This master's thesis presents an analysis of the reconstruction of \( D^\pm \) mesons and \( \Lambda_c^\pm \) baryons, originating from the $W{+}c$ process, in the ATLAS experiment at the LHC collider, using data collected at a collision energy of \( \sqrt{s} = 13\,\mathrm{TeV} \). The study concentrates on the decays \( D^+ \to K^- \pi^+ \pi^+ \) and \( \Lambda_c^+ \to p K^- \pi^+ \), employing machine learning methods, specifically multilayer neural networks, to distinguish signal events from combinatorial background. The results demonstrate that neural networks provide better signal-background separation compared to traditional cut-based selection methods, especially for the reconstruction of \( D^+ \) mesons. The analysis also highlights the challenges in reconstructing \( \Lambda_c^+ \) baryons, where the shorter lifetime and lower signal-to-background ratio make separation significantly more difficult. These findings are important for improving the modeling of charm quark hadronization in Monte Carlo simulations and for reducing systematic uncertainties in future analyses at the LHC.
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