arXiv subjects

Elham Astaraki

Publications and source records attributed to Elham Astaraki.

2 recordsLinked to original sources

arXiv2026

Laplace-Space Analysis of $xF_3$ Including Nuclear Effects and Gegenbauer-Polynomial Parton Distributions

We present a next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) QCD analysis of the non-singlet structure function $xF_3$, utilizing a Gegenbauer-polynomial representation for the input parton distribution functions (PDFs). The main objective is to assess how this flexible parameterization improves the extraction of valence quark distributions in the presence of nuclear effects. To this end, we incorporate nuclear modification factors into the input PDFs for heavy nuclear targets and solve the DGLAP evolution equations analytically in Laplace space. The structure function in Bjorken-$x$ space is then reconstructed using a Jacobi polynomial expansion. This combined framework enables a systematic investigation of the $xF_3$ data from the CCFR, NuTeV, and CHORUS experiments at both NLO and NNLO accuracies. We further examine the sensitivity of the extracted distributions to nuclear corrections and discuss their implications for the Gross--Llewellyn Smith, Bjorken, and Adler sum rules. Our results demonstrate that the Gegenbauer-polynomial PDF formalism provides a flexible and efficient framework for describing nuclear $xF_3$ data, yielding improved phenomenological consistency across a wide kinematic range.

hep-ph↗

arXiv2026

Machine Learning for Predicting the Proton Structure Function $F_2^P$ in QCD

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 hyperparameter optimization. Our results show that the MLP and GPR models achieve superior predictive accuracy. In particular, MLP exhibits the highest sensitivity to nonlinear gradients, while SVR proves most stable against experimental uncertainties. The close convergence of training and validation metrics confirms that the models learn the underlying QCD physics without overfitting to statistical fluctuations. This work highlights the potential of ML-based regression as a complementary tool for structure function analysis and kinematic extrapolation in high-energy physics.

hep-ph↗