Publisher's Synopsis
The collection of astronomical data predates by many centuries the invention of the telescope, and since then has grown exponentially, particularly over the last few decades. The result is catalogues and databases comprising billions of objects and many billions of corresponding data. The challenge is to extract meaningful information from these catalogs and databases-information that contributes both to the body of astronomical knowledge and to planning new research.
Automation and current data analysis techniques are the keys to meeting that challenge, and this book is the key to understanding and incorporating these methods into your research. Automated Data Analysis in Astronomy provides an up-to-date overview of modern astronomical catalogs and databases, data analysis techniques, and important forthcoming surveys. Culled from a recent IUCAA-hosted meeting, the papers in this book were contributed by experts from around the world and explore topics such as various automated detection and classification methods, principal component analysis, and artificial neural networks, including their implementation in several real-world studies.
Analyzing the huge amounts of data generated by astronomical studies requires concepts and techniques from disciplines beyond the typical astronomy curriculum. This book presents the opportunity for astronomy students to build some of the background they'll need and learn how teams at some of the leading observatories around the world turn raw data into useful, accessible information.
