ASTRA: a Transition-Density-Matrix Approach to Molecular Ionization
Bibliographic record
Abstract
We describe \ASTRA{} (AttoSecond TRAnsitions), a new close-coupling approach to molecular ionization that uses many-body transition density matrices between ionic states with arbitrary spin and symmetry, in combination with hybrid integrals between Gaussian and numerical orbitals, to efficiently evaluate photoionization observables. Within the transition-density-matrix approach, the evaluation of inter-channel coupling is exact and does not depend on the size of the configuration-interaction space of the ions. Thanks to these two crucial features, \ASTRA{} opens the way to studying highly correlated and comparatively large targets at a manageable computational cost. Here, \ASTRA{} is used to predict the parameters of bound and autoionizing states of the boron atom and of the N$_2$ molecule, as well as the total photoionization cross section of boron, N$_2$, and formaldehyde, H$_2$CO. Our results are in excellent agreement with theoretical and experimental values from the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".