beam loss plane recognition - indico-fnal (indico)

BeamlossplanerecognitionfortheLHC

GianlucaValentinoUniversityofMalta,Msida,Malta

BelenSalvachuaCERN,Geneva,Switzerland

ThanksalsototheLHCcollimationteamforsupportinmeasurementsandanalysis

ICFAMini-Workshop:MachineLearningApplicationsforParticleAcceleratorsSLAC,MenloPark,CA,USA,1st March2018

Outline

• Backgroundandmotivation:LHCcollimationandlossmaps

• Availabledatasets

• Featureselection

• ClassificationresultsusingNNandGBC

• Conclusions

1BeamlossplanerecognitionfortheLHCGianlucaValentino

Background:LHCcollimation• TheLHCisequippedwithamulti-stagecollimationsystemtoprotectitfromnormalandabnormalbeamlosses.• Normallosses:ensurethatprotonleakagetosuperconductingmagnetsisminimal,preventingquenches

• Abnormallosses:protectionagainstfastfailurescenariossuchasasynchronousbeamdump

• Thecollimationsystemcleansparticleswithlargebetatron andoff-momentumoffsets


TCL.

5R5

TCL.5L1 TCL.

6R1

TCL.

5R1

TCL.

4R1

TCT

PV.4

L2

T

DI.4

L2

TCTP

H.4L

2

TCTPH.4R2

TCTPV.4R2

TCLIB.6R2

TCLIA.4R2

IP1

IP2

IP3

IP4

IP5

IP6

IP7

IP8

TCTPV.4L5

TCTPH

.4L5 TCDQA.A4R6

TCDQA.B4R6

TCSP.A4R6

TCSG.5L3 TCP.6L3

TCTPH.4L8 TCTPV.4L8

TCTPH

.4L1

TCTPV.4L1 TC

TPH

.4R

1 TC

TPV.

4R1

TCTPV.4R8

TDI.4R8

TCTPH.4R8

TCLIB.6L8 TCLIA.4L8

TCSP.A4L6

TCDQA.B4L6

TCDQA.A4L6

TCTP

V.4R

5 TC

TPH

.4R

5

TCSG.A4R7 TCSG.B5R7 TCSG.D5R7 TCSG.E5R7 TCSG.6R7 TCLA.A6R7 TCLA.B6R7 TCLA.C6R7 TCLA.D6R7 TCLA.A7R7

TCL.4L5

TCL.5L5

TCL.6L5

TCP.6R3 TCSG.5R3

TCSG.4L3 TCSG.A5L3 TCSG.B5L3 TCLA.A5L3 TCLA.B5L3 TCLA.6L3 TCLA.7L3

TCLA.7R3 TCLA.6R3 TCLA.B5R3 TCLA.A5R3 TCSG.B5R3 TCSG.A5R3 TCSG.4R3

TCSG.A4R7 TCSG.B4R7 TCSG.D4R7 TCSG.A5R7 TCSG.B5R7 TCSG.A6R7 TCP.B6R7 TCP.C6R7 TCP.D6R7

B1 B2

TCL.

4R5

TCL.

5R5

TCL.

6R5

TCL.6L1

TCL.5L1

TCL.4L1

CMS

ATLAS

ALICE LHC-b

TCLA.A7L7 TCLA.D6L7 TCLA.C6L7 TCLA.B6L7 TCLA.A6L7 TCSG.6L7 TCSG.E5L7 TCSG.D5L7 TCSG.B5L7 TCSG.A4L7

TCP.D6L7 TCP.C6L7 TCP.B6L7 TCSG.A6L7 TCSG.B5L7 TCSG.A5L7 TCSG.D4L7 TCSG.B4L7 TCSG.A4L7 Momentum

cleaning Betatron cleaning

Background:LHCcollimation


Adouble-sidedLHCcollimator(canbeinstalledtocleaninthehorizontal,verticalorskewplanes)

Multi-stagehalocleaningprocess

Primarycollimator(TCP) Secondarycollimator(TCSG)

Absorber(TCLA) Tertiarycollimator(TCTP)

Background:beamlosses

• Inordertomonitorbeamlosses,~3600BeamLossMonitoring(BLM)ionizationchambersareplacedaroundtheLHC.


Ionizationchambers

Background:beamlossmaps• Thebetatron cleaningsystemisqualifiedbyintentionallycreatinghighlossesinthehorizontal(H)orvertical(V)planesusingthetransversedamper.


• Cold=lossesinsuperconductingmagnets(arcs)• Warm=lossesinnormalmagnets/IRs• Collimator=lossesatBLMatcollimator

• Lossmapsaregeneratedduringtestfillswithlowintensity(fewbunches).

• Withthecollimationsystemproperlysetup,weexpecthighestlossesatthebottleneckinIR7.

~3600BLMreadingsaroundthering

IR3IR7

Background:beamlossmaps• Wecanblow-upindividualbunchesinagivenbeam&plane:


• AzoomintoIR7 givesabetterviewofthehierarchy:

Problemdefinition&motivation

• Theobjectiveistobeabletoautomaticallyclassifybetweenthefourtypesoflossplanes:• Beam1Horizontal(B1H)• Beam1Vertical(B1V)• Beam2Horizontal(B2H)• Beam2Vertical(B2V)

• Thereforeourproblemhas4outputclasses.

• Understandingthebeamlosscharacteristicsanddynamicsduringnormaloperationiscrucialtocorrectthemandunderstandtheirlong-termimpactse.g.R2Eeffects


Problemdefinition&motivation


B1H B1V

B2H B2V

Availabledatasets

• Thedatafromthe~3600LHCBLMswereextractedeachtimethetransversedamperblow-upwasrunningduring2017.

