Speaker
Description
This contribution explores the application of data science, specifically machine learning techniques, in solving challenges in modern astronomy. The work highlights the use of supervised and unsupervised learning algorithms to analyze large-scale astronomical datasets, such as those generated by space telescopes and observatories.
The study focuses on three key areas:
Identifying patterns in stellar data to classify stars based on their spectral characteristics.
Developing predictive models to estimate exoplanet characteristics using transit data.
Leveraging clustering algorithms for the automatic detection of celestial phenomena such as supernovae or galaxy mergers.
By demonstrating the effectiveness of these methods, the research underscores the potential for data science to advance our understanding of the universe. This work is a step towards integrating machine learning into astronomical research workflows and developing user-friendly tools for astronomers.
| Stream | Science |
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