Reconstructing rotation curves with artificial neural networks
arxiv(2024)
Abstract
Galactic rotation curves have been served as indispensable tools for
determining the distribution of mass within galaxies. Despite several advances
in precision observations, some discrepancies still persist between the
inferred matter distribution from luminosity and observed rotation velocities,
often attributed to the presence of dark matter. Traditional parametric models,
while insightful, struggle with the complexity of galaxies with prominent
bulges or non-circular motions, but in contrast, non-parametric methods offer a
promising alternative, adapting to the intricate nature of rotation curves
without any prior assumptions. In this work, we employ artificial neural
networks to reconstruct rotation curves of 17 spiral galaxies from high-quality
data, demonstrating the efficacy of the non-parametric approaches in
characterizing galactic dynamics. Our findings underscore the importance of
employing diverse methodological approaches to comprehensively understand
galactic dynamics in modern astrophysics research. Moreover, the non-parametric
reconstruction approach with neural networks presents a promising avenue for
further investigations, capable of generating interpolations based on the
intrinsic patterns of the data.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined