Dark energy and Dark matter

This study presents an innovative theoretical approach to dark energy and dark matter, which are key components in explaining the expansion of the universe and the dynamics of large-scale structures. While dark energy drives the accelerated expansion of the universe, dark matter plays a critical role in the formation of galaxy clusters and the underlying structure of the cosmos. The key aspects of the study include: 1. Tensor-Based Energy Cycles: A tensor-based mathematical model is developed to elucidate the relationship between dark energy and dark matter. This model utilizes energy gradients and spacetime deformation effects to predict the density of dark energy. 2. Deformation Constant (de): The deformation constant (de) is introduced as an innovative parameter to calculate the cosmological effects of energy transformations. 3. Quantum and Cosmological Connections: Dark energy and dark matter are explored as bridges between quantum field theory and general relativity. Gradient-based calculations suggest a direct relationship between dark energy and dark matter. 4. Observational Data: The model is validated against observational datasets, including the Planck Legacy Archive (CMB data) and the Dark Energy Survey (DES). The results demonstrate alignment with the universe's expansion rate and the density distribution of dark matter. 5. Particle-Independent Approach: The study proposes a framework that relies solely on energy densities, providing an alternative to particle-based models for dark matter and dark energy. This simplifies the understanding of their physical mechanisms. Conclusion This research offers a novel perspective on dark energy and dark matter, introducing tensor-based energy cycles and the deformation constant as key concepts. These innovations provide a robust theoretical framework to explain the relationship between these two phenomena. The study's findings are consistent with observational data, underscoring the potential cosmological implications of the proposed model.

The author acknowledges the use of OpenAI’s language model for assistance in drafting certain sections of this workl