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Title: AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.
Authors:
 [1] ;  [2]
  1. Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile)
  2. Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)
Publication Date:
OSTI Identifier:
22270650
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 777; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; ASTRONOMY; ASTROPHYSICS; CATALOGS; CLASSIFICATION; COMPARATIVE EVALUATIONS; DATA ANALYSIS; ITERATIVE METHODS; PROBABILISTIC ESTIMATION; QUASARS; STATISTICS; VARIABLE STARS