• Thisresultedin5893lossmaps,whichwerethennarroweddownbasedon:

• Beamintensityloss:theintensitylossineachlossmapshouldbe>1E8protonstohavesufficientresolution;• Collimatorsettings:thecollimatorpositionshavetobeidenticalineachlossmap;• Visualchecks:toensureacorrecthierarchywasinplace.


Availabledatasets

10BeamlossplanerecognitionfortheLHC

Beam&Plane Injection Top EnergyB1H 84 496B1V 132 599B2H 127 383B2V 123 129

GianlucaValentino

• Asthebeamparameters,collimatorsettingsetc aredifferentbetweeninjectionandflattop,separatemodelsweretrainedforbothcases:

Featureselection

• Twofeaturesetswereconsidered:

1. AlltheBLMsintheIR7longitudinalpositionrange(19400– 20600m):261BLMs.2. OnlytheBLMslocatedatcollimatorsinthesamelongitudinalpositionrange:41BLMs.

• TheBLMreadingsineachlossmapwerenormalizedtothesameBLM(TCP.A6L7.B1@19796m),whichgenerallygivesthehighestreadingsforB1lossmaps.


TrainingProcedure• Twoseparatemodelsweretrainedforthelossmaps:

• atinjectionenergy(450GeV)• attopenergy(6.5TeV)

• Lossmapsineachofthefourdatasets(B1H,B1V,B2H,B2V)weresplitusinga50:50ratiobetweentrainingandtestingdatasets.

• Theallocationofaparticularlossmaptothetrainingortestingdatasetswasdonerandomly.

• Themodelswerethenusetopredictlabelsfortheasyetunseentestingdataset,whichwerecomparedtotheoriginalgroundtruth.

• Thefinalclassificationsuccessratewascalculatedbyaveragingthepredictionperformanceonthetestingdatasetover5tries(test+train)toavoidaluckysplit.


ResultswithaNeuralNetwork


Beam&Plane Injection TopEnergy

B1H 98.1% 99.8%

B1V 99.7% 99.9%

B2H 96.9% 98.5%

B2V 97.7% 96.9%


B1H 76.6% 99.4%

B1V 91.8% 98.5%

B2H 96.8% 99.5%

B2V 96.5% 96.6%

UsingonlythecollimatorBLMs

UsingallIR7BLMs

GianlucaValentino

4hiddenlayers:(300,130,75,30)

ResultswithGradientBoostingClassifier



B1H 97.6% 99.0%

B1V 98.2% 99.5%

B2H 95.3% 99.5%

B2V 97.1% 96.3%


B1H 97.6% 100%

B1V 99.1% 99.2%

B2H 95.9% 99.1%

B2V 96.5% 96.2%

UsingonlythecollimatorBLMs

UsingallIR7BLMs

GianlucaValentino

n_estimators =1000,max_depth =20

ClassificationoflossesduringLHCoperation

• Lossmapsareperformedincontrolledconditions(beamandplaneisknown),andMLmodelsweretrainedonthesedata.

• Nextstep:applyingthemodelstrainedonlossmapdatatoactuallossesinoperation.

• Twoscenariosconsidered:• Long-RangeBeam-Beam(LRBB)beamtest• LossesduringthestandardLHCmachinecycle

GianlucaValentino BeamlossplanerecognitionfortheLHC 15

Existingbeamlossdecompositionmethod

• BasedonSVD(seeM.Wyszynski,CERNsummerstudentproject&B.Salvachua etal.,“DecompositionofbeamlossesatLHC”,IPAC’17).

• Worksforbothoff-momentumandbetatron losses.

• Usesacalibrationfactor(obtainedthroughdedicatedcollimatorscrapingmeasurements)toconvertBLMreadingsinGy/stoproton/s.

• Asubsetofonly6BLMsperbeam,atHandVcollimators,isused.ThisvectoristhendecomposedasalinearcombinationoftheindividualB1H/B1V/B2H/B2Vcontributions.

• Itisstaticandnoteasilyadaptabletonewmachineconfigurations(requiresmanualselectionofBLMs).


Long-RangeBeam-Beamtest


• Beamtestusingwiresinstalledincollimatorstocompensatetheoctupolar termofthebeam-beaminIR5.

• TherewasaninitialB2Hblow-up,followedbyadditionallossesinB2Hasthewireswereswitchedonandoff.

• TheMLalgorithmcorrectlyclassifiedthe3spikesinlosseswhichwere~1e8p.Therewasonemisclassification(thoughherethelossesinB1andB2are~equal&~1e7p).

*classificationresult

B2HB1H

B2HB2H

Lossesduringthesqueezeinstandardoperation


SVD

GBC

Futurework:changetomore“hierarchical”classification(i.e.considerthatwecanhavelossessimultaneouslyinB1andB2)

Conclusions• MachinelearningtechniqueswereusedtotrainmodelstoclassifybetweendifferenttypesoflossplanesintheLHC.

• TheGradientBoostingClassifiergavethebestperformance.

• UsingonlythecollimatorBLMsgavesimilarorbetterresultsthanusingallBLMs.

• Futurework:• Explorecross-validationofparametersoncemoredataisavailable• Investigatedifferentfeatureselection&scalingtechniques• moresystematicteststoclassifylossesduringthestandardmachinecycle+comparisonwithSVDtechnique.


beam loss plane recognition - indico-fnal (indico)

Documents