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Area of Science:

  • Spectroscopy
  • Data Analysis
  • Image Processing

Background:

  • Cosmic rays introduce high-intensity noise into Raman hyperspectral images.
  • This noise obscures spectral data, hindering downstream analyses.
  • Existing cosmic ray removal methods are often insufficient for this type of data.

Purpose of the Study:

  • To develop and evaluate a novel method for detecting cosmic rays in deep ultraviolet Raman hyperspectral datasets.
  • To adapt and improve upon existing cosmic ray detection techniques from astronomical imaging.

Main Methods:

  • The novel method identifies cosmic rays as outliers in intensity distributions across wavelength channels.
  • It is adapted from cosmic ray removal techniques used in astronomical image processing.
  • The algorithm's performance is assessed across various data conditions.

Main Results:

  • The method effectively identifies cosmic rays in spatially uncorrelated hyperspectral datasets.
  • It outperforms other cosmic ray rejection methods in these specific conditions.
  • Limitations were observed in datasets with high inter-spectral variance or baseline drift.

Conclusions:

  • The developed method offers an effective approach for cosmic ray detection in deep ultraviolet Raman hyperspectral imaging.
  • It shows promise for semi-autonomous implementation in commercial and robotic Raman systems.
  • Further refinement may be needed for datasets with significant spectral variations or drift.