Novel approach to the global analysis of proton form factors in elastic electron-proton scattering
Bibliographic record
Abstract
We present a method for the global analysis of elastic electron-proton scattering when combining data sets from experiments with different overall normalization uncertainties. The method is a modification of one employed by the NNPDF collaboration in the fitting of parton distribution data. This method is an alternative to the ‘penalty trick’ method traditionally employed in global fits to proton electric and magnetic form factors, while avoiding the statistical biases inherent in that approach. We discuss issues that arise when extending the method to nonlinear models. For data with <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msup><a:mi>Q</a:mi><a:mn>2</a:mn></a:msup><a:mo>></a:mo><a:mn>1</a:mn><a:mspace width="0.16em"/><a:msup><a:mtext>GeV</a:mtext><a:mn>2</a:mn></a:msup></a:mrow></a:math> we find relatively minor differences to traditional model fits when the normalization uncertainties from different experiments are correctly accounted for. We discuss implications of this method for the well-known discrepancy between the form factor ratio <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"><c:mrow><c:msub><c:mi>G</c:mi><c:mi>E</c:mi></c:msub><c:mo>/</c:mo><c:msub><c:mi>G</c:mi><c:mi>M</c:mi></c:msub></c:mrow></c:math> extracted from the Rosenbluth and polarization transfer techniques. Published by the American Physical Society 2024
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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 | 0.001 |
| 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".