Real-time electron clustering in an event-driven hybrid pixel detector
•direct electron detectors produce clusters of pixel hits for each single electron.•a flow-counting algorithm with adaptive buffer size enable real-time analysis.•electron arrival times in continuous beams follow Poisson statistics.•femtosecond multi-electron pulses have anti-correlation effects. Ev...
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| Vydáno v: | Ultramicroscopy Ročník 255; s. 113864 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.01.2024
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| Témata: | |
| ISSN: | 0304-3991, 1879-2723, 1879-2723 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •direct electron detectors produce clusters of pixel hits for each single electron.•a flow-counting algorithm with adaptive buffer size enable real-time analysis.•electron arrival times in continuous beams follow Poisson statistics.•femtosecond multi-electron pulses have anti-correlation effects.
Event-driven hybrid pixel detectors with nanosecond time resolution have opened up novel pathways in modern ultrafast electron microscopy, for example in hyperspectral electron-energy loss spectroscopy or free-electron quantum optics. However, the impinging electrons typically excite more than one pixel of the device, and an efficient algorithm is therefore needed to convert the measured pixel hits to real single-electron events. Here we present a robust clustering algorithm that is fast enough to find clusters in a continuous stream of raw data in real time. Each tuple of position and arrival time from the detector is continuously compared to a buffer of previous hits until the probability of a merger with an old event becomes irrelevant. In this way, the computation time becomes independent of the density of electron arrival and the algorithm does not break the operation chain. We showcase the performance of the algorithm with a ‘timepix’ camera in two regimes of electron microscopy, in continuous beam emission and laser-triggered femtosecond mode. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0304-3991 1879-2723 1879-2723 |
| DOI: | 10.1016/j.ultramic.2023.113864 |