2D monochromatic x-ray imaging for beam monitoring of an x-ray free electron laser and a high-power femtosecond laser
Bibliographic record
Abstract
In pump-probe experiments with an X-ray Free Electron Laser (XFEL) and a high-power optical laser, spatial overlap of the two beams must be ensured to probe a pumped area with the x-ray beam. A beam monitoring diagnostic is particularly important in short-pulse laser experiments where a tightly focused beam is required to achieve a relativistic laser intensity for generation of energetic particles. Here, we report the demonstration of on-shot beam pointing measurements of an XFEL and a terawatt class femtosecond laser using 2D monochromatic Kα imaging at the Matter in Extreme Conditions end-station of the Linac Coherent Light Source. A thin solid titanium foil was irradiated by a 25-TW laser for fast electron isochoric heating, while a 7.0 keV XFEL beam was used to probe the laser-heated region. Using a spherical crystal imager (SCI), the beam overlap was examined by measuring 4.51 keV Kα x rays produced by laser-accelerated fast electrons and the x-ray beam. Measurements were made for XFEL-only at various focus lens positions, laser-only, and two-beam shots. Successful beam overlapping was observed on ∼58% of all two-beam shots for 10 μm thick samples. It is found that large spatial offsets of laser-induced Kα spots are attributed to imprecise target positioning rather than shot-to-shot laser pointing variations. By applying the Kα measurements to x-ray Thomson scattering measurements, we found an optimum x-ray beam spot size that maximizes scattering signals. Monochromatic x-ray imaging with the SCI could be used as an on-shot beam pointing monitor for XFEL-laser or multiple short-pulse laser experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".