The quantitative determination of laser-induced molecular axis alignment
Bibliographic record
Abstract
Experiments in the gas phase usually involve averaging observables over a random molecular axis alignment distribution. This deleterious averaging limits insights gained by probes of molecular dynamics, but can be overcome by prealigning molecular axes using laser-alignment methods. However, the transformation from the laboratory frame to the molecular frame of reference requires quantitative knowledge of the axis alignment distribution. The latter is often hard to obtain directly from experimental data, particularly for polyatomic molecules. Here we describe a general maximum-likelihood classification procedure for non-adiabatic numerical alignment simulations with free parameters that employs experimental data from an alignment-dependent probe. This method delivers (i) the most probable molecular frame angular dependence of the probe, and (ii) the most likely laboratory frame axis alignment distribution of the sample, each with a confidence interval. This procedure was recently used for studies of angle- and channel-resolved strong field ionization of 1,3-butadiene in the molecular frame [Mikosch et al., Phys. Rev. Lett. 110, 023004 (2013)], used here as an illustrative example.
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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) | 0.000 | 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".