Bibliographic record
Abstract
A series of previous papers [1] develops a dipole model in initial state impact parameter space that includes subleading effects such as running αs, unitarity, confinement and saturation. Here some recent work [2] is presented, where the model is applied to a new set of data: vector meson production in γ ⋆ p, DVCS and dσ/dt in pp. This allows us to tune a more realistic model of the proton wavefunction from the pp data, and confirm the predictive power of the model in high Q 2 of DVCS and vector meson production. For low Q 2 vector meson resonances dominate the photon wavefunction, making our predictions depend on a tuned parametrisation in this range. LU-TP 09-03 MCnet/09/02 To calculate cross sections for hadronic particles it is important to understand the evolution in the initial state. In a high energy collision, each of the two incoming particles will emit gluons before meeting and interacting. Enumerate the possible initial states with i,j and give each state a probability wi such that ∑ i wi = 1. With a scattering probability pij between state i and j the total interaction probability can be expressed as Ttot(b) = 2 ∑ wiwjpij. (1) That means that the expectation value of pij, weighted by wi can be measured. Similarly the diffractive, including elastic, cross section is ij Tdiff(b) = ∑ ij wiwjp 2 ij. (2) To get both these cross sections right, not only the expectation value of pij with respect to wi is required, but also the fluctuations. That is, it is possible to measure if the cross section is dominated by frequently occuring states with a low interaction probability, giving a low Tdiff/Ttot, or by rare states with a high interaction probability, giving a high Tdiff/Ttot. Also the elastic interaction probability can be written in this way as Tel(b) = ⎝ ∑ ij
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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".