English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a machine learning method to estimate the physical parameters ofclassical pulsating stars such as RR Lyrae and Cepheid variables based on anautomated comparison of their theoretical and observed light curve parametersat multiple wavelengths. We train artificial neural networks (ANNs) ontheoretical pulsation models to predict the fundamental parameters (mass,radius, luminosity, and effective temperature) of Cepheid and RR Lyrae starsbased on their period and light curve parameters. The fundamental parameters ofthese stars can be estimated up to 60 percent more accurately when the lightcurve parameters are taken into consideration. This method was applied to theobservations of hundreds of Cepheids and thousands of RR Lyrae in theMagellanic Clouds to produce catalogs of estimated masses, radii, luminosities,and other parameters of these stars.

Predicting Physical Parameters of Cepheid and RR Lyrae variables in an Instant with Machine Learning