A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in theMicroBooNE Liquid Argon Time Projection Chamber

C. Adams,31 M. Alrashed,11 R. An,10 J. Anthony,3 J. Asaadi,27 A. Ashkenazi,15 M. Auger,1 S. Balasubramanian,31

B. Baller,8 C. Barnes,16 G. Barr,19 M. Bass,2 F. Bay,28 A. Bhat,24 K. Bhattacharya,20 M. Bishai,2

A. Blake,12 T. Bolton,11 L. Camilleri,7 D. Caratelli,7, 8 I. Caro Terrazas,6 R. Carr,15 R. Castillo Fernandez,8

F. Cavanna,8 G. Cerati,8 Y. Chen,1 E. Church,20 D. Cianci,7 E. Cohen,25 G. H. Collin,15 J. M. Conrad,15

M. Convery,23 L. Cooper-Troendle,31 J. I. Crespo-Anadon,7 M. Del Tutto,19 D. Devitt,12 A. Diaz,15 K.

Duffy,8 S. Dytman,21 B. Eberly,23 A. Ereditato,1 L. Escudero Sanchez,3 J. Esquivel,24 J. J Evans,14

A. A. Fadeeva,7 R. S. Fitzpatrick,16 B. T. Fleming,31 D. Franco,31 A. P. Furmanski,14 D. Garcia-Gamez,14

G. T. Garvey,13 V. Genty,7 D. Goeldi,1 S. Gollapinni,26 O. Goodwin,14 E. Gramellini,31 H. Greenlee,8

R. Grosso,5 R. Guenette,9 P. Guzowski,14 A. Hackenburg,31 P. Hamilton,24 O. Hen,15 J. Hewes,14

C. Hill,14 G. A. Horton-Smith,11 A. Hourlier,15 E.-C. Huang,13 C. James,8 J. Jan de Vries,3 L. Jiang,21

R. A. Johnson,5 J. Joshi,2 H. Jostlein,8 Y.-J. Jwa,7 G. Karagiorgi,7 W. Ketchum,8 B. Kirby,2 M. Kirby,8

T. Kobilarcik,8 I. Kreslo,1 Y. Li,2 A. Lister,12 B. R. Littlejohn,10 S. Lockwitz,8 D. Lorca,1 W. C. Louis,13

M. Luethi,1 B. Lundberg,8 X. Luo,31 A. Marchionni,8 S. Marcocci,8 C. Mariani,30 J. Marshall,3 J. Martin-Albo,9

D. A. Martinez Caicedo,10 A. Mastbaum,4 V. Meddage,11 T. Mettler,1 G. B. Mills,13 K. Mistry,14 A. Mogan,26

J. Moon,15 M. Mooney,6 C. D. Moore,8 J. Mousseau,16 M. Murphy,30 R. Murrells,14 D. Naples,21

P. Nienaber,22 J. Nowak,12 O. Palamara,8 V. Pandey,30 V. Paolone,21 A. Papadopoulou,15 V. Papavassiliou,17

S. F. Pate,17 Z. Pavlovic,8 E. Piasetzky,25 D. Porzio,14 G. Pulliam,24 X. Qian,2 J. L. Raaf,8 A. Rafique,11

L. Rochester,23 M. Ross-Lonergan,7 C. Rudolf von Rohr,1 B. Russell,31 D. W. Schmitz,4 A. Schukraft,8

W. Seligman,7 M. H. Shaevitz,7 R. Sharankova,29 J. Sinclair,1 A. Smith,3 E. L. Snider,8 M. Soderberg,24

S. Soldner-Rembold,14 S. R. Soleti,19, 9 P. Spentzouris,8 J. Spitz,16 J. St. John,8 T. Strauss,8 K. Sutton,7

S. Sword-Fehlberg,17 A. M. Szelc,14 N. Tagg,18 W. Tang,26 K. Terao,23 M. Thomson,3 R. T. Thornton,13

M. Toups,8 Y.-T. Tsai,23 S. Tufanli,31 T. Usher,23 W. Van De Pontseele,19, 9 R. G. Van de Water,13 B. Viren,2

M. Weber,1 H. Wei,2 D. A. Wickremasinghe,21 K. Wierman,20 Z. Williams,27 S. Wolbers,8 T. Wongjirad,29

K. Woodruff,17 T. Yang,8 G. Yarbrough,26 L. E. Yates,15 G. P. Zeller,8 J. Zennamo,4, 8 and C. Zhang2

(The MicroBooNE Collaboration)1Universitat Bern, Bern CH-3012, Switzerland

2Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA3University of Cambridge, Cambridge CB3 0HE, United Kingdom

4University of Chicago, Chicago, IL, 60637, USA5University of Cincinnati, Cincinnati, OH, 45221, USA

6Colorado State University, Fort Collins, CO, 80523, USA7Columbia University, New York, NY, 10027, USA

8Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA9Harvard University, Cambridge, MA 02138, USA

10Illinois Institute of Technology (IIT), Chicago, IL 60616, USA11Kansas State University (KSU), Manhattan, KS, 66506, USA12Lancaster University, Lancaster LA1 4YW, United Kingdom

13Los Alamos National Laboratory (LANL), Los Alamos, NM, 87545, USA14The University of Manchester, Manchester M13 9PL, United Kingdom

15Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA16University of Michigan, Ann Arbor, MI, 48109, USA

17New Mexico State University (NMSU), Las Cruces, NM, 88003, USA18Otterbein University, Westerville, OH, 43081, USA

19University of Oxford, Oxford OX1 3RH, United Kingdom20Pacific Northwest National Laboratory (PNNL), Richland, WA, 99352, USA

21University of Pittsburgh, Pittsburgh, PA, 15260, USA22Saint Mary’s University of Minnesota, Winona, MN, 55987, USA

23SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA24Syracuse University, Syracuse, NY, 13244, USA

25Tel Aviv University, Tel Aviv, Israel, 6997826University of Tennessee, Knoxville, TN, 37996, USA

27University of Texas, Arlington, TX, 76019, USA28TUBITAK Space Technologies Research Institute, METU Campus, TR-06800, Ankara, Turkey

29Tufts University, Medford, MA, 02155, USA30Center for Neutrino Physics, Virginia Tech, Blacksburg, VA, 24061, USA

arX

iv:1

808.

0726

9v1

[he

p-ex

] 2

2 A

ug 2

018

2

31Yale University, New Haven, CT, 06520, USA

We have developed a convolutional neural network (CNN) that can make a pixel-level predictionof objects in image data recorded by a liquid argon time projection chamber (LArTPC) for thefirst time. We describe the network design, training techniques, and software tools developed totrain this network. The goal of this work is to develop a complete deep neural network based datareconstruction chain for the MicroBooNE detector. We show the first demonstration of a network’svalidity on real LArTPC data using MicroBooNE collection plane images. The demonstration isperformed for stopping muon and a νµ charged current neutral pion data samples.

