Authors: Pavlović, Marina 
Kovačević, Anđelka
Ilić, Dragana
Čvorivić Hajdinjak, Iva
Popović, Luka Č.
Simić, Saša
Affiliations: Mechanics 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Harnessing Deep Learning for Quasar Light Curve Modeling with QNPy
First page: 60
Conference: XIII Simpozijum "Matematika i primene", 1. i 2. decembar 2023, Beograd
Editors: Miljan Knežević
Aleksandra Delić
Issue Date: 2023
Rank: M34
ISBN: 978-86-7589-185-7
Quasar light curves exhibit intrinsic stochastic variability, which in combination with observational
technical limitations, such as frequent observational gaps and irregular cadences, creates significant challenges for their analysis. To effectively address these challenges, in order to explore quasar underlying physical processes, the common incorporation of deep learning stands out as a key method for efficiently modeling quasar light curves. Here, we present our Python package, now available as ”QNPy” on the PyPI platform, which represents a groundbreaking tool for modeling quasar light curves using meta-learning algorithms which are called conditional neural processes. We demonstrate the first application of the QNPy Python package on two case-study samples sourced from the Data Challenge of the LSST AGN Science Collaboration [ 1] and the GAIA space mission.
Keywords: quasars | time series modeling | computational astronomy | deep learning
Publisher: Beograd : Univerzitet, Matematički fakultet

Show full item record

Page view(s)

checked on May 9, 2024

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.