Datausedintheseexercisesarepublicandavailablehere:https://goo.gl/XGYju3

● Theyarecurrentlyusedtocomparedifferenttoptaggersresult→youareplayingwitharealMLproblem!

● IfyougetanAUClargerthan0.98,pleaseletusknow!Youdeserveapublication! https://arxiv.org/pdf/1902.09914.pdf

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Challengerules● Youcanparticipateasasingleparticipantorasateam● ThewinneristheonescoringthebestAUCinthechallengetestsample● Inthenotebooks,youwillfindsomelinesofcodeforpreparinganoutputzipfile,

containingyourmodelandtheweightsyouobtainedoutofyourtraining

● Chooseameaningfulnameforyourresultzipfile(i.e.yourname,oryourteamname)

● Downloadthezipfileanduploadithere:https://desycloud.desy.de/index.php/s/n38qi4eGdgKWLTQ

● Youcansubmitmultipleresults,payingattentiontonamethemaccordingly(addtheversionnumber,suchasv1,v34,etc.)

● YoucanusebothTopTagging_1orTopTagging_2asastartingpoint(trainoverconstituentsoroverimages)

● Wewillconsideryourbestresultforthefinalscore

● Thewinner(s)willbeaskedtopresenthis/herarchitecture

Deadlineforsubmission:todayat17.00!

● Themostimportantrules:

Don’tbeafraidtoaskquestions!Learnasmuchasyoucan!

Havefun!

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Challengerules

Backupslides

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Unixsettings

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Windowssettings