I. INTRODUCTION

LArTPCs are capable of producing high-resolution im-ages of particle interactions. This is one of the main rea-sons LArTPCs are the technology of choice for severalcurrent and future neutrino research programs includingthe Short Baseline Program [1] (SBN) and Deep Under-ground Neutrino Experiment [2] (DUNE). The Micro-BooNE experiment [3], as a part of the SBN program,studies the νe-like low energy event excess observed bythe MiniBooNE collaboration [4] using a LArTPC detec-tor and the on-axis Booster Neutrino Beamline [5] (BNB)at Fermilab as the νµ source.

A LArTPC consists of a homogeneous volume of liquidargon bounded by a cathode plane and an anode plane.When a charged particle traverses the sensitive region,ionization electrons and scintillation light are producedalong its trajectory. The scintillation light is detectedwithin several nano-seconds by an array of photomul-tiplier tubes (PMTs), providing a timing measurement.Ionization electrons take a few milliseconds to drift to-ward the anode under the applied electric field and aredetected by the anode plane, which is equipped with a setof wire planes and charge-sensitive readout circuits. Thespatial separation within the wires on the anode planedetermines the spatial resolution of the recorded two-dimensional (2D) projection images. The speed of driftelectrons along with their longitudinal diffusion, as wellas the electronics response and the sampling frequencyof the signal waveform determine the spatial resolutionalong the drift direction.

While the detector is able to capture many of the finedetails that will be useful for physics analysis, parsingthese details using automated reconstruction tools is stilla technical challenge. One particular challenge is thediscrimination between electromagnetic (EM) particles,namely e−, e+, and photons, and other particle types.EM particles above the critical energy (≈ 33 MeV in ar-gon) initiate an electromagnetic cascade of particles anddevelop a unique topology that consists of a collectionof branching features. Identifying this topology is a sim-ple form of particle identification, and is key informa-tion that can be used to discriminate between νe andνµ interactions, as shown in Figure 1. For EM parti-cles below the critical energy, other unique features areavailable. One example is low energy electrons (δ rays)knocked off from argon atoms by a muon traversing thedetector. These add multiple short branches to a muon

FIG. 1. Two examples of νe (a) and νµ (b) events simulatedin the MicroBooNE detector. The pixel color represents theamount of energy deposited per pixel in the 2D projectionimage. The pattern of ionization differs significantly betweenνe and νµ.

trajectory. Separating them from a muon helps to iden-tify the trunk of a muon trajectory. Another example is aMichel electron trajectory with low energy deposition perunit length (dE/dx) from the decay of a stopping muon.That trajectory typically exhibits a pattern of increasingdE/dx due to an increase in ionization energy-loss as itcomes to rest. Identifying and cleanly separating energydepositions from Michel electrons in the muon decay al-lows one to reconstruct the Michel spectrum with highaccuracy. This sample proves to be a valuable energycalibration source.

In this paper, we demonstrate that EM particles can bediscriminated from other particles at the pixel-level in animage using a class of machine learning algorithms knownas convolutional neural networks (CNNs). We refer toEM particles as shower and the others as track particlesin the rest of the paper. Our method uses noise-filtered,signal shape deconvolved waveforms [6] from the charge-collection plane organized in 2D image format prior toa pixel clustering analysis. Having a prediction of eachpixel as track or shower type prior to a pixel clusteringreconstruction stage simplifies the downstream pattern-recognition algorithms and reduces the need for iterativereconstruction processes. We use a class of CNNs calledsemantic segmentation networks (SSNets) to classify eachpixel of an image into a predefined set of semantics in-cluding a track, a shower, and a background pixel.

This work was performed as a step towards the de-velopment of a complete LArTPC event reconstructionand analysis chain using deep neural networks. It is anextension of an earlier study in which we demonstratedthe use of image classification and object detection tech-

3

FIG. 2. The MicroBooNE detector schematics showing three wire planes and example 2D projections of V and Y planewaveforms.

niques with CNNs for LArTPC data analysis [7]. Thenew contributions in this work include:

• First demonstration of a pixel-level object predic-tion for LArTPC event reconstruction using a deepneural network.

• First demonstration of a deep neural network onreal LArTPC detector data.

• Software tools and algorithms capable of generat-ing a pixel-level label (i.e., semantics) from LAr-Soft [8] data for supervised training and analysisof real detector data.

The software is open-source and accessible at Ref. [14].The deep neural network described here is currently

employed in the data reconstruction chain for the deep-learning based analysis of the MiniBooNE low-energy ex-cess by MicroBooNE. The analysis goal is to locate aneutrino interaction vertex and identify the interactiontype as being νe or νµ for low energy neutrinos (≈ 200 to600 MeV). Having track/shower separation informationis crucial for vertex reconstruction and particle clusteringalgorithms.

High rate environments, as in LArTPCs like the ShortBaseline Near Detector (SBND) and the DUNE near de-tector, both to be built in the not-too-distant future at

Fermilab, will benefit from sophisticated computer vi-sion techniques. Such techniques, including pixel label-ing as provided by the semantic segmentation approachdescribed here, will be very useful to untangle neutrino-induced tracks and showers.

II. MICROBOONE DETECTOR ANDPARTICLE IMAGES

The MicroBooNE LArTPC contains 85 tonnes of liq-uid argon in the active region, which is defined by a rect-angular shape with the dimensions 10.36 m in length,2.32 m in height, and 2.56 m in width along the driftdirection [3], as shown in Figure 2. The anode consists ofthree planes of parallel wires. The first and second planescontain 2,400 wires with orientations of +60 and -60 de-grees from the vertical, respectively. Ionization electronsproduce bipolar signals on the two induction planes asthey pass through them. The third is called the collec-tion plane and consists of 3,456 vertical wires. Wires onthe third plane are held at a positive potential and col-lect ionization electrons. Wires are separated by 3 mmpitch in all planes, and signal waveforms are digitized ata 2 MHz sampling rate and recorded for a duration of

4

FIG. 3. U-ResNet architecture diagram. Black arrows describe the direction of tensor data flow. Red arrows indicate concate-nation operations to combine the output of convolution layers from the encoding path to the decoding path. The final outputhas the same spatial dimension as the input with a depth of three, representing the background, track and shower probabilityof each pixel.

4.8 ms in each event. Combined wire waveforms, alignedby the digitization time, form 2D projected images of athree-dimensional (3D) particle trajectory from a differ-ent projection angle. The digitization time runs alongthe vertical axis and the wires run along the horizontalaxis in event displays shown in this paper (e.g. Figure 1).

In this paper we focus on the analysis of image datarecorded by the collection plane, which has a size of 3,456by 9,600 pixels. The spatial resolution of an image alongthe wire axis is 3 mm per pixel. For the analysis, every6 samples of a digitized waveform are summed together,corresponding to an approximate spatial resolution alongthe time axis of 3.3 mm. The resulting image dimensionis 3,456 by 1,600 pixels.

