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Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics

Vilalta, Ricardo and Gupta, Kinjal Dhar and Mahabal, Ashish (2015) Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Artificial Intelligence. No.9286. Springer , Cham, pp. 241-244. ISBN 978-3-319-23460-1. https://resolver.caltech.edu/CaltechAUTHORS:20151113-151109081

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1007/978-3-319-23461-8_22DOIArticle
http://rdcu.be/ttepPublisherFree ReadCube access
ORCID:
AuthorORCID
Vilalta, Ricardo0000-0001-8165-8805
Mahabal, Ashish0000-0003-2242-0244
Additional Information:© 2015 Springer International Publishing Switzerland.
Subject Keywords:Astroinformatics; Domain adaptation; Variable star classification
Series Name:Lecture Notes in Artificial Intelligence
Issue or Number:9286
Record Number:CaltechAUTHORS:20151113-151109081
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20151113-151109081
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:62100
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Nov 2015 00:32
Last Modified:29 Apr 2020 19:40

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