neutrino interaction classification with the dune ......2020/03/04 · monsalve dune collaboration...

Neutrino interaction classification with the DUNE

Convolutional Visual Network

Leigh Whitehead, Saúl Alonso-Monsalve

DUNE Collaboration Call3 April 2020


Purpose of the paper

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• Paper of the CVN Particle ID used in the TDR sensitivities.• Title: “Neutrino interaction classification with the DUNE Convolutional

Visual Network.”• Primary authors: Leigh Whitehead, Saúl Alonso Monsalve.• ARC: Alex Himmel, Andy Blake, Dan Cherdack, Andrea Scarpelli, Taritree

Wongjirad.• DUNE-doc-14125.

• Target journal: Physical Review D (PRD).• Deadline of the review: Monday, April 13th.


Overview

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• 1. Introduction to DUNE.• CP-violation measurement.• DUNE FD simulation and reconstruction.

• 2. CVN neutrino interaction classifier.• CVN inputs, outputs, and network architecture.• Training details.

• 3. Neutrino flavor identification performance.• Focus on CC νe and CC νμ selections.• Efficiencies of 90% for CC νe and 95% for CC νμ.

• 4. Exclusive final state results.• Results using the CVN outputs that count the number of final-state particles for:

protons, charged pions, and neutral pions.• 5. Robustness.

• Evaluate the CVN performance as a function of different observable physics parameters.

• 6. Conclusion.


Introduction to DUNE

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• Gives an introduction to neutrino physics, the experiment and the TDR CP-violation analysis.

• We include two plots of the event selection from the TDR.

• These are the CC veand CC ve events fora range of dCP values.


CVN neutrino interaction classifier

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• This section describes the details of the CVN.• The architecture including all of the inputs and outputs.• Details of the training sample and methods.

• Example input images of signal and background events.


CVN neutrino interaction classifier

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• We include a schematic diagram of the network architecture.

• Provide full details of the training procedure and the samples used.

• Plots of the loss and accuracy during the training process.


Neutrino flavor identification performance

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• This section describes the main result of the paper.• Corresponds directly to the results shown in the TDR.

• We show the distribution of the CVN classifier score for the ve and vuhypotheses for FHC and RHC beams.



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• This section describes the main result of the paper.• Corresponds directly to the results shown in the TDR.

• We show the distribution of the CVN classifier score for the ve and vuhypotheses for FHC and RHC beams.



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• The “final result” shown in this paper is the selection efficiency using the CVN compared to the CDR analysis.



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• The “final result” shown in this paper is the selection efficiency using the CVN compared to the CDR analysis.


Exclusive final state results

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• This section describes the potential of the other CVN outputs that count the number of final state particles.• This goes beyond what was used in the TDR analysis.• Provides a clear proof-of-principle for sub-dividing the FD event

selection in the future.


Robustness

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• This section is aimed at convincing the reader that we understand what the CVN is doing and that it behaves in an expected way as a function of different physics parameters.

• For example, we see that the selection efficiency drops as a we increase the hadronic energy in the system.


Robustness

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• This section is aimed at convincing the reader that we understand what the CVN is doing and that it behaves in an expected way as a function of different physics parameters.

• Similarly, we see NC background events with higher acceptance as the pion energy increases.


Conclusion

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• Reiterate the impressive performance of the CVN and that it is a robust classification algorithm.

• Suggest, dependent on further studies, that the particle counting outputs have the potential to increase the sensitivity further.


Public data release

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• Gitlab project:• https://gitlab.cern.ch/salonsom/cvn-paper.

• The code runs the CVN over a small sample.• The sample consists of 20 random MC events.• There is a README file available with a detailed description of the code.• The testing script produces a file called results.txt. This can be compared

to ./output/expected_results.txt to ensure the code has executed correctly.

https://gitlab.cern.ch/salonsom/cvn-paper

Mechanics of the review

Leigh Whitehead, Saúl Alonso-Monsalve 16

• We’re currently about half-way through the review period. • Comments are due back on Monday, April 13th.

• Send to [email protected], [email protected], [email protected].

• In doc-14125 you can find a draft of the paper as well as a spreadsheet template for comments.• Please fill in comments in the appropriate tab depending on what

the comments is on along with the column marking line, fig #, etc.• Put an X in the “Minor?” column if you don’t feel you need a

response to your comment from the authors.

Neutrino interaction classification with the DUNE

Convolutional Visual Network


DUNE Collaboration Call3 April 2020

neutrino interaction classification with the dune ......2020/03/04 · monsalve dune collaboration...

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