III. U-RESNET: TRACK/SHOWERPIXEL-LEVEL SEPARATION NETWORK

In this study we use U-ResNet, a hybrid of the U-Net [9] and residual network [10] (ResNet) design pat-tern. U-ResNet takes a single-channel 512 by 512 pixelimage as input and outputs an image of the same spatialdimension with 3 channels per pixel encoding a probabil-ity from multinomial logistic regression, or softmax, for apixel being a background, track, or shower type. We useU-Net as the base SSNet architecture design because of

its excellent performance in biomedical images [9] whichresemble those from LArTPCs where information den-sity is sparse. We replace the convolution layers in theoriginal U-Net with ResNet modules. ResNet is a genericCNN design pattern that was invented at the same timeas U-Net and enables the training of deep CNNs. Inour implementation, each ResNet module consists of twoconvolution layers of 3-by-3 kernel size, where each con-volution layer is followed by a batch normalization op-eration [11] and a rectified-linear unit (ReLU) activationfunction. The schematic U-ResNet design is shown inFigure 3.

The U-ResNet architecture can be interpreted intwo separate sections. The first half of the networktakes an input image, a data tensor with a dimensionof (512,512,1), and repeatedly applies convolution anddown-sampling operations. At the end of the first sec-tion, the data tensor has a dimension of (16,16,1024).The goal of this section of the network is to learn a non-linear, hierarchical representation of image features atdifferent scales. Since feature information is encoded ina low spatial resolution tensor at the end of this sectionof the network, it is referred to as an encoding path.

The second half of the U-ResNet takes the output of anencoding path, a tensor with a dimension of (16,16,1024),and repeatedly applies an up-sampling and convolutionoperation. An up-sampling is performed by an operation

5

called convolution-transpose which is an interpolation fil-ter that expands the spatial dimension of a tensor bya factor of two. By using neurons to incorporate theinterpolation operations, the U-ResNet architecture in-troduces a learnable interpolation filter optimized for as-signing object classification to the pixels. This section ofthe network does essentially the opposite of the encodingpath, hence it is called a decoding path.

An important and unique design pattern of U-Net is anadditional path to allow the flow of information betweenthe encoding path and the decoding path. This mitigatesthe loss of spatial resolution information in the encodingpath where down-sampling is performed. The idea andmethod employed in U-Net is simple and effective; inthe decoding path where high spatial resolution needs tobe recovered, we concatenate the data tensors from theencoding path where the spatial dimension matches. Inthe encoding path, data tensors hold the best possiblespatial resolution at each spatial resolution prior to thedown-sampling operations. Thus a simple concatenationallows information about spatial resolutions to flow intothe decoding path, allowing U-Net to perform a high-precision image segmentation.

IV. TRAINING U-RESNET

We train U-ResNet via a supervised learning methodthat uses simulated particle interaction images. In thissection we describe techniques employed for the trainingand optimization methods.

A. Transfer Learning

We exploit a transfer learning technique by first train-ing the first-half of U-ResNet for an image classificationtask using the identical data set from our previous pub-lication [7]. This data set contains single particle imageswhich could be e−, photon, muon, π− or proton. Thenetwork’s weights trained to discriminate between dif-ferent particle images provide a natural initial state toperform a pixel-level track/shower separation. When wesubsequently train the whole U-ResNet with pre-trainedweights, we let all network parameters be trained andfine-tuned.

B. Class/Pixel-wise Loss Weighting

Training of the U-ResNet is a process of minimizingthe loss, a measure of an error made by the network,over many iterations. The loss is computed by summingover a pixel-wise multinomial logistic loss in each image,and then averaging over all images in a batch of images.This definition of loss presents a challenge to training U-ResNet for LArTPC images where the fraction of pixelsthat are background is 99% or more, hence dominating

the total loss. In order to mitigate this challenge, theauthors of the original U-Net paper introduced a class-wise loss (CL) weighting factor [9] which is a reciprocalfor the number of pixels that belong to each class in animage.

In this study, we introduce a pixel-wise loss (PL)weighting factor that is multiplied by a pixel’s loss con-tribution to the total loss of an image. PL weightingenables the network’s training to focus on challengingparts of an image by up-weighting a pixel loss in thecorresponding regions. For the calculation of PL weight-ing factors, we define four categories of pixels with thelast category separated into particle type instances. Thefirst category contains background (i.e., zero) pixels thatsurround non-background (i.e., non-zero) pixels within 2pixels. The second category is the rest of background pix-els in the image that do not belong to the first category.The third category represents non-zero pixels within 4pixels of the generated event vertex. Finally, the fourthcategory is defined for each particle instance and includesnon-zero pixels that belong to a particle. Therefore thetotal number of categories may vary from one event to an-other. A PL weighting factor is computed per category.It is the reciprocal of the number of pixels belonging toeach category. A category with fewer pixel counts repre-sents a rare feature in an image data, and is assigned ahigher weighting factor. Figure 4 shows how pixels aregrouped into the four categories.

C. Optimization

We use the RMSProp [12] with an initial learning rateof 0.0003 to optimize the U-ResNet. The weights are up-dated after processing every batch of 60 images. Thetraining process is monitored using the Incorrectly Clas-sified Pixel Fraction (ICPF) metric. Figure 5 shows theloss and the average values of ICPF computed over val-idation samples as a function of epoch during the train-ing. Epoch is a measure of time, and one epoch corre-sponds to the time it takes to consume the same numberof images as the whole training sample. The learningrate is lowered by an order of magnitude at epoch 14 asshown in Figure 5. We determine the best performingnetwork parameters based on the lowest ICPF value onthe validation set which is generated independently fromthe data set used for training under the same simula-tion configuration. The performance of the network isthen quantified using the test set, which is yet anotherindependent sample generated with the same simulationconfiguration. Using the trained network, we find an av-erage ICPF value of 1.9% using all events in the test set.

The U-ResNet and this training scheme is implementedusing caffe [13], customized to employ the PL weight-ing scheme [14]. We trained our network using NVIDIATitanX [15] GPUs with 12 GB memory.

6

FIG. 4. Top: an example image from the training set in whichtwo protons, one electron, and one muon are produced. Thegaps along the trajectory of an electron and proton on the leftare due to unresponsive wires [6] in the detector. Bottom: theevent from the top image that shows PL weighting categoriesindicated in different colors.

V. DATA SAMPLES FOR TRAINING ANDVALIDATION

A. Training Sample Preparation

We prepare training samples using a custom event gen-erator called MultiPartVertex (MPV), available inthe MicroBooNE software repository, uboonecode [16].MPV can be configured to randomly generate a single 3Dpoint in a detector with the emission of multiple chargedparticles. Any random process employed by MPV is auniform distribution within the specified range in theconfiguration. The multiplicity and type of particles tobe generated are configurable parameters as outlined be-low. Restrictions and ranges for the generation are pre-sented in the following two paragraphs.

