From Pixels to NeutrinosIn the MicroBooNE LArTPCUsing Convolution Neural Networks

Taritree Wongjirad (Tufts U.)NPML WorkshopJuly 10th, 2020

Outline

Provide an overview of how MicroBooNE is making use of Convolutional Neural Networks

● Background on MicroBooNE and LArTPCs● CNNs are being used in an analysis to

investigate the MiniBooNE anomaly● Given goal of workshop, a view of next-Gen

CNNs under development

2Taritree Wongjirad

MicroBooNEExperiment

3

MicroBooNE

● LArTPC Neutrino Experiment at Fermilab● Detector located 470 m from start of beam -- 99.5% muon neutrinos● Taking physics data since Winter of 2015● Goals

○ LArTPC R&D: hardware/software/operations○ Investigate MiniBooNE “low energy electron neutrino excess”○ Study neutrino-argon interactions

4Taritree Wongjirad

MicroBooNE

● LArTPC Neutrino Experiment at Fermilab● Detector located 470 m from start of beam -- 99.5% muon neutrinos● Taking physics data since Winter of 2015● Goals

○ LArTPC R&D: hardware/software/operations○ Investigate MiniBooNE “low energy electron neutrino excess”○ Study neutrino-argon interactions

5Taritree Wongjirad

Capturing Images of Neutrino Interactions

6

Capturing Images of Neutrino Interactions

A neutrino (dashed grey) passes into the detector and interacts producing charged particles (solid yellow)

7

Capturing Images of Neutrino Interactions

Ionization electrons drift towards wireplanes

8

Capturing Images of Neutrino Interactions

Ionization induce detectable signals on nearby wires

9

Capturing Images of Neutrino Interactions

10

Capturing Images of Neutrino Interactions

(Y,Z) position of ionization recorded through coincident signals on different wire planes

X position give by time delay from light signal

11

Capturing Images of Neutrino Interactions

Recording wire signals over time, detector produces image-like data

12

Capturing Images of Neutrino Interactionstim

e

wire number

Plane 1 Plane 2 Plane 3

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 time13

Event Reconstruction

14

MiniBooNE nue Low Energy Excess

One of MicroBooNE’s goals is to investigate the observed electron neutrino excess by MiniBooNE

15Taritree Wongjirad

https://arxiv.org/pdf/2006.16883.pdf A. Hourlier Neutrino 2020 talk

Investigating the Excess

Flavor determined from finding partner lepton (muon,electron) produced in interaction

Neutrino energy inferred from momenta of resulting particles

16

DL Working Group AnalysisPMT Precuts

Cosmic Tagger/CROI finding

SSNet: track/shower labels

Candidate Vertex Search

Vertex selection

track/shower reco

1mu1p 1e1p

Multi-Particle ID

One of several low energy excess analyses using CNNs:1 lepton + 1 proton exclusive channel search

~50% of nue CC interactions at low energyProton helps reject cosmic raysProton provides handle to target high purity CC quasi-elastic interactions through Enu consistency with lepton

17Taritree Wongjirad

1e1p event

1mu1p event

DL Working Group AnalysisPMT Precuts

Cosmic Tagger/CROI finding

SSNet: track/shower labels

Candidate Vertex Search

Vertex selection

track/shower reco

1mu1p 1e1p

Multi-Particle ID

18Taritree Wongjirad

SSNet: track/shower labels

DL Working Group AnalysisPMT Precuts

Cosmic Tagger/CROI finding

SSNet: track/shower labels

Candidate Vertex Search

Vertex selection

track/shower reco

1mu1p 1e1p

Multi-Particle ID

Use track-shower labels to1) Seed potential 1e1p candidates2) Separate pixels for track and shower

for 3D reconstruction

19Taritree Wongjirad

See Ran Itay’s Talk (following this one)See his Neutrino 2020 satellite talk as well

DL Working Group AnalysisPMT Precuts

Cosmic Tagger/CROI finding

SSNet: track/shower labels

Candidate Vertex Search

Vertex selection

track/shower reco

1mu1p 1e1p

Multi-Particle ID

Use Multiple Particle ID1) After vertex and particles found2) Reject non-1e1p neutrino events (e.g. events with pi0)

20Taritree Wongjirad

See Rui An’s Talk (following Ran’s)

MicroBooNE LEE Analysis Status

21Taritree Wongjirad

Progress of analysis shown at Neutrino 2020 -- see G. Karagiorgi talk, student posters, public notes

MicroBooNE LEE Analysis Status

22Taritree Wongjirad

Sideband checks show good agreementShower reconstruction, based on simple SSNet shower-pixel clustering, validated using Pi0 mass

(showing “no-excess” prediction)

See Davio Cianci (Columbia) Neutrino 2020 poster

See Jarrett Moon (MIT) Neutrino 2020 poster See Katie Mason (Tufts)

Neutrino 2020 posterSystematics used in plots described in Lauren Yates

Neutrino 2020 poster

What’s Next?

