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2 - Observational procedures and data reduction

Published online by Cambridge University Press:  06 August 2010

Evencio Mediavilla
Affiliation:
Instituto de Astrofísica de Canarias, Tenerife
Santiago Arribas
Affiliation:
Space Telescope Science Institute, Baltimore
Martin Roth
Affiliation:
Astrophysikalisches Institut Potsdam
Jordi Cepa-Nogué
Affiliation:
Instituto de Astrofísica de Canarias, Tenerife
Francisco Sánchez
Affiliation:
Centro Astronónomico Hispano-Alemán de Calar, Alto, Spain
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Summary

Introduction

In this chapter, I give an introduction to observing with integral field units and performing basic reduction of the resulting data, prior to scientific analysis. After briefly considering the context of the lectures, I begin by discussing strategies for observing. This is followed by a short tutorial on sampling theory and its application to integral field unit (IFU) data, before continuing with an overview of the requirements for each stage of data reduction. I finish by considering the data reduction process as a whole, along with associated issues such as error propagation and file formats.

Background

Techniques for integral field spectroscopy (IFS) have been in development for at least two decades (Vanderriest, 1980). During the 1980s–1990s, numerous prototype IFUs and even a few public instruments were deployed at observatories and used for scientific work. Nevertheless, IFS has only become widely available at major telescopes during the past five years or so, following two centuries of slit spectroscopy. Experience in observing with IFUs and processing the data is just starting to become commonplace within the community, but will be spread more widely by the current generation of postdoctoral and student astronomers.

In terms of data reduction and analysis, IFS poses some non-trivial new requirements. The most obvious factor is the introduction of 3D datasets to mainstream optical and near-infrared (NIR) (as opposed to radio) astronomy. Although older scanning methods such as Fabry–Perot interferometry produce higher-dimensional datasets, these techniques are relatively specialized by comparison.

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Publisher: Cambridge University Press
Print publication year: 2010

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