||The local environment of a Brightest Cluster Galaxy (BCG) can provide insight into the (still not understood) formation process of the BCG itself. BCGs are the most massive galaxies in the Universe, and their formation and evolution are a popular and current research topic (Linden et al. 2006, Bernardi et al. 2006, Lauer et al. 2006). They have been studied for some time (Sandage 1972, Ostriker & Tremaine 1975, White 1976, Thuan & Romanishin 1981, Merritt 1985, Postman and Lauer 1995, among many others). Our goal in this chapter is to study how the local environment can affect the physical and measurable properties of BCGs. We will conduct an exploratory research exercise.
In this chapter, we will show how the Virtual Observatory (VO) can be effectively utilized for doing modern scientific research on BCGs. We identify the scientific functionalities we need, the datasets we require, and the service locations in order to discover and access those data. This chapter utilizes IDL\'s VOlib, which is described in Chapter 24 of this book and is available at http://www.nvo.noao.edu.
IDL provides the capability to perform the entire range of astronomical scientific analyses in one environment: from image reduction and analysis to complex catalog manipulations, statistics, and publication quality figures. At the 2005 and 2006 NVO Summer Schools, user statistics show that IDL was the most commonly used programming language by the students (nearly 3-to-1 over languages like IRAF, Perl, and Python). In this chapter we show how the integration of IDL to the VO through VOlib provides even greater capabilities and possibilities for conducting science in the era of the Virtual Observatory.
The reader should familiarize themselves with the VOlib libraries before attempting the examples in this tutorial. We first build a research plan. We then discover the service URLs we will need to access the data. We then apply the necessary functions and tools to these data before we can do our analysis. Finally, we conduct the scientific analysis and discuss the results. The code we use for this is scripted so that the analysis can be re-done multiple times. For instance, the researcher might want to adjust the analysis slightly or use new and larger datasets which might become available.
In the following sections, we discuss each step in the process and provide codesnippets that enable a researcher to conduct an entire research project from within a single programming environment. We provide the full code in the software distribution accompanying this book.