Calibração das medições de energia em um detector de partículas utilizando informações de assimetria do perfil de deposição no calorímetro e técnicas de inteligência artificial

Abstract

The ATLASdetector(AToroidalLHCApparatuS),partoftheLargeHadronCollider(LHC)at CERN, isoneofthelargestandmostcomplexparticlephysicsexperimentseverbuilt.Itiscomposed of severalsubsystems,includingthecalorimetrysystem,whichconsistsoffinelysegmenteddetectors responsible formeasuringtheenergyandpositionofparticles.ATLASwasdesignedtodetectandclassify subatomic particlesproducedinhigh-energycollisions.Oneofthechallengesfacedintheexperiment is thecalibrationoftheenergyofthedetectedparticles,whichisessentialtoensuretheaccuracyof the analysesperformed.ThisstudyinvestigatedtheenergycalibrationoftheATLAScalorimeterusing regressorsbasedonGradientBoostedDecisionTrees(GBDT)andfeedforwardneuralnetworksofthe Multilayer Perceptron(MLP)type,whichreceivedasinputstructurescalledStandardRingsandQuarter Rings. Thesestructuresarebuiltfromcalorimeterinformationandorganizedinawaythatpreserves the spatialcharacteristicsofparticleshowers.Fromthedefinitionofaregionofinterestaroundthe particle’sinteractionpoint,thesignalsfromthesensorsarearrangedinconcentricrings.TheStandard Rings encodetheenergyofthecellscontainedineachringforeachcalorimeterlayer,whiletheQuarter Rings areobtainedbydividingeachStandardRingintofourparts—exceptforthefirstring(HotCell) — allowingthecaptureofasymmetriesintheenergydepositionprofile.Oneofthemainchallenges in calorimetercalibrationisdealingwithvariationsinthedetectorresponse,whichcanbesignificant depending ontheparticles’transverseenergy(ET ) andpseudorapidity(η), ageometricparameterthat describes theparticle’spositionrelativetothebeamaxis.Thesevariationsbecomeparticularlycritical at lowenergyvalues,whereelectronidentificationismoredifficultduetothecharacteristicenergy deposition profile.Thecalibrationfactor(α), definedastheratiobetweentheMonteCarlosimulated energyandtheenergyestimatedbytheHighLevelTrigger(HLT),isappliedtotherawenergyprovided by theFastCaloalgorithminordertocorrectdistortionsresultingfromenergylossesinthedetector. Another aspectconsideredinthisstudyistheimpactofpile-up—multiplesimultaneousinteractionsina single collision—whichaffectsthepreciseidentificationandreconstructionofparticles.Therefore,the analysis wasconductedunderdifferentscenarios,withandwithoutthepresenceofpile-up,andusing differentinputstrategies:rawdataanddataderivedfromtheextractionofenergeticandasymmetric variables.Theresultsindicatethatalthoughpile-uppartiallyreducescalibrationeffectiveness—especially in energy-basedestimates—thetestedmodelsmaintainedrobustperformance.TheQuarterRingsstood out byshowingmoresignificantgainsincertainpseudorapidityregions,whiletheapplicationoffeature extractioncontributedtoimprovingtheestimates,althoughsomeconfigurationsprovedmoresensitiveto complexexperimentalenvironments.TheuseofQuarterRingsledtoimprovementsofupto18.08%in pseudorapidity-based calibrationandupto15.2%inenergy-basedcalibration,showingaclearadvantage overtheuncalibratedscenarioandperformancesimilartothatobtainedwithStandardRings.Theseresults reinforce thefeasibilityofusingspatiallyrefinedstructures,suchastheQuarterRings,combinedwith machine learningmodelstoimproveATLAScalorimetercalibration,evenunderadverseconditionssuch as thepresenceofpile-up.Theproposedapproachoffersapromisingalternativeforfutureimprovements in fastreconstructionsystemsandeventanalysisintheexperiment.