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Related Experiment Video

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Real-time electron clustering in an event-driven hybrid pixel detector.

J Kuttruff1, J Holder1, Y Meng1

  • 1Universität Konstanz, Fachbereich Physik, 78464 Konstanz, Germany.

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|October 15, 2023
PubMed
Summary

A new clustering algorithm efficiently processes raw data from hybrid pixel detectors, enabling real-time identification of single-electron events in ultrafast electron microscopy. This advancement is crucial for advanced techniques like hyperspectral electron-energy loss spectroscopy.

Keywords:
Cluster algorithmDirect electron detectorFemtosecond electron pulsesPoisson statistics

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Area of Science:

  • Physics
  • Materials Science
  • Instrumentation

Background:

  • Event-driven hybrid pixel detectors offer nanosecond time resolution for advanced electron microscopy.
  • Accurate single-electron event detection is challenging due to multi-pixel excitation by impinging electrons.

Purpose of the Study:

  • To develop a robust and fast clustering algorithm for real-time processing of raw detector data.
  • To enable efficient conversion of multi-pixel hits into single-electron events for ultrafast electron microscopy.

Main Methods:

  • A novel clustering algorithm compares detector hits (position and time) against a buffer of previous events.
  • The algorithm determines event mergers based on the probability of relevance, ensuring computational efficiency.
  • The algorithm was tested using a 'timepix' camera in continuous and femtosecond laser-triggered electron microscopy modes.

Main Results:

  • The developed algorithm achieves real-time processing of continuous data streams.
  • Computation time is independent of electron arrival density, preventing operational bottlenecks.
  • Successful demonstration in both continuous beam and femtosecond laser-triggered electron microscopy regimes.

Conclusions:

  • The presented clustering algorithm is a robust solution for real-time single-electron event identification in ultrafast electron microscopy.
  • This facilitates advanced applications such as hyperspectral electron-energy loss spectroscopy and free-electron quantum optics.
  • The algorithm's efficiency and independence from data density ensure its broad applicability in high-speed electron detection.