Quantifying the Impact of Dark Matter Distribution on Cosmic Structure Formation: A Methodological Framework

Authors

  • Casey Mitchell Dr. Sc
  • Morgan Green Professor
  • Adrian Robinson Associate Professor

Keywords:

dark matter, cosmic structure formation, astrophysics, galaxy clusters, computational simulations, observational cosmology, density profiles, theoretical models

Abstract

The role of dark matter in cosmic structure formation remains a critical inquiry in contemporary astrophysics. This study presents a novel methodological framework for quantifying the impact of dark matter distribution across various cosmic scales. Employing advanced computational simulations and observational data from the latest telescopes, we analyze the correlation between dark matter density profiles and galaxy cluster formation. Our results indicate a significant deviation from existing models, suggesting that the influence of dark matter is more complex than previously understood. These findings have profound implications for our understanding of the universe’s evolution and the fundamental physics governing cosmic structures. Thus, this research not only refines our theoretical models but also opens new avenues for investigation in both observational and particle physics.

Author Biographies

Casey Mitchell, Dr. Sc

Dr. Sc
Ludwig Maximilian University of Munich
Geschwister-Scholl-Platz 1, 80539 Munich, Germany

Morgan Green, Professor

Professor
California Institute of Technology
1200 E California Blvd, Pasadena, CA 91125, USA

Adrian Robinson, Associate Professor

Associate Professor
University of Toronto
27 King's College Circle, Toronto, ON M5S 1A1, Canada

References

Boynazarov, T., Ryu, D. H., Cho, A. Y., Abbas, H., & Choi, T. (2025). Flexible Hf0. 5Zr0. 5O2/La0. 7Sr0. 3MnO3 Heterostructure by Water-Etching Transfer for Tunable Multilevel RRAM in Neuromorphic Computing. Journal of Alloys and Compounds, 184383.

Boynazarov T, Ryu DH, Cho AY et al (2025) Flexible Hf0.5Zr0.5O2/La0.7Sr0.3MnO3 heterostructure by water-etching transfer for tunable multilevel RRAM in neuromorphic computing. J Alloys Compd 1044:184383. https://doi.org/10.1016/J.JALLCOM.2025.184383

Published

2025-12-24

Issue

Section

Articles