Speaker
Description
This study addresses the inefficiencies of existing Fourier analysis software for astronomical time-series data, such as Period04 and FAMIAS, which typically require file-by-file processing. The aim was to develop a scalable and automated solution for batch processing large datasets in order to save time and improve reproducibility. To achieve this, a Python-based Fourier analysis pipeline was designed and implemented using scientific libraries such as NumPy, SciPy, and Astropy. The workflow includes discrete Fourier transform computation, automated peak-frequency detection and validation, False Alarm Probability estimation through Monte Carlo simulations, and output visualization. The developed pipeline successfully automates the Fourier analysis process and enables the efficient processing of multiple files. Its results agree with Period04 within computational tolerance, with a mean percentage difference of approximately 1.1%. This represents a substantial improvement over the manual approaches used in Period04 and FAMIAS, reducing total analysis time and removing user-dependent inconsistencies. Overall, the Python-based pipeline provides a fast, scalable, and reproducible alternative for Fourier analysis in astronomy, and is particularly well suited to large datasets, such as light curves from hundreds of stars, making it valuable for automated workflows in fields such as asteroseismology.
| Apply for student award at which level: | Honours |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |