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Machine Learning Applied for Spectra Classification

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

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Abstract

Spectroscopy experiment techniques are widely used and produce a huge amount of data especially in facilities with very high repetition rates. In High Energy Density (HED) experiments with high-density materials, changes in pressure will cause changes in the spectral peak. Immediate feedback on the actual status (e.g. time-resolved status of the sample) would be essential to quickly judge how to proceed with the experiment. The two major spectral changes we aim to capture are either the change of intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum.

In this work, we apply recent popular machine learning/deep learning models to HED experimental spectra data classification. The models we presented range from supervised deep neural networks (state-of-the-art LSTM-based model and Transformer-based model) to unsupervised spectral clustering algorithm. These are the common architectures for time series processing. The PCA method is used as data preprocessing for dimensionality reduction. Three different ML algorithms are evaluated and compared for the classification task. The results show that all three methods can achieve 100% classification confidence. Among them, the spectra clustering method consumes the least calculation time (0.069 s), and the transformer-based method uses the most training time (0.204 s).

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Acknowledgement

The authors would like to thank Christian Plueckthun and Zuzana Konopkova at European XFEL for providing the HED experimental spectral data.

This work was supported by China Scholarship Council (CSC). Furthermore, Péter Hegedűs was supported by the Bolyai János Scholarship of the Hungarian Academy of Sciences.

Author information

Authors and Affiliations

  1. University of Szeged, Szeged, Hungary

    Yue Sun, Sandor Brockhauser & Péter Hegedűs

  2. European XFEL GmbH, Schenefeld, Germany

    Yue Sun & Sandor Brockhauser

  3. MTA-SZTE Research Group on Artificial Intelligence, ELKH, Szeged, Hungary

    Péter Hegedűs

Authors
  1. Yue Sun
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  2. Sandor Brockhauser
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  3. Péter Hegedűs
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Corresponding author

Correspondence to Yue Sun .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Sun, Y., Brockhauser, S., Hegedűs, P. (2021). Machine Learning Applied for Spectra Classification. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12957. Springer, Cham. https://doi.org/10.1007/978-3-030-87013-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-87013-3_5

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