From Pixels toNeutrinos
Convolution Neural Networks
Applied to the MicroBooNE experiment
Taritree Wongjirad (Tufts U.)
SLAC Seminar
June 6, 2019

Outline
Discuss efforts on the MicroBooNE
experiment to use convolutional neuralnetworks to improve physics analysis●●●●

What are we trying to solve with CNNs? -and why?
From physics problem to ML problem
Current Analysis EffortDevelopments“Eyes” of the MicroBooNE detector -- atime-projection chamber,-- before installation
2

The MicroBooNE Experiment in a nutshell“An accelerator neutrino oscillation
experiment”3

Neutrinos“An accelerator neutrino oscillation
experiment”

4Neutrinos
The Three “FLAVORS”

Neutrinos are a type of the fundamentalparticle
Key stats:● No electric charge● Interacts only via weak force and gravity● Very, very small mass: ~8 orders smaller thannext heaviest particle!
● Come in three “flavors” -- based on what otherparticle they make during certain interactions

electronmuontau5Life as Neutrino Physicist
No electric charge=> observe indirectlyFlavor related to particle producedduring certain interaction=> key to identifying type

Only Weak Force=> rare process𝝂eelectron
6

Neutrino Source“An accelerator neutrino oscillation
experiment”

MicroBooNE uses a high-intensity
beam of neutrinos made at
Fermi National Accelerator Lab

7

Neutrino Oscillations“An accelerator neutrino oscillation
experiment”
Neutrino created in certain flavor ...

Goal is to look forevidence of Neutrino
Oscillations

can later be
detected in other flavor ...
or later in original ...

Flavor detectedoscillations over distancetraveled8

Neutrino Oscillations
Anomalies in other pastexperiments can beinterpreted as oscillations
occurring because of newflavor or neutrino?

Exciting if true!

9MicroBooNE setup
Beam created as almost
entirely muon neutrinos

Given distance traveled andenergy of neutrinos,
Should be the case @ detector10MicroBooNE setup
But if consistent with past
Anomaly (measured in same
beam line)
We will see excess ofelectron neutrinos(at low energies)11

MicroBooNE setup
Our target measurement, then, is the energy ofneutrinos and the counts of the different flavors

The detector, a liquid argon time projection chamber,provides high resolution images with which we can extract
these measurements
12Capturing Images of Neutrino Interactions

13Capturing Images of Neutrino Interactions
A neutrino(dashed grey)
passes into thedetector andinteracts
producingcharged particles(solid yellow)
14Capturing Images of Neutrino Interactions
Charged particles
produce ionizationelectrons alongpath(neutrino neutraland leaves nodirecty signature)15

Capturing Images of Neutrino Interactions
GPU-accelerated photon simulation, showing final
location of photons

Light also produced bycharged particles.
Travels to sensors onshort (ns) timescales

Red: hit photo-sensor (PMT)
Blue: absorbed on surface
Green: abosrbed in bulk
White-lines: particle trajectories

Light provides timingfor event -- and course
position info.
16

Capturing Images of Neutrino Interactions
Ionization electrons
drift towards
wireplanes

17Capturing Images of Neutrino Interactions
Ionization induce
detectable signals
on nearby wires

18Capturing Images of Neutrino Interactions

19Capturing Images of Neutrino Interactions
In principle: enoughinformation for 3Dreconstruction(Y,Z) position of ionization
recorded throughcoincident signals on
different wire planes
X position give by timedelay from light signal
20

Capturing Images of Neutrino Interactions
Recording wiresignals over time,
detector produces
image-like data

21

Capturing Images of Neutrino Interactions
Plane 2

Plane 3time

Plane 1

wire number

Example of data event in MicroBooNE. View of same event for each projection.
Color scale indicates amount of ionization electrons seen on wire at given time22

Measurables in the image
Flavor determined from finding partner lepton (muon,electron) produced in interaction
Neutrino energy inferred from momenta of resulting particles

23

Measurables in the image
From ML perspective: the problem is predicting class and energy from images
We have simulations that can produce training examples with various labels

24One approach: directly predict values
Example: CNNs have been used (another experiment) to predict the neutrino flavor
𝜈e interaction

𝜈𝜇 interaction

Aurisano, A. et al. JINST 11 (2016) no.09
25

One approach: directly predict values
Issue with training network to target final observable: modeling uncertainties
Neutrinos hit constituents of the nucleus -- anextremely complicated system to model
Neutrino energy inference influenced by knowing“type” of interaction
Can produce particles that cause patterns whichfake the signal (primary e.g. photons frominteraction can look like electrons)

