A Challenge of Developing a Classifier for Multi-Band Classification of Variable Stars
Xiao-Hui Xu, Qing-Feng Zhu, Xu-Zhi Li, Hang Zheng, Jin-Sheng Qiu

TL;DR
This study investigates the feasibility of creating a multi-band classifier for variable stars using random forests, demonstrating success for some types like RRAB but limitations for others such as Cepheids.
Contribution
The paper presents a novel approach to develop a single classifier applicable across different optical bands and survey data for variable star classification.
Findings
Feasibility of cross-band classification for RRAB variables.
Limitations in classifying Cepheid variables across bands.
Impact of light-curve normalization and feature selection on classification accuracy.
Abstract
Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify various types of variable stars. However, obtaining these classifiers often requires substantial manpower and computational resources. To conserve these resources, it would be best to have a classifier that can be used across surveys. We explore the possibility that a classifier created in one optical band can also work in other bands, likely from different survey facilities. We construct a random forest classifier based on photometric data in ASAS-SN V-band and OGLE I-band, and apply the classifier on ASAS-SN V-band and ZTF r-band light curves of variable star samples. We explore the classification differences of using the magnitude light-curves or…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astrophysics and Star Formation Studies · Astronomy and Astrophysical Research
