Tackling the Challenges of Active Galactic Nuclei Data with Machine Learning Models
Understanding the Complexity of AGN Light Curve Data
Active Galactic Nuclei (AGNs) are among the most luminous and dynamic objects in the universe, characterized by their variable light emissions that provide key insights into the mechanics of galaxy evolution. A fundamental challenge in studying AGNs is the nature of the data collected where the parameters such as time and wavelength are critical. Each observation captures the light curve of an AGN.
However, this data isn't straightforward. Observations are taken using different instruments, like different stations or satellites, leading to variations in data quality and measurement techniques. More critically, there are inevitable gaps in the data, caused by factors ranging from environmental conditions blocking observations to the simple fact that different tools have different operational time frames and capabilities.