For 80% of the sample, the MPV is configured to gen-erate events with a random total particle multiplicity be-tween one and four. One of the generated particles mustbe a light lepton (e− or µ−) with kinetic energy rang-ing from 50 to 1000 MeV. The direction of each particleis chosen from an isotropic distribution. For the othergenerated particles, the MPV is configured to randomlyassign their types to a photon, charged pion, proton, or

FIG. 5. (a) The training loss value as a function of train-ing time using the validation sample. The red line shows theaverage at a given Epoch computed using 200 the neighbor-ing Epoch points. (b) Incorrectly Classified Pixel Fraction(ICPF) for the same sample. The sudden drop in both fig-ures at Epoch 14 is due to lowering of the learning rate by afactor of 10.

another lepton (e− or µ−). We also set the maximummultiplicity for leptons and protons to be three and pho-tons and charged pions to be two. There is no strongmotivation for this configuration. In fact we demonstratelater in this paper that the network works well on neu-trino candidate events with a shower particle from realdetector data with multiplicity five.

The remaining 20% of images are generated with adifferent configuration. The total multiplicity is set ran-domly between one and four particles but there is norestriction to include at least one light lepton. Instead,particle types are set randomly between showers (e− andphoton) and tracks (µ−, charged pion, and proton). Foreach particle type, the maximum multiplicity is set totwo. The ranges for the randomly assigned momentumare specified as 30 to 100 MeV/c for e− and photon, 85to 175 MeV/c for µ−, 95 to 195 MeV/c for charged pion,and 300 to 450 MeV/c for proton. The distribution ofparticle directions is isotropic. This 20% fraction is cho-sen to have a particular focus on the low energy regionwhere classification of particle types becomes difficult.The motivation for this is to enhance the networks per-

7

formance in this energy region. We generate 140,000 im-ages, randomly selecting 100,000 for training, 20,000 forvalidation, and 20,000 images for testing the network’sperformance, with no image in more than one set.

After the particle generation stage, the training sam-ple is run through the detector simulation and waveformprocessing scheme of the experiment. The procedure issimilar to that of our previous study [7] but with an up-dated version of LArSoft [8] and uboonecode [16].The latter software contains important updates includ-ing a data-driven detector noise model [6], noise filteringalgorithms, and data-driven TPC charge signal deconvo-lution kernels [17, 18]. These improvements aim to reducethe potential discrepancies between data and simulationsamples. Hence, these improvements are important forthe U-ResNet trained purely on simulation to work effec-tively on real detector data. Further suppression of thediscrepancy between data and simulation for noise withlow amplitude is accomplished by setting the pixel valueto zero for pixels with amplitude below 10. Finally, wecrop the 512 by 512 pixel image from the whole collec-tion plane image which has the original size of 3,456 by1,600 pixels. The cropping algorithm (CRA) defines anaxis-aligned 3D rectangular volume within the detectorof a configurable size that contains a set of 3D points,called constraint points. The location of the 3D box isset by the algorithm under two conditions. First, thedefined box must contain all given 3D constraint points.Second, while satisfying the first condition, a range is de-fined that maximizes the number of non-zero 2D pixelsincluded in the projection of the rectangular box in thecollection plane. By satisfying these two conditions, thebox location is allowed to float freely. We use the 3Dinteraction vertex as the constraint point in this study.The resulting 512 by 512 pixel images contain the inter-action point location for each event and the maximumnumber of non-zero pixels in the projection.

B. Benchmark Simulation Samples

Separately from the testing set, we generate five addi-tional simulation samples to benchmark the performanceof U-ResNet. These simulation samples include two typesof neutrino interactions simulated using the GENIE [19]neutrino event generator within LArSoft [8] and theMPV generator events generated under three differentgenerator configurations. The image preparation stepsare identical to those of the training samples except forthe event generation step which is unique to the genera-tor type and configuration. This brings us to a total ofsix simulated samples, consisting of 120,000 events, thatwe can analyze with the trained network.

The neutrino samples consist of 20,000 νµ and 20,000νe events, generated with the Booster Neutrino Beam [5](BNB) beam flux information. Each MPV samples in-cludes 20,000 images of events. One MPV sample isconfigured to generate one proton and one electron only

(1e1p). Particles are simulated with a uniform energydistribution and isotropic momentum direction distribu-tion. The kinetic energy range is set to be 50 to 500 MeVfor e− and 50 to 300 MeV for protons. In addition, thereare two more MPV samples generated: low energy 1e1p(1e1p-LE) and low energy 1µ1p (1µ1p-LE) where the lat-ter is similar to 1e1p except a µ− is generated in placeof an e−. These samples are generated in the low en-ergy (LE) range. For 1e1p-LE, the e− has momentumdistributed from 30 to 100 MeV/c. For 1µ1p-LE, themomentum of µ− is distributed from 85 to 175 MeV/c.For both samples, the momentum of the proton is dis-tributed from 300 to 450 MeV/c.

C. Benchmark Data Samples

In order to validate the network’s performance on realdata, we prepared two data samples for which we have agood understanding from the traditional reconstructionapproaches available in LArSoft [20].

The first is a sample of Michel electron events [21], alsoused in our first physics result publication. This sampleprimarily consists of one track (stopping muon) and oneshower (decay electron) and is identified using a recon-struction algorithm developed by the collaboration. TheMichel electron images are simple and therefore usefulto study how the network response depends on a limitedamount of image features.

The second data set is a sample of charged-currentνµ candidate interactions with one or more photons pro-duced, primarily via π0 decay at the interaction ver-tex. The CCπ0 sample gives a different perspective thanMichel electrons because it primarily consists of higherenergy showers and tracks that make the image morefeature-rich and complicated. Validation of the networkperformance on both data sets is crucial.

For the Michel electron sample we use a random subsetof events identified as Michel electron events in Ref [21].We processed 100 data and 100 simulation events throughthe same waveform processing procedures applied to gen-erate our training sample. Then we use the reconstructeddecay position of the Michel electron as a constraint tocrop with the CRA, which produce 512 by 512 pixel im-ages containing both a stopping muon and a decay elec-tron. Next we use the LArCV [14] toolkit to producea pixel-level categorization of track and shower pixelsthrough hand-scanning of images by physicists. In thisprocess, we ignored pixels that are related neither to astopping muon nor a Michel electron. The ignored pixelsare typically due to other cosmic ray muons or secon-daries produced by them. This allows us to reduce thenumber of pixels to be labeled. The disagreement ratebetween the physicist-labels and the U-ResNet’s classifi-cation is then compared between the real data and sim-ulated data to quantify how the network performancediffers between data and simulation.

