Modeling quasar variability through self-organizing map-based process

TL;DR

This paper explores using Self-Organizing Maps to preprocess quasar light curves, improving the performance of Conditional Neural Processes in modeling these complex astronomical signals.

Contribution

It demonstrates that applying SOM classification as a preprocessing step enhances the accuracy of QNPy in modeling quasar light curves.

Findings

SOM preprocessing improves QNPy performance.

SOM classification clusters quasar light curves effectively.

Enhanced modeling accuracy after SOM application.

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 SWIFT/BAT data and modeling curves from several clusters, results show the Conditional Neural Process performs better after SOM classification. We conclude that SOM classification of quasar light curves could be a beneficial prepossessing method for QNPy.