Modeling quasar variability through self-organizing map-based neural process

Abstract

Conditional Neural Process (QNPy) has shown to be a good tool for modeling quasar light curves. However, given the complex nature of the source and hence the data represented by light curves, processing could be time-consuming. In some cases, accuracy is not good enough for further analysis. In an attempt to upgrade QNPy, we examine the effect of the prepossessing quasar light curves via the Self-Organizing Map (SOM) algorithm on modeling a large number of quasar light curves. After applying SOM on the SWIFT/BAT data and modeling curves from several clusters, results show the Conditional Neural Process performs better after the SOM clustering. We conclude that the SOM clustering of quasar light curves could be a beneficial prepossessing method for QNPy.

Keywords

• methods: data analysis
• galaxy: nucleus
• galaxies: active
• quasars: emission lines