Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract)

Aidan Lytle, Neil Pritchard, Alicia Aarnio, Thomas Weighill

AAAI 2023 pp. 16274-16275

doi:10.1609/AAAI.V37I13.26997 /aaai/2023/lytle2023aaai-toplogical/

Abstract

In our technology-dependent modern world, it is imperative to monitor the Sun for space weather threats to critical infrastructure. Topological data analysis (TDA) is a new set of mathematical techniques used in data analysis and machine learning. We demonstrate that TDA can robustly detect and classify solar surface and coronal activity. This technique is a promising step toward future application in predictive space weather modeling.

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Cite

Text

Lytle et al. "Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26997

Markdown

[Lytle et al. "Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lytle2023aaai-toplogical/) doi:10.1609/AAAI.V37I13.26997

BibTeX

@inproceedings{lytle2023aaai-toplogical,
  title     = {{Toplogical Data Analysis Detects and Classifies Sunspots (Student Abstract)}},
  author    = {Lytle, Aidan and Pritchard, Neil and Aarnio, Alicia and Weighill, Thomas},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16274-16275},
  doi       = {10.1609/AAAI.V37I13.26997},
  url       = {https://mlanthology.org/aaai/2023/lytle2023aaai-toplogical/}
}