{"paper":{"title":"Machine Learning for Predicting the Proton Structure Function $F_2^P$ in QCD","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ph","authors_text":"Elham Astaraki, Fatemeh Arbabifar, Shahin Atashbar Tehrani","submitted_at":"2026-06-04T17:16:07Z","abstract_excerpt":"We present a comparative study of four supervised machine learning regression algorithms -- Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and Multilayer Perceptron (MLP) -- for predicting the proton structure function $F_2^p(x, Q^2)$ using high-precision BCDMS experimental data. Unlike conventional methods that solve the DGLAP evolution equations, our data-driven framework directly captures the complex nonlinear dynamics of partonic structure. To ensure statistical robustness, we employ $k$-fold cross-validation and perform thorough hyp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06414","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.06414/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}