Astronomy and astrophysics · Paper · June 2023
Correcting Mislabeled Quasars in Extragalactic Catalogs
Quasars, a type of active galactic nuclei (AGNs), are some of the brightest objects in the universe. They allow astronomers to accurately observe distant objects and look farther back in time, offering researchers a better understanding of our universe's history. However, the X-ray and extragalactic databases that catalog astronomical objects often mislabel quasars as other objects. Therefore, I seek to improve these classifications by identifying quasars in the Deep Fields component of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) database and cross-checking them with the extragalactic catalogs. The typical method of classifying an object as a quasar is via manual visual classification: examining unfolded light curves for star-like objects that exhibit irregular short-term and long-term variations in brightness. Unfortunately, this is time-intensive and prone to human error, so I attempted to accelerate the process and boost accuracy with deep neural networks. I first visually classified data of about 4,000 objects for my training set and included four different filters. After preprocessing, I trained a Long Short-Term Memory (LSTM) neural network with different variations of hyperparameters until I achieved an accuracy of 97.5%. When I ran my model through my data, it identified 992 new quasars, 796 of them being actually quasars while the rest were misclassified, yielding an overall binary accuracy of 80% for the entire CHFTLS Deep Field database. Of the identified quasars, 14.8% of which were new quasars and 83.3% were mislabeled in the NASA/IPAC Extragalactic Database (NED). Many of the mislabeled quasars tended to appear as galaxies as well as unidentified sources of ultraviolet or X-ray radiation. In the future, I seek to improve my model for higher accuracy.