XRM2024 - Mon10K - "Dark field x-ray imaging at the femtosecond using a laser driven x-ray source"
Bibliographic record
Abstract
Laser-driven plasma accelerators[1,2,3] are a new concept of electron accelerators, up to 1000 times more compact than conventional ones based on radiofrequency cavities. One of the remarkable features of this type of device is that the electrons wiggle inside the plasma during the acceleration emitting bright femtosecond X-ray flashes[4,5]. This X-ray emission, called betatron radiation, has been successfully used for X-ray imaging, including single shot phase contrast imaging[6]. Here we report on the combination of betatron radiation with a multimodal imaging modality, beam tracking [7], to produce transmission, refraction, and dark-field (scattering) images in a single shot at the femtosecond. Beam-tracking is a robust imaging method applicable to both synchrotron and laboratory x-ray sources. It consists of structuring the X-ray beam into physically separated beamlets using an absorption mask. The three channels, transmission, refraction, and scattering are retrieved from the distortion of the beamlets due to the presence of the sample. The experiment was carried out at the X-ray betatron beamline at the Advanced Laser Light Source in Montreal, Canada. We present the results obtained, we discuss current limitations, prospective applications, and future developments. References [1] Mangles, S. et al., (2004). Nature, 431 (535-538) [2] Geddes, C.G.R. et al., (2004). Nature, 431 (538-541) [3] Faure J. et al., (2004). Nature, 431 (541-544) [4] Kneip S. et al. (2010), Nat. Phys. 6 (980-983) [5] Cipiccia, S. et al., (2011), Nat. Phys. 7 (867-871) [6] Fourmaux, S. et al. (2011). Opt. Lett. 36 (2426) [7] Vittoria, F. A. et al. (2015). Appl. Phys. Lett. 106, (224102) [8] Navarrete Leon C. et al. (2023). Optica 10 (880-887)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".