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(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