High order transient absorption spectroscopy: a new technique for probing multiple excitations
Bibliographic record
Abstract
Transient absorption (TA) spectroscopy gives dynamical information about excited states and spectral information about their excitations. If the pump pulses are strong, then multiple excitations can be produced, and the signal has contributions from single excitations mixed with those from multiple excitations. I will describe a new technique in which TA spectra are acquired at several pump intensities, enabling extraction of high signal-to-noise TA spectra and systematically separated high-order spectra [1,2]. I will show the spectral and dynamical information in high-order spectra. The higher orders contain information both about multiply excited states and singly excited states that are usually dark. I will give an intuitive taxonomy of the response pathways that characterize these high-order signals, extending the standard TA pathways -- stimulated emission, excited-state absorption, and ground-state bleach -- to higher orders. I will show examples from several molecular and solid-state systems. [1] Malý, Lüttig, Rose, Turkin, Lambert, Krich, and Brixner, Nature 616 280 (2023) [2] Lüttig, Rose, Malý, Turkin, Bühler, Lambert, Krich, and Brixner, J Chem Phys 158 234201 (2023)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".