Anomalous material-dependent transport of focused, laser-driven proton beams
Bibliographic record
Abstract
Intense lasers can accelerate protons in sufficient numbers and energy that the resulting beam can heat materials to exotic warm (10 s of eV temperature) states. Here we show with experimental data that a laser-driven proton beam focused onto a target heated it in a localized spot with size strongly dependent upon material and as small as 35 μm radius. Simulations indicate that cold stopping power values cannot model the intense proton beam transport in solid targets well enough to match the large differences observed. In the experiment a 74 J, 670 fs laser drove a focusing proton beam that transported through different thicknesses of solid Mylar, Al, Cu or Au, eventually heating a rear, thin, Au witness layer. The XUV emission seen from the rear of the Au indicated a clear dependence of proton beam transport upon atomic number, Z, of the transport layer: a larger and brighter emission spot was measured after proton transport through the lower Z foils even with equal mass density for supposed equivalent proton stopping range. Beam transport dynamics pertaining to the observed heated spot were investigated numerically with a particle-in-cell (PIC) code. In simulations protons moving through an Al transport layer result in higher Au temperature responsible for higher Au radiant emittance compared to a Cu transport case. The inferred finding that proton stopping varies with temperature in different materials, considerably changing the beam heating profile, can guide applications seeking to controllably heat targets with intense proton beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".