23

Future Plans: Gen-2 DL Analysis

● Opportunity for Gen-2 analysis incorporating more ML to improve efficiency and purity AND add more data

● Ultimate aim is for a system of networks○ train entire system end-to-end○ downstream networks can be trained to accommodate

upstream failures; upstream networks avoid difficult mistakes to downstream

● DL reco applied to more analyses, e.g. cross sections● Include many of the new techniques we’ll hear about in this

workshop!

24Taritree Wongjirad

GEN-1GEN-2

Future Plans: SBN

● Gen-2 release overlaps with with the SBN-era. Opportunities and challenges:

○ Sharing networks -- how to apply across detectors○ Constraining det. uncertainties and their effects on network using near detector ○ Playing with others -- cross pollination with other Reco efforts, e.g. Wire Cell, Pandora○ Common software -- put in place to provide tools for DUNE as well

25Taritree Wongjirad

New CNN Applications

● Lots of new efforts○ Image repair: Katie Mason, Tufts (talk) (slides)○ 3D space points from images: Ralitsa Sharankova, Tufts (talks)(slides)○ Clustering for cosmic and nu interactions: Joshua Mills, Tufts (talk) (slides)○ Electron energy reconstruction: Nick Kamp, MIT (talk)(slides)○ Vertex Finding (BNL)○ Waveform deconvolution (BNL)○ Generating images

26Taritree Wongjirad

Image Repair for improved Tracking

Reducing track reco mistakes will:● improve muon momentum

reconstruction -- important in use of Enu consistency

● Leaves behind fragments that one must cut -- reducing harshness of cut can improve efficiency

27Taritree Wongjirad

Goal of “Infill” network is to Reduce tracking mistakes

Katie Mason(Tufts grad. student)

Build 3D points by combining information from the three wire planesU plane V plane Y plane

28

Making 3D points

MicroBooNE off-beam data

MicroBooNE off-beam data

28Taritree Wongjirad

Clustering/tracking is “easier” in 3D as different particles spatially separate in 3D while overlapping in 2D projections

Ralitsa SharankovaTufts postdoc

Mask R-CNN for cosmic detection and rejection

Example application on MicroBooNE cosmic data

29Taritree Wongjirad

Instance-aware segmentation based on the Mask R-CNN network

Goal is to improve cosmic rejection/clustering, allowing for looser selection cuts and higher efficiency J. Mills

Tufts GradFelix Yu

Tufts Junior

Electron energy estimation

Nick KampMIT grad. student

Energy bias and resolution improvement over current shower reco (in DL analysis)

Mainly through handling difficult cases e.g. adjusting estimate when dead wires present

preliminary

Blue: old methodRed: CNN

preliminary

30Taritree Wongjirad

Generative Networks

CNNs are capable of generating novel images

Well-studied application is the generation of faces

Recent efforts in field to control aspects of generated image

TL-GAN, Shaobo GUAN

31Taritree Wongjirad

Generative Networks for LArTPCs

● Replace simulation: ○ however, in my personal opinion, HEP’s ability to produce

simulated data from physic model one of our field’s advantages. Could generative networks make anything comparable?

○ Could be useful in some contexts where current simulation is difficult and not as accurate-- e.g. photodetector simulation

● Final-state hypothesis testing, fitting

○ Algorithms to parse complicated final states need to make choices -- increase particle energy? Change angle? Add scattering vertex? Produce secondary?

○ Fast generation of what final-state hypothesis looks like in data used to compare to event and provide metric for decision making

Final state Hypothesis

- Particle ID- Kinematics- Secondaries

Comparison to event image

32Taritree Wongjirad

Generative Networks for LArTPCs

Training examples

First attempts at generating small imagesTrying to understand if there are issues inherent to producing LArTPC data

Generated examples

Kai StuartTufts ‘20

(now at Broad Institute)

33Taritree Wongjirad

Prof. Shuchin Aeron

Tufts ECE

Paul LutkusTufts Senior

Making use of open dataset! https://arxiv.org/abs/2006.01993

Generator mapping into “compressed” data manifold

Generative Networks for LArTPCs

Training examples

Still a ways to go -- but it’s a start

Generated examples

DC-GAN (2015) BigGAN (2018)

34Taritree Wongjirad

Summary

MicroBooNE has been a great incubator for CNN-for-LArTPC development

Stay tuned for analysis using DL output this year!

Gen-2 analysis with new CNNs in development -- looking forward to using developments from within MicroBooNE AND other experiments

35Taritree Wongjirad

Thank you

36