Time-stretched multi-hit 3D velocity map imaging of photoelectrons
Bibliographic record
Abstract
The 2D photoelectron velocity map imaging (VMI) technique is commonly employed in gas-phase molecular spectroscopy and dynamics investigations due to its ability to efficiently extract photoelectron spectra and angular distributions in a single experiment. However, the standard technique is limited to specific light-source polarization geometries. This has led to significant interest in the development of 3D VMI techniques, which are capable of measuring individual electron positions and arrival times, obtaining the full 3D distribution without the need for inversion, forward-convolution, or tomographic reconstruction approaches. Here, we present and demonstrate a novel time-stretched, 13-lens 3D VMI photoelectron spectrometer, which has sub-camera-pixel spatial resolution and 210 ps (σ) time-of-flight (TOF) resolution (currently limited by trigger jitter). We employ a kHz CMOS camera to image a standard 40 mm diameter microchannel plate (MCP)/phosphor anode detector (providing x and y positions), combined with a digitizer pick-off from the MCP anode to obtain the electron TOF. We present a detailed analysis of time-space correlation under data acquisition conditions which generate multiple electrons per laser shot, and demonstrate a major advantage of this time-stretched 3D VMI approach: that the greater spread in electron TOFs permits for an accurate time- and position-stamping of up to six electrons per laser shot at a 1 kHz repetition rate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".