Introduction Experimental Setup Results Conclusion End Matter

SASNets: Classifying Small Angle Scattering

Data Using Convolutional Neural Networks

Chris Wang1, 2

Advisors: William Ratcliff2 and Paul Kienzle2

1Montgomery Blair High School, 2NIST Centre for Neutron Research

August 2, 2017

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Outline

Introduction

Introduction to SANS

Introduction to CNNs

Experimental Setup

Network Design

Classification Task

Results

Results

Conclusion

Conclusion

End Matter

End Matter

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Introduction Experimental Setup Results Conclusion End Matter

Introduction to SANS

Introduction to SANS

▶ Probes matter structure with neutrons

▶ Uses neutron’s special properties

▶ Model > Scattered pattern not invertible

(a) Q vectors1.

(b) Example SANS

result2.

2

A. J. Jackson, Introduction to small-angle neutron scattering and neutron

reflectometry. 2008.

2

SASView Documentation. 3 / 15
Introduction Experimental Setup Results Conclusion End Matter

Introduction to SANS

SANS Data

▶ 1D pattern is integral over all θ and φ

(a) Detector setup. (b) 10 random cylinder models.

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Introduction to CNNs

Introduction to Convolutional Neural Networks

▶ Network of nodes (axons) and connections (synapses).

▶ Convolutional operation on input > spatial invariance.

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Introduction to CNNs

CNN Example

Figure 3: A CNN with features shown.

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Network Design

CNN Design

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Classification Task

Classification Task

(a) 10 random sphere models. (b) 10 random cylinder models.

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Classification Task

Implementation

▶ Implemented random data generation, model training, &

model analysis

▶ Python 2.7, Tensorflow, Keras

Figure 5: Data used in network.

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Results

Classification Results

▶ 54.9% validation accuracy on the 71 model set

▶ Ran for 34 epochs, 2 hours and 30 minutes

▶ Adam optimizer3 using multinominal logistic regression4

Figure 6: Accuracy and Validation graphs

3Kingma and J.Ba, ”Adam: A method for stochastic optimization,” arXiv preprint.

4

S. Menard, Applied logistic regression analysis, vol. 106. Sage, 2002. 10 / 15
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Results

Classification Results

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Results

Classification Results

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Conclusion

Conclusion & Next Steps

▶ Demonstrate CNN can make significant progress on model

classification problem

▶ Implemented network capable of 54.9% accuracy on 71

model set

▶ Found that network finds groups of models from raw data

▶ Current data unrealistic, expand model to real data ranges

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End Matter

Acknowledgements

▶ NIST Centre for Neutron

Research

▶ Centre for High Resolution

Neutron Scattering

▶ The SANS Subproject of

NSF-funded DANSE

DMR-0520547

▶ The many colleagues at

NCNR

▶ William Ratcliff and Paul

Kienzle

▶ NCNR SHIP Directors

▶ NIST

▶ NSF

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End Matter

Questions

Any Questions? Thanks for listening!

More information can be found at sasnets.readthedocs.io.

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