For the CCπ0 events, samples of 100 data images are

8

identified primarily by an automated reconstruction [20].Event selection algorithms look for a νµ CC interactioncandidate vertex, namely a muon track with EM-showersfrom π0 decay near to that vertex. Such a muon trackmust be either contained or associated with a protontrack to reject cosmogenic backgrounds. Selected eventsmust pass through a subsequent hand-scanning processby physicists to ensure a high purity. The reconstruc-tion algorithm in the reconstruction chain provides anestimate of the interaction vertex position. This recon-structed vertex is used as a constraint for CRA to pro-duce 512 by 512 pixel images. For the comparison study,we simulate events with BNB νµ interactions and cosmicrays. A total of 100 CCπ0 events are selected based onsimulation information. The neutrino interaction vertexlocation from simulation information is used as a con-straint for CRA to produce the same size images for sim-ulated events. Data and simulation events are processedby an identical waveform process chain used to preparethe training sample. Finally, the pixel-level physicist la-bels are generated for the CCπ0 sample. The same condi-tion is applied and the physicists labeled only pixels thatare considered to be related to a neutrino interaction, ig-noring pixels with cosmic ray induced energy depositions.

VI. NETWORK PERFORMANCE ONSIMULATION SAMPLES

We benchmark the performance on test simulationsamples using four metrics.

• ICPF mean: the average value of incorrectly clas-sified pixel fraction per image computed over allevents in a sample. The ICPF metric is a measurethat takes into account false positives and the frac-tion of labels for the track and shower categories.

• ICPF 90% quantile: the ICPF value below which90% of events in a sample are present.

• Shower error rate: the average value of the showerpixel error rate, defined as the fraction of incor-rectly labeled shower pixels as track pixels in eachimage, averaged over all images in a sample.

• Track error rate: the average value of the trackpixel error rate, defined as the fraction of incor-rectly labeled track pixels as shower pixels in eachimage, averaged over all images in a sample.

For all samples, the ICPF distributions are very similar.We show one example for the test sample in Figure 6.In general, most images have very low ICPF values, wellbelow 10% for all test samples.

The results can be found in Table VI. The network isgeneralized to perform well on simulated neutrino eventsto a level that allows us to apply the technique as apart of the reconstruction chain. We do not train the

FIG. 6. The binned ICPF distribution over all images in thetest set.

TABLE I. Values of the network performance metrics includ-ing the average of ICPF mean value, 90% quantile, the av-erage of incorrectly classified pixel fraction for shower pixels,and the same for track pixels. The test samples described inSection V B. Values are shown in percentages.

ICPF ICPFSample mean 90% Shower TrackTest 1.9 4.6 4.1 2.6νe 6.0 13.8 7.6 13.8νµ 3.9 4.5 14.2 4.31e1p 2.2 5.7 2.8 4.01µ1p-LE 2.3 2.2 6.2 2.41e1p-LE 3.9 11.5 3.8 8.0

network on our signal prediction - neutrino events simu-lated by GENIE - because this may introduce a modelbias. The benchmark results also demonstrate that theU-ResNet can classify pixels from the low energy two par-ticle topologies of 1e1p and 1µ1p into track/shower at theICPF mean value of 3.9% and 2.3%, respectively. Theseare the two simplest topologies of neutrino interactions,and it is important for U-ResNet to perform well so thatit can be used to distinguish the two neutrino flavors. Inthe 1µ1p-LE sample, despite the fact that no showers areproduced in the primary neutrino interaction, challengesfor the network arise from similarities between muonsand electrons at very low energies and from secondaryinteractions like Michel electrons from muon decays.

Figure 7 shows the binned ICPF for the 1e1p and 1e1p-LE samples as a function of kinematic variables. Fig-ure 7(a) shows the correlation with the opening anglebetween the two particles in 1e1p sample. We expect the

9

FIG. 7. The ICPF error rates for U-ResNet labeling track(proton) and shower (electron) pixels for the benchmark testset are plotted against initial kinematic variables from simula-tion information. (a) The opening angle between two particlesfrom the 1e1p sample. (b) The electron kinetic energy fromthe 1e1p-LE sample. (c) The proton kinetic energy from the1e1p-LE sample.

ICPF value to increase when the two particles are co-linear and the 2D projections overlap, making it hard todistinguish the two tracks. When they are back-to-back,the difficulty to distinguish them arises from the fact thattwo trajectories may appear as the trajectory of one par-ticle. Although it is outside of the scope of this paper,some of these difficulties could be mitigated if multiple2D projection information is incorporated.

Figure 7(b) and (c) show the dependence of the perfor-mance on the kinetic energy of a particle from 1e1p-LE

sample. We observe that the network performs worseat lower energies. The ICPF value reaches near 15% at50 MeV proton kinetic energy. A proton at this energycan only travel a few centimeters in LAr, which trans-lates into 10 pixels or fewer in the collection plane image.Such a small amount of information makes the networkstask difficult. A similar trend of decreasing performancecan be also seen for electron kinetic energy, although themagnitude is much smaller. The critical energy abovewhich electrons primarily produce bremsstrahlung in LAris about 33 MeV. In the low energy region near or belowthe critical energy, electrons may not show a geometricalfeature of showers characterized by a cascade of radia-tion. Thus, the network may struggle identifying themas showers.

Overall, these kinematic distributions show the trendwe expect, and set milestones to be achieved by futurework on deep neural network development for LArTPCdata reconstruction. A few randomly chosen exampleoutputs of the networks are shown in the Appendix fromthe νe and νµ benchmark set.

VII. NETWORK PERFORMANCE WITHDETECTOR DATA AND COMPARISON TO

SIMULATION

In this section we report the validation of U-ResNeton real detector data, in particular Michel electron andCCπ0 neutrino candidate events. Both data and simu-lation samples are processed by a physicist and containpixel-level prediction labels. We report the comparisonof the network’s disagreement with physicist-applied la-bels. The details of data preparation steps are describedin the previous sections.

A. Data/Simulation Comparison Using The MichelElectron Sample

Table II summarizes the analysis results for the Michelelectron sample. The disagreement rate between aphysicist analyzer and the network prediction is be-low the 3% level on average for both data and simu-lation. Figure 8(a) shows the distribution of the pixelfractions where U-ResNet and physicists disagreed ontrack/shower categorization over 100 events. The cal-culated physicist/network labeling for data and simula-tion agrees within statistical uncertainty. Figure 8 alsoshows binned distributions of pixel scores for data andsimulation. The track or shower label for each pixel isassigned by a physicist analyzer, and is not expected tobe perfect. The score distributions show a similar trendbetween data and simulation. The error bars are notdrawn in the score distributions since it is not trivial toderive an error for a pixel-wise score where we expectstrong inter-pixel correlations. Finally, Table II showsthat the network has a smaller ICPF when using labels

10

FIG. 8. (a) Binned distribution of ICPF for track and showerpixels where the pixel-level labels are produced by a physicist.The data and simulation distributions are area-normalizedand represent 100 Michel electron events. There is no eventoutside the shown range on the horizontal axis. (b) The nor-malized, binned softmax probability distributions for showerpixels on data and simulation. (c) The same as (b) for trackpixels.

generated from simulation information. The Appendixshows four randomly selected examples of Michel elec-tron events from real data.

