1 research outputs found
Toward an understanding of the properties of neural network approaches for supernovae light curve approximation
The modern 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. Most of the objects have never received a
spectroscopic follow-up, which is especially crucial for transients e.g.
supernovae. In such cases, observed light curves could present an affordable
alternative. Time series are actively used for photometric classification and
characterization, such as peak and luminosity decline estimation. However, the
collected time series are multidimensional, irregularly sampled, contain
outliers, and do not have well-defined systematic uncertainties. Machine
learning methods help extract useful information from available data in the
most efficient way. We consider several light curve approximation methods based
on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and
Normalizing Flows, to approximate observations of a single light curve. Tests
using both the simulated PLAsTiCC and real Zwicky Transient Facility data
samples demonstrate that even few observations are enough to fit networks and
achieve better approximation quality than other state-of-the-art methods. We
show that the methods described in this work have better computational
complexity and work faster than Gaussian Processes. We analyze the performance
of the approximation techniques aiming to fill the gaps in the observations of
the light curves, and show that the use of appropriate technique increases the
accuracy of peak finding and supernova classification. In addition, the study
results are organized in a Fulu Python library available on GitHub, which can
be easily used by the community.Comment: Submitted to MNRAS. 14 pages, 6 figures, 9 table