Risk of model errors being trained into the networks
26

MicroBooNE specific issue: backgrounds
For MicroBooNE, lots ofbackgrounds from cosmic rays
since detector is on the surface
Requires parsing of image tofind neutrinos
Images with target events only
1 in 1000-10,00027

DL Reconstruction
Our goal is to produce constituent particles. Manytechniques available-Need object detection
Classification

Evaluation with respect to analysis performance important28

Current Analysis
Have built a fullreconstruction/analysis chain tosearch for the oscillation signal
A mixture of CNNs and traditionalalgorithms
Working analysis benchmarks
improvements from CNN
techniques

Pre-selection using light
Choosing interesting regions andremoving obvious backgrounds
CNN for shower (electron) versus
track (muon) patterns
Reconstruct neutrino interaction
Particle classification
29Current Analysis

First applications chosen as they
are techniques where we could use
non-signal data to evaluate network
behavior on real data30

Preparing the data
●Images preparation:
○
○
○
○

●

Noise filtering
pulse finding + zero suppression
Deconvolve wire response■ Accounting for electronics response + expected induced signal
Downsample in time (summed) by factor of 6

3D consistent cropping○○○

Full size: 3456 (wire) x 6448 (ticks)
Downsampled size: 3456 x 1008 -- both dimensions about 3 mm
Cropped into 832 wire x 512 ticks (24 images per plane)31Pixel labeling32

Pixel labeling

We use a U-Net for thisproblem

33

Pixel labeling: behavior on real versus sim. images

These are cosmic
particles that cometo rest in the detector
Mostly muons, manyof which decay intoelectrons
Use to check track
and shower labeling34

Analysis Status
Analysis components complete
Evaluating:● Data versus sim. differences
through distributions of particle
kinematics● Sensitivity of analysis to see
anomalous signal (or excluding it)
Not yet ready● Hitting various performancemilestone with simulation dataset● Aim is to release result within year
35

Further CNN techniques in the works
Also tackling more parts of thereconstruction chain

Finding and removing non-neutrino
tracksProviding 3D spacepoints to perform 3D
reconstruction at earlier stage“Reparing” images to assist trackreconstruction
36

Applying Instance Aware Segmentation

Currentlyadapting
Detectron
Mask-RCNN
network37

Mask R-CNN for cosmic detection and rejection
Example application on
MicroBooNE cosmic data38

Mask R-CNN: evaluating performance

39

Mask R-CNN: evaluating performance40

Image Repair and Tracking

41

Image Repair and Tracking

Gaps impair ability to trackreconstruction to accurate getmomentum
Currently try to detect when track ends
in gap and remove events
Overcoming this can also help withefficiency
42

Image Repair and Tracking

Fills in tracks -- but can produce regions thatare “odd” looking
Research ways to penalize net for producing
odd shapes -- similar to way GANs are trained43

3D Space Points

44

3D Space Points

45

LArFlowin LArTPC
context
matchability = 0
when true target pixel indead wires, below
thresh, etc.

enforce same-time tick,so only wire-direction
flow predicted46

LArFlow
Network predicts correspondence between pixels (charges) in Y, U, V ADC images
For pixel i in Y plane: the CNN is asked to predict
shift needed to move to pixel 1175

Which is where the corresponding pixel is in U planef(i)iCorrespondence prediction gives 3D space-point for that charge
47

LArFlow: Network2D ADC image

encoder

decoder(skip connections used)

Uconv
concatYV

deconv

MU,Ydeconv

FU,Ydeconv

MV,Ydeconv

FV,YCNN

CNN
Loss function

𝓛 = λM + F𝓛

For future: enforce 3D consistency
loss between Y->U and Y->Vprediction48

48

LArFlow: Loss49

49

LArFlow: Initial Performance
Have plans to use cosmic
muon data to evaluate similar
metrics
Good enough for cosmic
rejection
Improvements in precisionneeded for neutrino
reconstruction

5050

LArFlow: Initial Performance
Top: combining 3D points withtrack/shower labeling

Bottom: using Mask-RCNN network tocluster cosmic muon candidates

5151

Towards 3D space-point reconstruction
Our work is incollaboration with
DL-based reco on
space-points done
here at SLAC

5252

Summary
Results from work by:
CNNs well-suited to analysis of LArTPC
imagesApplications developed in conjuction withphysics analyses -- important for knowing
effect on ultimate goal

Katie Mason(grad)

Joshua Mills(grad)

Moving towards an end-to-end reconstruction
chain using networks
Stay tuned for analysis result!
Ralitsa Sharanova(post-doc)
5353