Experimental and Computational Approaches to Dark Matter Detection in High-Energy Physics

Abstract

Dark matter constitutes approximately 27% of the universe's mass-energy content, yet its nature remains one of the most profound mysteries in modern physics. Direct and indirect detection of dark matter particles, as well as computational modeling, have become central to advancing our understanding of the cosmos. This research examines both experimental and computational approaches used in high-energy physics to detect dark matter, focusing on the interplay between theoretical predictions, collider experiments, and simulation frameworks. Experimental techniques include direct detection via recoil signals in cryogenic and liquid noble detectors, indirect detection through gamma rays and neutrinos, and collider searches at facilities such as the Large Hadron Collider (LHC). These methods aim to identify Weakly Interacting Massive Particles (WIMPs), axions, sterile neutrinos, and other hypothesized dark matter candidates. Computational approaches employ advanced simulations, Monte Carlo methods, and statistical modeling to predict interaction cross-sections, background noise, and detector responses, enhancing the sensitivity of experiments. Structural equation modeling using SmartPLS was applied to evaluate the relationships between experimental parameters, computational predictions, and detection outcomes. Results indicate significant correlations between optimized detector parameters and the likelihood of dark matter signal detection, demonstrating the value of integrating computational modeling with experimental design. The study highlights the necessity of multi-modal approaches, where experimental data and computational simulations complement one another to refine dark matter models and reduce uncertainties. This integrated strategy not only improves detection sensitivity but also informs theoretical frameworks, narrowing the parameter space for viable dark matter candidates. Future research should focus on next-generation detectors, enhanced computational algorithms, and global collaboration for cross-validation of results. These efforts are critical for resolving fundamental questions about the composition of the universe and for guiding new physics beyond the Standard Model.