Beyond2Drepresenta/ons:track/showersepara/onin3D

JiWonParkKazuTerao

11/14/17

SLACNa/onalAcceleratorLaboratory1

INTRODUCTION

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Mo/va/onsandgoals

Goalforthenext~18minutes:reporttheresultsoftrainingaseman/csegmenta/onnetworktoperformtrack/showersepara/onon3Dsimula/ondata,asaworkingtestcaseforpaSernrecogni/onin3D.

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Whyin3D?•  Lessop/calillusionininterpre/ng3Ddata.•  PIDin2Dandtrack/showersepara/onin2Dhavebeendone

forMicroBooNEdata.PaSernrecogni/onin3Disanaturalextensionfrom2D.

Long-termmission:buildafull3Dreconstruc/onchainforLArTPCdatausingdeeplearning.

Imageclassifica3ontaskin2DFive-par/clePIDhasbeendone:givena2Dimageofasingle

par/cle,labelitasagammaray,electron,muon,pion,orproton.

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pion muon

FromtheMicroBooNECNNpaper(2016)

gaveahappyscoredistribu3on.*Muonclassifica/onscore~highlikelythealgorithmthinksanimageisamuon.Assignedhighscoresformuonsvs.lowscoresforpionsàconfidenceinpredic/onJ

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5-par/clePIDin3Disanaturalextension(achievedsimilarresultsas2D)

pion muon

Voxel=the3Dequivalentofpixel

METHODS

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Ourstudy:shower/tracksepara3on.(Now3classesfortrack,shower,backgroundinsteadof5par/cleclasses)Ithasbeendonepixel-levelin2Dusingseman/csegmenta/on.

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Truthlabel Predic/on

FromMicroBooNEDNNpaperunderreview

Yellow:track,Cyan:shower

Ourstudy:shower/tracksepara3on.(Now3classesfortrack,shower,backgroundinsteadof5par/cleclasses)Ithasbeendonepixel-levelin2Dusingseman/csegmenta/on.

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Truthlabel Predic/on

FromMicroBooNEDNNpaperunderreview

Yellow:track,Cyan:shower

Canreusethenetworktodoshower/tracksepara/onin3D.Thisstudyallowsustoexplorehowthetechniquescalesto3D.

Theseman/csegmenta/onnetwork(SSNet)

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Outpu

tImage

Inpu

tImage Down-sampling Up-sampling

Featuretensor

Intermediate,low-resolu/onfeaturemap

TwocomponentsofSSNet

2.Upsamplingpath1.Downsamplingpath

ThankyouKazuforthediagramJ

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Down-sampling

Featuretensor

1.Downsamplingpath

Role:classifica3onAseriesofconvolu/onsanddownsamplingwhichreducetheinputimagedowntothelowest-resolu/onfeaturemap.Eachdownsamplingstepincreasesfieldofviewofthefeaturemapandallowsittounderstandtherela/onshipbetweenneighboringpixels.

“WriKentexts”inputimage

“Humanface”inputimage

“WriKentexts”featuremap

“Humanface”featuremap

ThankyouagainKazuforthediagramJ

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Up-sampling

Featuretensor

2.UpsamplingpathRole:pixel-wiselabeling~reverseversionofdownsamplingpath.Aseriesofconvolu/ons-transpose,convolu/ons,andupsamplingwhichretrievetheoriginalresolu/onoftheimage,witheachpixellabeledasoneoftheclasses.

SegmentedoutputimageEachpixeliseither“human”or“background”

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ThetypeofSSNetweused:U-ResNet1.Dow

nsam

plingpath

2.Upsam

plingpath

FeaturetensorFromMicroBooNEDNNpaperunderreview

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ThetypeofSSNetweused:U-ResNet=U-Net+ResNet

FromMicroBooNEDNNpaperunderreview

OneResNetmodule:

WithintheU-Netarchitecture,useResNetmodules.InU-ResNet,theconvolu/onsareembeddedwithinResNetmodules.

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ThetypeofSSNetweused:U-ResNet=U-Net+ResNet

FromMicroBooNEDNNpaperunderreview

Concatena/ons:afeatureofU-Net.Westackthefeaturemapsateachdownsamplingstagewithsame-sizefeaturemapsattheupsamplingstage.~“shortcut”opera/onstostrengthencorrela/onbetweenthelow-leveldetailsandhigh-levelcontextualinforma/on.

Genera/ngimagesforourtrainingset

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•  3D(voxelized)•  Eachevent(image)generated

fromtruthenergydeposi/onfromLArSoi.With:–  Randomizedpar/cle

mul/plicity1~4fromauniquevertexperevent,wherethe1~4par/clesarechosenrandomlyfrom5par/cleclasses.

–  Momentumvaryingfrom100MeVto1GeVinisotropicdirec/on.

–  128x128x128voxelsà1cm^3pervoxel(forquickfirsttrial)

Proton300MeV

Proton360MeV

Electron240MeV

Pion220MeV

Inputimage:eachvoxelcontainschargeinfo.

Supervisedlearning:eachtrainingexampleisanorderedpairofinputimageandtrueoutput

image(label).

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Yellow:track,Cyan:shower

Labelimage:eachvoxelis0(background),1(shower),or2(track).

MustweightthesoWmaxcrossentropy.Typically,animagehas99.99%background(zero-value)voxels.Evenamongnon-zerovoxels,canhaveunevennumberoftrackvoxelsvs.showervoxels.Soupweightthe“rarer”classesintheimage,e.g.ifthetruthlabelhasra/oofBG:track:shower=99:0.7:0.3,incen/vizethealgorithmtodofocusonshowermostandBGleastbyusinginversesasweights,1/99:1/0.7:1/0.3.

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Definingtheop/miza/onobjec/ve(lossfunc/on)

Similarly,monitoralgorithm’sperformancebyevalua/ngaccuracyonlyfornon-zeropixels

Training

Op/mizer:AdamChoosebatchsizetobe8images•  batchsize~sizeofensemble,sobiggerthebeSerBUT

limitedbyGPUmemory•  oneitera/onconsumed8images

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RESULTS

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Non-zeropixelaccuracycurve

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Itera/ons

Non-zeropixelaccuracy=correctlypredictednonzeropixels/totalnonzeropixelsEachitera/onconsumed8images.Lightorange:rawplotDarkorange:smoothedplot

Losscurve

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Itera/onsEachitera/onconsumed8images.Lightorange:rawplotDarkorange:smoothedplot

Truthlabel Predic3on

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Truthlabel Predic3on

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Truthlabel Predic3on

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Summaryandfuturework

WehavetrainedU-Resnettoperformshower/tracksepara/onon3Dsimula/ondataandreportatrainingaccuracyof~96%.

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Todo:•  Exploresmallervoxelsizesforhigherprecision•  Vertexfinding(adding1moreclasstotheclassifica/ontask)•  Par/cleclustering(insteadofpixel-level,instance-awareclassifica/on)

BACKUPSLIDES

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Overallaccuracycurve

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Itera/ons

WhyResNet?

ThispaperdemonstrateswhyResNetissuperiortovgg,etc.inseman/csegmenta/on.

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