Estimating Photometric Redshifts Using Genetic Algorithms

• Published in: Applications and Innovations in Intelligent Systems XIV pp. 75 - 87
• Main Authors: Miles, Nicholas, Freitas, Alex, Serjeant, Stephen
• Format: Book Chapter
• Language: English
• Published: United Kingdom Springer London, Limited 2006; Springer London
• Subjects: Artificial intelligence; Average Classification Accuracy; Photometric Redshift; Spectroscopic Redshift; Star Formation Rate; Uniform Crossover
• ISBN: 9781846286650; 1846286654
• DOI: 10.1007/978-1-84628-666-7_6

Photometry is used as a cheap and easy way to estimate redshifts of galaxies, which would otherwise require considerable amounts of expensive telescope time. However, the analysis of photometric redshift datasets is a task where it is sometimes difficultto achievea high classification accuracy. This work presents a custom Genetic Algorithm (GA) for mining the Hubble Deep Field North (HDF-N) datasets to achieve accurate IF-THEN classification rules. This kind of knowledge representation has the advantage of being intuitively comprehensible to the user, facilitating astronomers’ interpretation of discovered knowledge. The GA is tested againstthe state of the art decision tree algorithm C5.0 [Rulequest, 2005] in two datasets, achieving better classification accuracy and simplerrule sets in both datasets.