deep neural network applications to proton decay analysis
TRANSCRIPT
Food for Thoughts
Deep Neural Network Applications
to Proton Decay Analysis
Kazuhiro Terao SLAC National Accelerator Laboratory
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Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
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K+
µ+
e+pµ+
e+
Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
• What can they do? - Image classification - Object detection - Pixel segmentation - Clustering - Hierarchy/Graph
V.S.
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Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
• What can they do? - Image classification - Object detection - Pixel segmentation - Clustering - Hierarchy/Graph
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Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
• What can they do? - Image classification - Object detection - Pixel segmentation - Clustering - Hierarchy/Graph “shower” vs. “track”
separation
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Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
• What can they do? - Image classification - Object detection - Pixel segmentation - Clustering - Hierarchy/Graph
This is work in progress…
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Deep Neural Network Applications• Two popular types: CNN & RNN - CNN actively developed in computer vision (CV) - RNN in natural language processing (NLP) - Both are popular in physics analysis
• What can they do? - Image classification - Object detection - Pixel segmentation - Clustering - Hierarchy/Graph
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Proton Decay: Example I“ROI” Image Classification
K+
µ+
e+
pµ+
e+
Very popular “catch-all” approach. There is no reason why this cannot be combined with BDT or a different approach
(they can be complimentary discriminators)8
Proton Decay: Example IIVertex reconstruction
Q: What is the vertex reconstruction efficiency? If not great for traditional methods, this can be improved. Crucial for good clustering, thus directionality estimate.
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Proton Decay: Example IIIMichel identification
MicroBooNE demonstrated Michel ID with high purity. Segmentation (+ point prediction) can help to improve
Michel electron search efficiency.10
Proton Decay: Example IVµ/π separation
Q: Is this useful? Study with 3mm wire pitch toy simulation (mimiced MicroBooNE) could separate µ/π at ~80% level
π- µ-
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Proton Decay: Example VOptical signal analysis
You can surely apply DNNs on optical data analysis
Sorry, No Picture (lack of effort)
Please Imagine a Paradise
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Venues in Techniques
All 2D techniques mentioned so far can benefit from providing multiple projection images with a proper network architecture.
Multi-plane Network
3D Pattern Recognition3D is the solution to avoid much of difficulties caused by 2D projections. All techniques mentioned so far can be applied in 3D as a simple extension.
Courtesy of Laura Domine (SLAC grad. student) Presented @ Nu2018
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Introduction to ML@SLAC
Me Laura Domine Tracy Usher
SLAC Reconstruction Developers
ML Focused
Noah SailerSummer Student
Proton DecayGraduate Student
Generic RecoPI/Ass. Scientist
Generic RecoScientist
Generic Reco
orga
nize
r Faculty Knu subgroup lead
HiroStudent mentor
Physics analysis planningPlease feel free to contact me if you are interested in or need help for developing DL techniques. It’s part of DLP activity!
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Next Meeting: Noah’s Summer ReportNoah has studied to apply CNNs for identifying multiple K+ decay modes as well as pµ+ topology to see how well separation can be made and investigate how to improve the performance from “simple” training approach.
Early findings include a sign of the network learning “mono-energetic µ+” and systematic comparison to show improvement by using multiple planes.
µ+ kinetic energy from mis-ID pµ+ events
Courtesy of Noah Sailer Using 2-plane network Delivered @ 5:30AM Today
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K+ decay into π+π0
Image classification can identify this mode almost as good as µ+ decay mode (see Noah’s talk @ next meeting)
Images for Fun (found within 50 events)
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K+ decay into e+π0π0
Pretty hard, in particular e+ radiated right away
Images for Fun (found within 50 events)
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