Further, we inspected the robustness of the networkagainst scaling pixel values. Since there is no calibrationapplied at the stage of processed waveforms, we expecta difference in the signal strength between data and sim-ulation. We run a simple differentiation algorithm tocompare the signal strength between Michel data and

TABLE II. Values of the network performance metrics forthe Michel electron sample. The top row indicates the typeof sample used (simulation or data), the second shows thesource of a label used for analysis, and the third shows thesource of a pixel prediction. The forth and the fifth rows indi-cate the ICPF mean value over all samples and 90% quantile,respectively. The bottom two rows show the mean of ICPFfor track and shower pixels, separately. Values are given aspercentages.

Sample Data Simulation Simulation SimulationLabel Physicist Physicist Simulation SimulationPrediction U-Resnet U-ResNet U-ResNet PhysicistICPF mean 1.8 2.6 2.5 2.3ICPF 90% 3.3 4.4 4.5 3.1Shower 6.2 5.7 4.0 3.9Track 1.1 1.9 1.6 1.3

FIG. 9. Peak pixel value distribution for Michel electron im-ages for data and simulation using the 3-pixel differentiationalgorithm described in the text. The vertical axis shows thepixel counts while the horizontal axis shows the peak pixelvalues.

simulation images. The algorithm inspects every pixel inan image. The algorithm finds peak pixels by comparinga given pixel with the one before and after it along thetime axis to determine the one with a higher pixel valuethan its neighbors. This is the simplest form of a signalpeak amplitude finder algorithm. The distributions fordata and simulation are shown in Figure 9, which shows ashift between the data and simulation peak positions byabout 20% to 30%. For this study we scale the pixel val-ues of data images by a constant factor and compare theperformance of the network with different scaling factors.Figure 10 shows the results of this study. Although weobserve that the ICPF becomes worse when we apply thescaling factor, the change is within 1% absolute when wescale pixel values by 25%, which is at the level of currentdisagreement rate between simulation and uncalibrateddetector response.

11

FIG. 10. The average ICPF in percent for the Michel electrondata versus pixel scaling factor is shown in blue. The pixel-fraction disagreement between physicist and network catego-rizations is shown in percent in red for track and shower pixelsseparately.

FIG. 11. Three regions for an analysis of the inter-pixel cor-relation for a Michel in data. Region 1 contains the minimumionizing muon trajectory. Region 2 focuses on the end of themuon trajectory where dE/dx increases. Region 3 contains adecay Michel electron.

B. Qualitative Analysis of Inter-Pixel CorrelationsUsing the Michel Electron Sample

We take a qualitative look at the correlation of pixelscores using the Michel electron image from one of thereal data examples shown in Figure 11. In the followingsections, we focus on three regions shown in the figure.These correspond to 1) a minimum-ionizing muon track,2) a portion of the track with high dE/dx near the muonstopping point, and 3) the low energy Michel electronshower, respectively.

1. Minimum Ionizing Muon Track

One possible property used to distinguish a minimumionizing muon from a low energy electron is the topologyof its trajectory. This is often a long straight line, ascompared to a more “jagged” electron trajectory due tohigher multiple coulomb scattering. We choose subsetsof Region 1 shown in Figure 11 to test this hypothesis bymasking all remaining pixels in the image to zero. Fig-ure 12 shows the masked images and the correspondingtrack vs. shower score distribution of non-zero (i.e., un-masked) pixels by running the U-ResNet on each image.In Figure 12 (a) to (d), we show a series of images withincreasing number of unmasked pixels to determine howthe score distribution changes. When we provide only a5-pixel long minimum ionizing track, separation is weak.The separation improves as we include more neighboringpixels, which makes the straight-track shape longer andlonger. We conclude that this confirms our hypothesisthat the network is focusing on the length of a straightminimum ionizing particle’s trajectory.

2. Bragg Peak

A stopping muon increases its energy deposition den-sity, dE/dx, as it loses momentum and near the stop-ping point has the highest dE/dx called the Bragg peak.This increasing dE/dx is a useful signature to identify astopping muon [21] and therefore make a decision thata trajectory is track-like. In Figure 12, we show thatthe network struggles with a straight, minimum ioniz-ing track-like trajectory of relatively few pixels. Fig-ure 13 shows Region 2 of Figure 11, near the stoppingmuon’s Bragg peak point, where we masked the rest ofmuon trajectory and the entire electron charge deposi-tions. Track and shower score distributions are well sep-arated at all track lengths. This is a distinct featurefrom Figure 12. We therefore conclude that the networkis keying on an increasing dE/dx, or a high dE/dx, toclassify a straight-line-like topology into track-like withhigh confidence even if the length of such a trajectory isdown to several pixels.

3. Low Energy Electron Shower

One key feature of an electromagnetic shower is itsnon-straight line trajectory. To test this hypothesis, wetake a closer look at the Michel electron charge depositionin Region 3 of Figure 11. In this event a Michel electrontraveled straight for several pixels. Then it started toscatter off of other electrons before the end of the tra-jectory. Because of its mass, the Michel electron is min-imum ionizing for most of its trajectory. We investigatehow the network’s confidence varies if we separate theinitial straight, minimum ionizing trajectory of a Michelelectron from the remaining image.

12

FIG. 12. Minimum ionizing muon track in Region 1 of Figure 11 where all pixels in the image are masked except for a smallportion of a muon track shown in the images above. The lower row shows normalized track vs. shower score distributions forall non-zero pixels in the image.

FIG. 13. Stopping muon track in Region 2 of Figure 11 where all pixels in the image are masked except for the small portionof the muon track next to its stopping point shown in the images above. The lower row shows normalized track and showerscore distributions for all non-zero pixels in the image.

Figure 14 shows that, where the Michel electron pic-ture is complete, all pixels are identified strongly as ashower. We then mask the first several pixels that looklike a track of a minimum ionizing muon (Figure 14(b)).The network’s confidence remains very strong in this re-gion. We also show the network’s response to the firstseveral pixels of a Michel electron (Figure 14(c)). Thenetwork is entirely uncertain whether this is a track or ashower in this case.

