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Published September 2015 | Version public
Book Section - Chapter Metadata-only

Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics

Creators

  • 1. ROR icon University of Houston
  • 2. ROR icon California Institute of Technology

Contributors

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.

Additional Information

© 2015 Springer International Publishing Switzerland.

Additional details

Identifiers

Eprint ID
62100
Resolver ID
CaltechAUTHORS:20151113-151109081

Dates

Created
2015-11-18
Created from EPrint's datestamp field
Updated
2021-11-10
Created from EPrint's last_modified field

Caltech Custom Metadata

Series Name
Lecture Notes in Artificial Intelligence
Series Volume or Issue Number
9286