Using Convolution Neural Networks From Pixels to Neutrinos · MicroBooNE LArTPC Neutrino Experiment...
Transcript of Using Convolution Neural Networks From Pixels to Neutrinos · MicroBooNE LArTPC Neutrino Experiment...
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
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MicroBooNEExperiment
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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
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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
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Capturing Images of Neutrino Interactions
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Capturing Images of Neutrino Interactions
A neutrino (dashed grey) passes into the detector and interacts producing charged particles (solid yellow)
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Capturing Images of Neutrino Interactions
Ionization electrons drift towards wireplanes
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Capturing Images of Neutrino Interactions
Ionization induce detectable signals on nearby wires
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Capturing Images of Neutrino Interactions
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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
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Capturing Images of Neutrino Interactions
Recording wire signals over time, detector produces image-like data
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Capturing Images of Neutrino Interactionstim
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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
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MiniBooNE nue Low Energy Excess
One of MicroBooNE’s goals is to investigate the observed electron neutrino excess by MiniBooNE
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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
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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
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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
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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
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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)
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See Rui An’s Talk (following Ran’s)
MicroBooNE LEE Analysis Status
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Progress of analysis shown at Neutrino 2020 -- see G. Karagiorgi talk, student posters, public notes
MicroBooNE LEE Analysis Status
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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?
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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!
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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
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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
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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
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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
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Making 3D points
MicroBooNE off-beam data
MicroBooNE off-beam data
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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
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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
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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
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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
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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)
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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)
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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
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Thank you
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