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Summary
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This study calibrates optical transition edge sensors (TES) by modeling photon detection. A mixture model determines the optimal feature set to accurately estimate photon counts per laser pulse, minimizing uncertainty.

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Area of Science:

  • Physics
  • Optical Engineering
  • Sensor Technology

Background:

  • Accurate calibration of optical transition edge sensors (TES) is crucial for precise photon counting.
  • Existing methods require determining the ratio of deposited photons to total photons per laser pulse.

Purpose of the Study:

  • To develop a robust method for calibrating TES by estimating photon deposition per pulse.
  • To minimize uncertainty in photon count estimation using a data-driven approach.

Main Methods:

  • Modeling photon deposition per pulse as a Poisson process.
  • Utilizing a mixture model to analyze the pulse height spectrum.
  • Employing an uncertainty minimization criterion for optimal feature set selection.

Main Results:

  • The mixture model successfully identified features corresponding to different photon deposition numbers.
  • An optimal feature set was selected from training data based on uncertainty minimization.
  • The method accurately estimated photon deposition and its uncertainty for independent test data.

Conclusions:

  • The proposed mixture model approach provides a reliable method for TES calibration.
  • This technique effectively quantifies photon deposition and associated uncertainties.
  • The uncertainty budget comprehensively includes measurement errors and model-related systematic effects.