\secA{Introduction and getting going quickly}

This document provides a user's guide to \duchamp, an object-finder
for use on spectral-line data cubes. The basic execution of \duchamp\
is to read in a FITS data cube, find sources in the cube, and produce
a text file of positions, velocities and fluxes of the detections, as
well as a postscript file of the spectra of each detection.

So, you have a FITS cube, and you want to find the sources in it. What
do you do? First, you need to get \duchamp: there are instructions in
Appendix~\ref{app-install} for obtaining and installing it. Once you
have it running, the first step is to make an input file that contains
the list of parameters. Brief and detailed examples are shown in
Appendix~\ref{app-input}. This file provides the input file name, the
various output files, and defines various parameters that control the
execution.

The standard way to run \duchamp\ is by the command
\begin{quote}
\texttt{Duchamp -p [parameter file]}
\end{quote}
replacing \texttt{[parameter file]} with the name of the file listing
the parameters. 

An even easier way is to use the default values for all parameters
(these are given in Appendix~\ref{app-param} and in the file
InputComplete included in the distribution directory) and use the
syntax
\begin{quote}
\texttt{Duchamp -f [FITS file]}
\end{quote}
where \texttt{[FITS file]} is the file you wish to search. 

In either case, the program will then work away and give you the list
of detections and their spectra. The program execution is summarised
below, and detailed in \S\ref{sec-flow}. Information on inputs is in
\S\ref{sec-param} and Appendix~\ref{app-param}, and descriptions of
the output is in \S\ref{sec-output}.

\secB{A summary of the execution steps}

The basic flow of the program is summarised here -- all steps are
discussed in more detail in the following sections.
\begin{enumerate}
\item If the \texttt{-p} option is used, the parameter file given on
  the command line is read in, and the parameters absorbed.
\item The FITS image is located and read in to memory.
\item If requested, a FITS image with a previously reconstructed array
  is read in.
\item If requested, BLANK pixels are trimmed from the edges, and
  the baseline of each spectrum is removed.
\item If the reconstruction method is requested, and the reconstructed
  array has not been read in at Step 3 above, the cube is
  reconstructed using the \atrous\ wavelet method.
\item Searching for objects then takes place, using the requested
  thresholding method.
\item The list of objects is condensed by merging neighbouring objects
  and removing those deemed unacceptable.
\item The baselines and trimmed pixels are replaced prior to output.
\item The details of the detections are written to screen and to the
  requested output file.
\item Maps showing the spatial location of the detections are written.
\item The integrated spectra of each detection are written to a
  postscript file. 
\item If requested, the reconstructed array can be written to a new
  FITS file.
\end{enumerate}

\secB{Guide to terminology and conventions}

First, a brief note on the use of terminology in this guide. \duchamp\
is designed to work on FITS ``cubes''. These are FITS\footnote{FITS is
the Flexible Image Transport System -- see \citet{hanisch01} or
websites such as
\href{http://fits.cv.nrao.edu/FITS.html}{http://fits.cv.nrao.edu/FITS.html}
for details.} image arrays with three dimensions -- they are assumed
to have the following form: the first two dimensions (referred to as
$x$ and $y$) are spatial directions (that is, relating to the position
on the sky), while the third dimension, $z$, is the spectral
direction, which can correspond to frequency, wavelength, or
velocity. The three dimensional analogue of pixels are ``voxels'', or
volume cells -- a voxel is defined by a unique $(x,y,z)$ location and
has a unique flux or intensity value associated with it.

Note that it is possible for the FITS file to have more than three
dimensions (for instance, a fourth dimension representing Stokes
parameters). Only the two spatial dimensions and the spectral
dimension are read into the array of pixel values that is searched for
objects. All other dimensions are ignored\footnote{This actually means
that the first pixel only of that axis is used, and the array is read
by the \texttt{fits\_read\_subsetnull} command from the
\textsc{cfitsio} library.}. Herein, we discuss the data in terms of
the three basic dimensions, but you should be aware it is possible for
the FITS file to have more than three. Note that the order of the
dimensions in the FITS file does not matter.

Each spatial pixel (a given $(x,y)$ coordinate) can be said to be a
single spectrum, while a slice through the cube perpendicular to the
spectral direction at a given $z$-value is a single channel (the 2-D
image is a channel map).

Detection involves locating a contiguous group of voxels with fluxes
above a certain threshold. \duchamp\ makes no assumptions as to the
size or shape of the detected features, other than having
user-selected minimum size criteria. Features that are detected are
assumed to be positive. The user can choose to search for negative
features by setting an input parameter -- this inverts the cube prior
to the search (see \S\ref{sec-detection} for details).

Finally, note that it is possible to run \duchamp\ on a
two-dimensional image (\ie one with no frequency or velocity
information), or indeed a one-dimensional array, and many of the
features of the program will work fine. The focus, however, is on
object detection in three dimensions.

\secB{Why \duchamp?}

Well, it's important for a program to have a name, and the initial
working title of \emph{cubefind} was somewhat uninspiring. I wanted to
avoid the classic astronomical approach of designing a cute acronym,
and since it is designed to work on cubes, I looked at naming it after
a cubist. \emph{Picasso}, sadly, was already taken \citep{minchin99},
so I settled on naming it after Marcel Duchamp, another cubist, but
also one of the first artists to work with ``found objects''.