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# System prepended metadata

title: Auger Electron Spectroscopy

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# Auger Electron Spectroscopy

- Abstract
We present a new approach for analyzing Auger Electron Spectroscopy data of elements and compounds using deep leaning. Also we published for the first time a dataset containing more than 3000 elements and compound. Due to the lack of public dataset, We combined pure element data from Multipak and compound samples, collected with PHI-680. The trained neural network achieved very high accuracy in detecting and characterizing the pure elements. The neural network architecture, data augmentation techniques and loss function are presented and discussed in detail. Our approach is applicable to other spectral data given enough training data.

- Introduction
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- Methods
1. Dataset
3. Neural Network architecture 
4. 
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- Results And Discussion
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- Conclusions
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- Futur work
