Browsing by Author "Čvorović - Hajdinjak, Iva"
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Čvorović - Hajdinjak, Iva (Beograd , 2025)[more][less]
Abstract: This doctoral dissertation addresses the development and application of advanced methods for analyzing the temporal variability of active galactic nuclei (AGN) through the modeling of their optical light curves. The research integrates unsupervised and generative learning techniques, by combining Self- Organizing Maps (SOM) for data preprocessing and Conditional Neural Processes (CNP) for light curve prediction. For the first time in the study of AGN light curves, clustering via SOM has been implemented for preprocessing, alongside the application of CNP for modeling variability. This innovative approach facilitates a more effective modeling of light curves characterized by uneven sampling and missing observations. The QNPy software package was developed and optimized for large-scale parallel processing of extensive time series data. The proposed methodology was validated using light curves from the All-Sky Automated Survey for SuperNovae (ASAS-SN) and the SWIFT/BAT mission, covering a broad range of time scales and variability. The analysis prove that clustering light curves with SOM enhances the performance of neural process, particularly for objects exhibiting simpler variability patterns. The effects of SOM hyperparameters on clustering and prediction performance were carefully examined. The models were validated using loss function and mean squared error evaluations on real data. The proposed methodology shows strong potential for scalable processing of the large time-series data, anticipated in upcoming projects such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, enabling automated classification, anomaly detection, and the extraction of scientifically significant objects from catalogs containing hundreds of millions of sources. URI: http://hdl.handle.net/123456789/5766 Files in this item: 1
doktorska_disertacija_iva_cvorovic_hajdinjak.pdf ( 7.248Mb )
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