Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics
Abstract
Astroinformatics is an interdisciplinary field of science that applies modern computational tools to the solution of astronomical problems. One relevant subarea is the use of machine learning for analysis of large astronomical repositories and surveys. In this paper we describe a case study based on the classification of variable Cepheid stars using domain adaptation techniques; our study highlights some of the emerging challenges posed by astroinformatics.
Cite
Text
Vilalta et al. "Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23461-8_22Markdown
[Vilalta et al. "Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/vilalta2015ecmlpkdd-star/) doi:10.1007/978-3-319-23461-8_22BibTeX
@inproceedings{vilalta2015ecmlpkdd-star,
title = {{Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics}},
author = {Vilalta, Ricardo and Gupta, Kinjal Dhar and Mahabal, Ashish},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {241-244},
doi = {10.1007/978-3-319-23461-8_22},
url = {https://mlanthology.org/ecmlpkdd/2015/vilalta2015ecmlpkdd-star/}
}