Finally, we investigate the intersection of Region 2 and3 by adding the final pixels of the Bragg peak of the stop-ping muon to the first several pixels of the Michel elec-tron, as shown in Figure 15 We add a heat map whichshows the score for non-zero pixels for the classified cat-egory. In the heat map we observe the dark-red region,corresponding to the score value 1, and the other regionin yellow/orange color which corresponds to weaker clas-sification scores. We also show the track and shower score

13

FIG. 14. Michel electron in Region 3 of Figure 11 where all pixels in the image are masked except for a portion of an electrontrajectory shown in the images above. The lower row shows normalized track and shower score distributions for all non-zeropixels in the image.

FIG. 15. The intersection of Region 2 and 3 of Figure 11 where all pixels in the image are masked except for a small portionof a Michel electron and the Bragg peak from a stopping muon. The middle histogram shows a normalized track and showerscore distribution for all non-zero pixels in the image. The right figure shows the normalized score value of the classified pixelcategory.

distribution of all non-zero pixels. The network’s confi-dence level remains high on classifying the Bragg peak astrack-like. Secondly, by comparing the score distributionof Figure 15 and the score distribution in Figure 14(c),we conclude that the beginning portion of the Michelelectron now has a higher likelihood to be classified asshower-like. This suggests that the network is using thepresence of the Bragg peak in the image to improve theclassification of the minimum ionizing straight trajectorythat starts from the end of the Bragg peak, which is oth-erwise ambiguous as track-like or shower-like.

C. Data/Simulation Comparison Using The CCπ0

Sample

The results of running U-ResNet on the CCπ0 imagesamples are summarized in Table III. We find a similartrend as observed in the Michel electron sample, but with

slightly higher disagreement rates. This is expected giventhat CCπ0 samples are complex images because of thehigher number of particles and interactions involved. Thetop plot in Figure 16 shows the ICPF distribution. Asin the case of the Michel electron sample, data and sim-ulation are in agreement within statistical fluctuations.In Figure 16(b,c), we show the score distributions for thetrack and shower pixels labeled by a physicist. A sim-ilar trend is observed between data and simulation inboth pixel categories. Four example images are shown inthe Appendix with the U-ResNet output. The displayedevents are visually selected by a physicist because of theirparticularly busy vertex activities.

Following the analysis of the Michel sample, we inves-tigate how the scaling of pixel values in the data imageaffects the network performance. The result is shown inFigure 17. We find that the ICPF values have small vari-ation among the scaling factors applied in the study. Thissuggests that, although the effect is small, at the 1% level

14

FIG. 16. (a) The binned distribution of ICPF where the pixel-level labels are produced by a physicist. The Data (black) andsimulation (red) distributions are area-normalized, producedfrom 100 CCπ0 events. There is no event outside the shownrange on the horizontal axis. (b) The normalized, binned soft-max probability distributions for shower pixels by the networkon data and simulation. (c) The same as (b) for track pixels.

for a 25% pixel value scaling factor, mismatched signalstrength in data does affect the network’s response.

D. Disagreement Between U-ResNet and PhysicistLabeling for the CCπ0 Sample

The CCπ0 events present far richer topologies thanthe Michel events, and we do not attempt to perform thepixel-masking and the deduction exercise to learn how

FIG. 17. The ICPF mean in percent for CCπ0 data (blue) forvarying pixel scaling factor shown on the horizontal axis. Acategory-wise physicist-network disagreement pixel fraction inpercent is shown in red for track and shower pixels separately.

TABLE III. Values of the network performance metrics for theCCπ0 sample. The top row indicates the type of a sample used(simulation or data), the second shows the source of a labelused for analysis, and the third shows the source of a pixelprediction. The forth and the fifth rows indicate the ICPFmean value over all samples and 90% quantile, respectively.The bottom two rows show the mean of ICPF for track andshower pixels, separately. Values are given as percentages.

Sample Data Simulation Simulation SimulationLabel Physicist Physicist Simulation SimulationPrediction U-ResNet U-ResNet U-ResNet PhysicistICPF mean 3.4 2.5 1.8 2.0ICPF 90% 9.0 5.7 4.6 4.8Shower 4.8 3.4 3.0 2.6Track 2.7 2.4 2.2 2.9

the network works in this sample. Instead, we study theCCπ0 data events where the disagreement between physi-cist and network labeling is largest. Four such events areidentified and shown in Figure 18. The four events shownare ordered by level of disagreement rates of 0.166, 0.166,0.162, and 0.125, respectively. In the example shown onthe top of the figure, the disagreement is mainly in a longtrack-like trajectory originating from the interaction ver-tex. While a physicist analyzer decided this is a track,it could also be a minimum ionizing electron that shouldbe classified as a shower. The second display from thetop shows the network’s attempt to separate a track-liketrajectory that is present inside a high energy shower. Inthe third image, a large portion of particle trajectory isinvisible due to unresponsive region of the detector run-ning vertically toward the right of this image. This makesit difficult to analyze the remaining particle trajectorieswhere the U-ResNet mixes track and shower pixel de-cisions for the same trajectory. Finally, in the bottomimage, the network predicts two track-like trajectories

15

FIG. 18. Four example CCπ0 events with highest ICPF values using physicist generated pixel labels. Left: input images to thenetwork. Middle: track (yellow) and shower (cyan) using physicist generated labels. Right: track (yellow) and shower (cyan)labels predicted by the network.

16

coming from the interaction vertex while the physicistanalyzer merged two tracks into one near the vertex. Theregion around the interaction vertex is complicated by en-ergy deposition from a shower, which makes it difficult todetermine which decision is correct. The performance ofthe network for these maximal disagreement events sup-plements our understanding from the qualitative Micheldata analysis and documents some of the ways that thenetwork fails in categorizing pixels.

VIII. CONCLUSION

In this paper we have presented the first applicationof a deep semantic segmentation network, U-ResNet, toperform track/shower separation at the pixel-level forLArTPC images. We explore training techniques in-cluding transfer learning and pixel-wise error weightingmethods. Our software tools and algorithms to storeand apply the pixel-level labeling are made available inRefs [14, 16].

U-ResNet achieved an average ICPF of 6.0% and 3.9%benchmarked with 20,000 images of νe and νµ inter-actions, respectively, simulated with realistic neutrinobeam information. The same network achieved an av-erage ICPF of 3.9% for 1e1p-LE events in which elec-trons have a uniform momentum distribution from 30 to100 MeV/c, and protons from 300 to 450 MeV/c. Theaverage ICPF is found to be 2.3% for 1µ1p-LE eventswhich include protons with the same uniform momen-tum distribution and momentum range, and muons in amomentum range of 85 to 175 MeV/c.

