Table 2 Fitting results for the raw and predicted NR data.

From: Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry

  

Standard

Low-count

Predicted

Layer 1

SLD/10\(^{-4}\) \(\hbox {nm}^{-2}\)

\(6.79 \pm 0.011\)

\(6.84 \pm 0.090\)

\(6.80 \pm 0.059\)

Thickness/nm

\(62.2 \pm 0.034\)

\(61.8 \pm 0.186\)

\(62.1 \pm 0.228\)

Roughness/nm

\(0.42 \pm 0.013\)

\(0.234 \pm 0.185\)

\(0.243 \pm 0.076\)

Layer 2

SLD / 10\(^{-4}\) \(\hbox {nm}^{-2}\)

\(1.19 \pm 0.010\)

\(0.905 \pm 0.493\)

\(1.13 \pm 0.059\)

Thickness/nm

\(4.35 \pm 0.087\)

\(4.51 \pm 0.292\)

\(4.15 \pm 0.055\)

Roughness/nm

\(1.22 \pm 0.049\)

\(0.904 \pm 0.264\)

\(1.57 \pm 0.275\)

Silicon oxide\(^a\)

SLD / 10\(^{-4}\) \(\hbox {nm}^{-2}\)

3.47

3.47

3.47

Thickness/nm

1.60

1.60

1.60

Roughness/nm

0.40

0.40

0.40

Substrate\(^a\)

SLD / 10\(^{-4}\) \(\hbox {nm}^{-2}\)

2.07

2.07

2.07

Thickness/nm

\(\infty\)

\(\infty\)

\(\infty\)

Roughness/nm

0.30

0.30

0.30

  1. \(^a\)Fixed values in the fitting procedure.
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