Neural-networks: A tool for gamma-spectroscopy ?

Gabriel T. – GSD

nuclear physics

Neural-networks: What are they ?

Gabriel T. – GSD

Software 2.0

Building block for Industry 4.0

Artificial intelligence is the new electricity

Just another tool in your machine learning toolbox

Is deep learning already hitting its limitations?

Companies will spend $50 billion on artificial intelligence this year

with little to show for it

Amount of data

Per

form

ance

Neural-networks: How do they work ?

Gabriel T. – GSD

neural network

Input Output

Neural-networks: How do they work ?

Gabriel T. – GSD

neural network

Input Output

Features: - model independent- support for high-dimensional data

Neural-networks: How do they work ?

Gabriel T. – GSD

feed-forward

Input Output

connectionsneurons

w02

w11

w01

w12

Neural-networks: How do they work ?

Gabriel T. – GSD

feed-forward

back-propagation

Input Output

connectionsneurons

w02

w11

w01

w12

Neural-networks: How do they work ?

Gabriel T. – GSD

Convolutional classifier:

LSTM module:

Neural-networks: What are they good for ?

Gabriel T. – GSD

Material sample

Zeff

= ?

Classical approach: dual-R Neural network approach

𝐼=𝐼 0 ∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

R≈ln (Attlow)

ln (Att high)≈1𝑍 eff

NNAttlowAtthigh

Z eff

G. V. Turturica, V. Iancu, et al., EPJ Plus 135, 140 (2020)

Neural-networks: What are they good for ?

Gabriel T. – GSD

Material sample

Zeff

= ?

𝐼=𝐼 0 ∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

Simulated Experimental0

0.01

0.02

0.03

0.04

0.05

0.06

Evaluation RMSE

Dual-R ANN

24%48%

G. V. Turturica, V. Iancu, et al., EPJ Plus 135, 140 (2020)

Neural-networks: What are they good for ?

Gabriel T. – GSD

Material sample

Zeff

= ?

Classical approach: dual-R Neural network approach

𝐼=𝐼 0 ∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

R≈ln [Integral (Att low)]

ln [ Integral (Att high)]≈1𝑍 eff

NN Z eff

Atthigh

G. V. Turturica, V. Iancu, C. A. Ur, under review @ NIM A

high-energy

low-energy

high-energy

low-energy

Neural-networks: What are they good for ?

Gabriel T. – GSD

G. V. Turturica, V. Iancu, C. A. Ur, under review @ NIM A

Neural network: discrimination improvement, ½ dose reduction, ½ time reduction

Material sample

Zeff

= ?

𝐼=𝐼 0∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

high-energy

low-energy

high-energy

low-energy

Neural-networks: What are they good for ?

Gabriel T. – GSD

Classical approach: dual-R Neural network approach

R≈ln (Attlow)

ln(Atthigh)NN

Z effAttlow

AtthighG. V. Turturica, V. Iancu, et al., EPJ Plus 135, 140 (2020)

Neural-networks: What are they good for ?

Gabriel T. – GSD

G. V. Turturica, V. Iancu, et al., EPJ Plus 135, 140 (2020)

Neural-networks: What are their limits ?

Gabriel T. – GSD

Material sample

𝐼=𝐼 0∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

Experiment: 19 points

Geant4:378 points

Simulated Experimental0

0.01

0.02

0.03

0.04

0.05

0.06

Evaluation RMSE

Dual-R ANN

24%48%

Neural-networks: What are their limits ?

Gabriel T. – GSD

Material sample

𝐼=𝐼 0∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

Experiment: 19 points

Geant4:378 points

Simulated Experimental0

0.01

0.02

0.03

0.04

0.05

0.06

Evaluation RMSE

Dual-R ANN

24%48%

Pretraining was essential !

Pretraining data

Neural-networks: What are their limits ?

Gabriel T. – GSD

Material sample

𝐼=𝐼 0∙𝑒−𝜇(𝐸 ,𝑍 )𝜌 𝑙

Dual-R NN – experimental dataPretraining

NN – experimental data

Neural-networks: What are their limits ?

Gabriel T. – GSD

Ghost imaging setup:

Compressed sensing:

2000 images

[123, 401, …, 212]

- high complexity input- complex network architecture- large sample size- hard to simulate samples

Neural network:

Neural-networks: What are their limits ?

Gabriel T. – GSD

Ghost imaging setup:

Compressed sensing:

2000 images

[123, 401, …, 212]

- high complexity input- complex network architecture- large sample size- hard to simulate samples

no convergence was achieved

Neural network:

Neural-networks: What we hope they can do ?

Gabriel T. – GSD

Robust automatic spectral analysis:

Automatic geometric and isotopic object identification:

NN

isotopic: 0.73

geometric: 0.99

authentication network

unique item

Neural-networks: A tool for gamma-spectroscopy ?

Gabriel T. – GSD

Advantages Limitations

- limited performance on small sized datasets- hard to train on high complexity input

- can outperform classical algorithms on regression tasks. - relatively easy to generate accurate pretraining data using Monte Carlo simulations