We quantified and validated U-ResNet, trained purelyon simulated image samples, on LArTPC images fromreal detector data. We calculated the fraction of incorrectpixel labeling between U-ResNet and a physicist analyzerand found an average disagreement fraction of 1.9% and3.4% for Michel electron data and CCπ0 data, respec-tively. The same analysis was performed using simula-tion samples, and we found that the level of disagree-ment is consistent for data and simulation samples. Thisis the first time such validations have been shown on realLArTPC data. From a qualitative analysis on the Michelelectron data we conclude that the network is focusing onintuitively reasonable physics features in the image. Thesuccessful application of a semantic segmentation net-work on LArTPC data is an important milestone towarddeveloping a full LArTPC data reconstruction chain us-ing a deep neural network.

IX. ACKNOWLEDGEMENT

This document was prepared by the MicroBooNE col-laboration using the resources of the Fermi National Ac-celerator Laboratory (Fermilab), a U.S. Department ofEnergy, Office of Science, HEP User Facility. Fermilabis managed by Fermi Research Alliance, LLC (FRA),

acting under Contract No. DE-AC02-07CH11359. Mi-croBooNE is supported by the following: the U.S. De-partment of Energy, Office of Science, Offices of HighEnergy Physics and Nuclear Physics; the U.S. NationalScience Foundation; the Swiss National Science Founda-tion; the Science and Technology Facilities Council of theUnited Kingdom; and The Royal Society (United King-dom). Additional support for the laser calibration sys-tem and cosmic ray tagger was provided by the AlbertEinstein Center for Fundamental Physics, Bern, Switzer-land.

APPENDIX

In this appendix we show example event displays ofsimulation and data. Figure 19 shows simulated νe andνµ interactions. Figure 20 shows a stopping muon witha decay Michel electron. Figure 21 shows “busy” CCπ0

candidate events visually selected including those with aparticle multiplicity greater than 4. In all of these eventdisplays, gaps in tracks and showers are due to unrespon-sive wires. Overall, we observe good agreement betweenthe simulation information and output from the networktrack-shower pixel labeling in diverse event types.

17

FIG. 19. Event displays of νe (upper two rows) and νµ interactions (lower two rows). The left column images are inputs to thenetwork. The middle column shows labeled images based on simulation information. Track pixels are masked in yellow andshower pixels are in cyan. The right column shows the output of the network.

18

FIG. 20. Michel electron event displays from real detector data. Left: input images to the U-ResNet. Middle: track (yellow)and shower (cyan) physicist labels. Right: track (yellow) and shower (cyan) labels predicted by the network.

19

FIG. 21. Neutrino event displays from CCπ0 candidate detector data selected based on activity around the interaction vertex.Left: input images to the network. Middle: track (yellow) and shower (cyan) physicist labels. Right: track (yellow) and shower(cyan) labels predicted by the network.

20

[1] M. Antonello et al. (MicroBooNE, LAr1-ND, andICARUS-WA104 Collaboration), “A Proposal for aThree Detector Short-Baseline Neutrino Program in theFermilab Booster Neutrino Beam,” arXiv:1503.01520[physics.ins-det].

[2] R. Acciarri et al. (DUNE Collaboration), “Long-BaselineNeutrino Facility (LBNF) and Deep Underground Neu-trino Experiment (DUNE) : Volume 1: The LBNF andDUNE Projects,” arXiv:1601.05471 [physics.ins-det].

[3] R. Acciarri et al. (MicroBooNE Collaboration), “Designand construction of the MicroBooNE detector,” Journalof Instrumentation 12, 02017 (2017).

[4] A. A. Aguilar-Arevalo et al. (MiniBooNE Collaboration),“Unexplained Excess of Electron-Like Events From a 1-GeV Neutrino Beam,” Phys. Rev. Let. 102, 10802 (2008).

[5] A. A. Aguilar-Arevalo et al. (MiniBooNE Collaboration),“The Neutrino Flux Prediction at MiniBooNE,” Phys.Rev. D 78, 072002 (2009).

[6] R. Acciarri et al. (MicroBooNE Collaboration), “NoiseCharacterization and Filtering in the MicroBooNE Liq-uid Argon TPC,” Journal of Instrumentation 12, 08003(2017).

[7] R. Acciarri et al. (MicroBooNE Collaboration), “Con-volutional neural networks applied to neutrino events ina liquid argon time projection chamber,” Journal of In-strumentation 12, 03011 (2017).

[8] LArSoft release redmine 06.26.01 https:

//cdcvs.fnal.gov/redmine/projects/larsoft/wiki/

ReleaseNotes062601.[9] O. Ronneberger, P. Fischer, T. Brox, “U-Net: Convo-

lutional Networks for Biomedical Image Segmentation,”Medical Image Computing and Computer-Assisted Inter-vention (MICCAI) 9351, 234 (2015).

[10] K. He, X. Zhang, S. Ren, J. Sun, “Deep Residual Learn-ing for Image Recognition,” Computer Vision and Pat-tern Recognition (CVPR), 2016 IEEE Conference on,770 (2016).

[11] S. Ioffe and C. Szegedy., “Batch normalization: Acceler-ating deep network training by reducing internal covari-ate shift,” arXiv:1502.03167.

[12] T. Tieleman and G. Hinton, “Lecture 6.5-rmsprop: Di-vide the gradient by a running average of its recentmagnitude,” COURSERA: Neural Networks for MachineLearning, 4 (2012).

[13] Y. Jia et al., “Caffe: Convolutional Architecture for FastFeature Embedding,” arXiv:1408.5093.

[14] V. Genty, K. Terao and T. Wongjirad, LArCV: LArTPCImage Processing/Analysis Framework for Deep Learn-ing https://www.github.com/LArbys/LArCV

[15] NVIDIA GeForce Titan X GPU Card Specification,http://www.geforce.com/hardware/desktop-gpus/

geforce-gtx-titan-x/specifications

[16] uboonecode release redmine 06.26.01.13 https://

cdcvs.fnal.gov/redmine/projects/uboonecode.[17] C. Adams et al. (MicroBooNE Collaboration), “Ioniza-

tion Electron Signal Processing in Single Phase LArT-PCs I. Algorithm Description and Quantitative Evalu-ation with MicroBooNE Simulation,” arXiv:1802.08709[physics.ins-det].

[18] C. Adams et al. (MicroBooNE Collaboration), “Ioniza-tion Electron Signal Processing in Single Phase LArT-PCs II. Data/Simulation Comparison and Performancein MicroBooNE,” arXiv:1804.02583 [physics.ins-det].

[19] C. Andreopoulos et al., “The GENIE Neutrino MonteCarlo Generator,” it NIM A614 87-104 (2010).

[20] R. Acciarri et al. (MicroBooNE Collaboration), “ThePandora multi-algorithm approach to automated pat-tern recognition of cosmic-ray muon and neutrino eventsin the MicroBooNE detector,” Eur. Phys. J. C 78, 82(2017).

[21] R. Acciarri et al. (MicroBooNE Collaboration), “Michelelectron reconstruction using cosmic-ray data from theMicroBooNE LArTPC,” Journal of Instrumentation 12,09014 (2017).