Abstract
Machine learning can be utilized to classify spectra flagged as Active Galactic Nuclei (AGNs) belonging to Seyferts or Quasars, expediting data collection and aiding in analyzing the AGN types. While many properties of Seyferts and Quasars can be used as feature points in training a machine learning model, one relatively available property with high information density is the spectra of the AGN types. This paper aims to describe the training and results of a K-Nearest Neighbors and a Dense Neural Network machine learning model built to classify AGNs as Seyfert type 1s, Seyfert type 2s, or Quasars.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
