Machine Learning:Potential Application for Particle

IdentificationParker Adamson1,2

Advisors: Dr. Michael Youngs2, Dr. Sherry Yennello2

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Forward Array Using Silicon Technology FAUST:

• FAUST has 68 Si/CsI(Tl) ∆E-E telescopes.1

• Energy calibration and spectra linearization to obtain isotopic abundances is very time consuming.

• Reduce the time for isotopic identification.• without jeopardizing the quality of the results.

How?• training neural networks to recognize the isotopic

regions, and create gates.

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1H2H3H 3He 4He 6He

Machine Learning Basics:(Multilayer Perceptron)• Neural Network: A mathematical model

consisting of weights and biases between nodes in the network.

• Node: A structure which takes multiple inputs (from the previous layer multiplied by a weight) and produces a single output (minus a bias).2

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CsI E

Si ΔE

Z

A

Hidden Layer7:5:3

InputLayer

OutputLayer

Neural Network Training:

• Epoch: one epoch is the period in which every data point is checked and the weights and biases are adjusted according to the averaged results of randomized mini batches.

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Figure from3 F. Chollet, "Deep Learning with Python" (2018)

Diagnostics:

• Trained neural networks on SRIM4 simulated 2% resolution data.• 24,990 data points• 750 epochs• NN hidden layer structure of 50:15:5

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Diagnostics:

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Diagnostics:

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Diagnostics:

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Diagnostics:• What is it actually “learning”?• Let’s just give it a rectangle of ‘noise’ and see what it does.• Allow Predicted A±0.5 Z±0.5 for positive isotopic ID.

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Diagnostics:• Allow Predicted A±0.5 Z±0.5 for positive isotopic ID.

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Diagnostics:• Allow Predicted A±0.3 Z±0.5 for positive isotopic ID.

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Diagnostics:• Allow Predicted A±0.1 Z±0.5 for positive isotopic ID.

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Diagnostics:• What if we tighten Z instead?• Allow Predicted A±0.3 Z±0.5 for positive isotopic ID.

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Diagnostics:• Allow Predicted A±0.3 Z±0.3 for positive isotopic ID.

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Diagnostics:• Allow Predicted A±0.3 Z±0.1 for positive isotopic ID.

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Results:• Detector 22 real data (23,294 training data points).• Energy calibrated and linearized to determine isotopic IDs.1

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Results:

• Hidden layer structure:(30:12)

• Trained on detector 22• Tested on detector 22

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Results:

• Hidden layer structure:(30:12)

• Trained on detector 22• Tested on detector 23

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Results:• Can we apply trained networks to other detectors?

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Results:• How can we get the less abundant isotopes?• Apply networks trained on simulated data?

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Conclusions:• Properly identify a large percent• Consistent for detectors from the same experiment• Machine Learning can potentially save time for the linearization

process.

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What’s next:• Improve lower constraints for protons• Explore transfer learning

• Need a smaller identified data set for experimental data.• Improve rare isotope ID’s

• Investigate ways to incorporate energy calibrations

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References:[1] L.A. Heilborn, “Proton-Proton Correlation Functions Measured Using

Position-Sensitive FAUST” Texas A&M Ph.D. Thesis (2018)[2] M. Nielsen, “Neural networks and deep learning” (2019)

http://neuralnetworksanddeeplearning.com[3] F. Chollet, "Deep Learning with Python" (2018)

http://faculty.neu.edu.cn/yury/AAI/Textbook/DeepLearningwithPython.pdf[4] SRIM. www.srim.org

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Questions?

This work was supported by the NSF REU Grant PHY-1659847 the Welch Foundation Grant A-1266 and DOE DE-FG02-93ER40773

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