Active Galactic Nuclei (AGN) are among the most energetic and variable objects in the universe, powered by supermassive black holes accreting matter at galactic centers. Understanding their complex variability patterns across different wavelengths and timescales is crucial for unraveling the physics of black hole accretion, but analyzing these multi-dimensional light curves presents significant computational and methodological challenges.
EzTaoX tackles this challenge head-on with a sophisticated framework built on Gaussian Processes, leveraging the speed of JAX for scalable analysis. The toolkit seamlessly handles multi-wavelength observations from different surveys, allowing researchers to model correlated variability patterns across optical, infrared, and other bands. Its modular architecture integrates with modern machine learning workflows through numpyro and jaxopt, while tinygp provides the computational backbone for handling large datasets efficiently.
Developed specifically for the upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), EzTaoX is poised to analyze millions of AGN light curves that will revolutionize our understanding of black hole physics. The framework’s flexibility makes it equally valuable for current surveys like WISE and ZTF, enabling researchers to extract maximum scientific insight from existing and future time-domain astronomy datasets.
⭐ Stars: 16
💻 Language: Python
🔗 Repository: LSST-AGN-Variability/EzTaoX