Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1943
Title: Upgrading QNPI: Modelling Quasar Light Curves in Large Surveys
Authors: Raju, Aman
Kovačević, Anđelka 
Pavlović, Marina
Ilić, Dragana 
Čvorkić-Hajdinjak, Iva
Popović, Luka Č.
Affiliations: Astronomy 
Astronomy 
Issue Date: 2024
Rank: M34
Publisher: Beograd : Astronomska opservatorija
Related Publication(s): VI Conference on Active Galactic Nuclei and Gravitational Lensing : Program and abstracts, June 2.-6., 2024, Zlatibor
Conference: Conference on Active Galactic Nuclei and Gravitational Lensing(6 ; 2024 ; Zlatibor)
Abstract: 
We build on the LSST-SER-SAG- S1 team’s QNPy (modeling Quasar time series with Neural processes in Python) by integrating Self- Organizing Maps (SOMs) and Attentive Latent Neural Processes to offer a computationally efficient and reliable package to model quasar variability. Harnessing the power of SOMs for clustering and Attentive Latent Neural Processes for features sampled from within the latent space, we present the pilot results of our analysis on several large surveys from the Optical and X-ray bands including the LSST AGN Data Challenge, Gaia, ZTF, and Swift Surveys.
URI: https://research.matf.bg.ac.rs/handle/123456789/1943
DOI: 10.69646/aob24015
Appears in Collections:Research outputs

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