Abstract

A deep inelastic sample is selected from the nuclear charged current inclusive analysis, by requiring reconstructed $Q^{2} \geq 1.0$ (GeV/c)$^{2}$ and $W \geq 2.0$ GeV/c$^{2}$. The neutrino energy range of $5 < E_{\nu} < 50$ previously applied to this analysis in the Low Energy (LE) analysis is removed and the energy range cut is imposed on the muon energy from $2 < E_{\mu} < 50$ GeV. Machine learning vertexing is used to improve the track-based vertexing resulting in higher purity and efficiency of the sample. A local sideband method is utilized to remove the plastic background for the events reconstructed in the target. After plastic backgrounds and non-DIS backgrounds are subtracted, the reconstructed neutrino energy and Bjorken-x are unsmeared to their true quantities via Bayesian unfolding. The unfolded distributions of carbon, iron, lead, and plastic are efficiency corrected and then divided by the flux and the number of nucleons in the target to produce the absolute cr oss section as a function of $E_{\nu}$ and differential cross section as function of $x_{bj}$. The results of the analysis show good agreement between data and MC for $E_{\nu}$ and $x_{bj}$ (see Figures \ref{fig:xsecEnuC} through \ref{fig:xsecXCH}). None of the distributions displayed significant differences with the theoretical models underlying MINERvAs MC.

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