READ ME File For 'Dataset for Phase identification despite amplitude variation in coherent beam combination using deep learning'

Dataset DOI: https://doi.org/10.5258/SOTON/D2344.

ReadMe Author: James A Grant-Jacob  University of Southampton https://orcid.org/0000-0002-4270-4247

This dataset supports the publication:
AUTHORS:James A. Grant-Jacob, Ben Mills and Michalis N. Zervas
TITLE:Phase identification despite amplitude variation in coherent beam combination using deep learning
JOURNAL: Optics Continuum
PAPER DOI IF KNOWN: 


This dataset contains:

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The figures are as follows:

Figure_1.png Fig. 1. Simulated far-field intensity patterns corresponding to different combinations of phase values and amplitude values.

Figure_2.png Fig. 2. Showing a) that the intensity pattern for equal amplitude values is comparable to the mean of the intensity patterns for non-equal amplitude values, with quantitative metrics of b) intersection over union and c) image subtraction.

Figure_3.png Fig. 3. Simulated far-field diffraction patterns from two slits, for changing amplitude and phase values. The dotted green lines are included for assisting with visual comparison.

Figure_4.png Fig. 4. Schematic of the image-to-image network used for transforming simulated diffraction patterns into predictions of phase.

Figure_5.png Fig. 5. Concept for training and testing the neural network. The neural network is trained to identify phase values from simulated far-field intensity patterns. The training data contains intensity patterns corresponding to random phase values and equal amplitude values (the amplitudes are always set to a value of one). The testing data contains intensity patterns corresponding to random phase values and random amplitude values (the amplitudes are chosen from a uniform distribution from zero to one).

Figure_6.png Fig. 6. Showing a) phase, amplitude, and simulated intensity patterns, along with the phase values predicted by the neural network directly from the simulated intensity patterns, and b) the phase prediction error for different numbers of fibres with non-zero amplitude values.

Figure_7.png Fig. 7. Showing phase, amplitude, and simulated intensity patterns, along with the phase values predicted by the neural network for a) cases where the central fibre amplitude is set to zero (i.e., removed), and b) where the positions of the fibres are changed from a 7-fibre hexagonal arrangement to a 5-fibre ‘plus’ arrangement.

Figure_8.png Fig. 8. The capability of the neural network for predicting the phase directly from intensity patterns that correspond to amplitude values with a uniform distribution between zero and one. Showing for each of a-c), thirty examples of simulated intensity patterns corresponding to a fixed phase and random amplitude values, along with the graph showing the phase values predicted by the neural network for each of these thirty intensity patterns. Showing d), the phase prediction error for different amplitudes.

Figure_9.png Fig. 9. The capability of the neural network in identification of the phase values when a phase gradient is present in the simulation. Showing a) the simulated phase profile, the associated simulated focal intensity profile, and the prediction phase profile, for a range of phase gradients, and b) comparison of phase values for each fibre.



Licence: CC-BY

Related projects:
EP/W028786/1
EP/T026197/1
EP/P027644/1


Date that the file was created: 03, 2023