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The purpose of this ABC Guide to
XMM-Newton data analysis is to provide
a simple walk-through of basic data extraction and analysis tasks.
Also included is a guide to references and help available to aid in
the analysis of the data. We have tried to balance providing enough
information to give the user a useful introduction to a variety of
analysis tasks with not providing too much information, which would make
a guide like this too ponderous to use. As such, there is no intention
to replace the SAS Handbook, which should be considered the highest
authority for the use of SAS. Therefore this document will not display
the full versatility of the SAS tasks, and of SAS itself, but it will
hopefully show a path through the forest.
Chapter 2 provides lists of web-based references for the
XMM-Newton project, help desks, analysis guides, and science and
calibration data. Chapter 3 provides a description
of the data files provided for observation data sets.
Chapter 5 discusses the installation and use of SAS.
Chapter 4 provides a small amount of background for users
who may be unfamiliar with X-ray astronomy and aims to put some of
the topics they will encounter in later chapters in context.
The rest of the chapters discuss the analysis of EPIC, RGS, and OM data
respectively, taking the detector mode and SAS interface (command line
or GUI) into account. We also include brief introductions to analysis
of the reduced data with XSPEC and Xronos.
This document will continue to evolve. Updated versions will be made
available on our web site at:
http://heasarc.gsfc.nasa.gov/docs/xmm/abc/
This guide would not have been possible without the help and comments
from all people involved in the XMM-Newton project. In particular, we
would like to thank Giuseppe Vacanti and Julian Osborne whose comments
made this a more complete and accurate document.
IMH wishes to thank all the OM calibration team and in particular
Antonio Talavera, Matteo Guainazzi and Bing Chen for their help in
the preparation of this and other documents related to the OM.
SLS wishes to thank Dave Lumb, Richard Saxton, and Steve Sembay for their
helpful insights into EPIC data analysis.