Understanding of the properties of neural network approaches for transient light curve approximations

• Published in: Astronomy and astrophysics (Berlin) Vol. 677; p. A16
• Main Authors: Demianenko, Mariia, Malanchev, Konstantin, Samorodova, Ekaterina, Sysak, Mikhail, Shiriaev, Aleksandr, Derkach, Denis, Hushchyn, Mikhail
• Format: Journal Article
• Language: English
• Published: Heidelberg EDP Sciences 01.09.2023
• Subjects: Approximation; Celestial bodies; Classification; Gaussian process; Light curve; Luminosity; Mathematical analysis; Multilayer perceptrons; Neural networks; Normalizing (statistics); Outliers (statistics); Photometry; Supernovae; Time series; Transients (astronomy)

Context. Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and mo...