REVIEW 4 cited by
Neural Network Parametrization of Deep-Inelastic Structure Functions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Neural Network Parametrization of Deep-Inelastic Structure Functions
read the original abstract
We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.
Forward citations
Cited by 4 Pith papers
-
Interpreting Parton Distributions with Shapley Values
Exact Shapley values on PDF flavors treat χ² as the cooperative payoff, revealing data constraints and an unexpected intermediate-x gluon insensitivity.
-
Propagating data noise through the fit: the Monte Carlo replica distribution
Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under...
-
The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
Merely mentioning MRI availability in the prompt drives 70-80% of apparent multimodal F1 gains in clinical VLMs, even when no imaging is present.
-
Scheme-invariant stratified factorization algebras for inclusive deep inelastic scattering
Proposes a scheme-invariant stratified factorization algebra framework that derives the DIS convolution formula independently of collinear scheme or operator basis choices.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.