\label{sec:single}

\subsection{In a nutshell}

\begin{verbatim}
  1  read uv YourData
  2  uv_stat
  3  uv_map
  4  clean
  5  view clean
  6  write * YourResult
\end{verbatim}
\begin{enumerate}\itemsep 0pt
\item Read your \uv{} data
\item Have a look at its header, and get recommendations on
image characteristics.
\item Image it
\item Deconvolve
\item see the result
\item Save the result if OK.
\end{enumerate}
You are done. And this is often good. However, it may take a while, and
the angular resolution and/or the brightness sensitivity may not be optimal.
So, it may be worth for you to read the information below and adjust
the control variables of \com{UV\_MAP} 

\subsection{Measurement equation and other definitions}
\label{sub:single:principle}

The measurement equation of an instrument is the relationship between the
sky intensity and the measured quantities. The measurement equation for a
millimeter interferometer is to a good approximation (after calibration)
\begin{latexonly}  
$$
  V(u,v) = \mbox{FT}\cbrace{B_\mathrm{primary}.I_\mathrm{source}}(u,v)+N
$$
\end{latexonly}
\begin{rawhtml}  
$$
  V(u,v) = \mbox{FT}\left(B_\mathrm{primary}.I_\mathrm{source}\right)(u,v)+N
$$
\end{rawhtml}
where $\mbox{FT}({F}(u,v))$ is the bi-dimensional Fourier transform of
the function $F$ taken at the spatial frequency $(u,v)$, 
$I_\mathrm{source}$
the sky intensity distribution, $B_\mathrm{primary}$ the primary beam of the
interferometer (almost a Gaussian whose FWHM is the natural resolution of the
single-dish antenna composing the interferometer), $N$ some thermal noise
and $V(u,v)$ the calibrated visibility at the spatial frequency $(u,v)$.
This measurement equation implies different kinds of problems.
\begin{enumerate}
\item The presence of noise leads to sensitivity problems.
\item The presence of the Fourier transform implies that visibilities
  belongs to the Fourier space while most (radio)astronomers are used to
  interpret images. A step of \emph{imaging} is thus required to go
  from the \uv{} plane to the image plane.
\item The multiplication of the sky intensity by the primary beam implies a
  distortion of the information about the intensity distribution of the
  source.
\item Finally, the main problem implied by this measurement equation is
  certainly the irregular, limited sampling of the \uv{} plane because it
  implies that the information about the source intensity distribution is
  incomplete.
\end{enumerate}
Deconvolution techniques are needed to overcome the incomplete
sampling of the \uv{} plane. To show how this can be done, we need additional definitions
\begin{itemize}
\item Let us call $V = \mbox{FT}({B_\mathrm{primary}.I_\mathrm{source}})$ the
  continuous visibility function.
\item The sampling function $S$ is defined as
  \begin{itemize}
  \item $S(u,v) = 1/\sigma^2$ at $(u,v)$ spatial frequencies where
    visibilities are measured by the interferometer. $\sigma{}$ is the rms
    noise predicted from the system temperature, antenna efficiency,
    integration time and bandwidth. The sampling function thus contains
    information on the relative weights of each visibility.
  \item $S(u,v) = 0$ elsewhere.
  \end{itemize}
\item We finally call $B_{\mathrm{dirty}} = \mbox{FT}^{-1}(S)$ the dirty
  beam.
\end{itemize}
If we forget about the noise, we can thus rewrite the measurement equation
as
\begin{latexonly}
$$  I_\mathrm{dirty} = \mbox{FT}^{-1} \cbrace{S.V}. $$
\end{latexonly}
\begin{rawhtml}
$$  I_\mathrm{dirty} = \mbox{FT}^{-1} \left(S.V\right). $$
\end{rawhtml}

Using the property \#1 of the Fourier transform (see Appendix), we obtain
\begin{latexonly}
$$  I_\mathrm{dirty} = B_\mathrm{dirty} \ast \cbrace{B_\mathrm{primary}.I_\mathrm{source}}, $$
\end{latexonly}
\begin{rawhtml}
$$  I_\mathrm{dirty} = B_\mathrm{dirty} \ast \left(B_\mathrm{primary}.I_\mathrm{source}\right), $$
\end{rawhtml}
where $\ast$ is the convolution symbol. Thus, the incompleteness of the
\uv{} sampling translates into the image plane as a convolution by the dirty
beam, implying the need of deconvolution. From the last equation, it is
easy to show that the dirty beam is the point spread function of the
interferometer, \ie\ its response at a point source. Indeed, for a point
source at the phase center, 
\begin{latexonly}
$({B_\mathrm{primary}.I_\mathrm{source}}) = I_{\mathrm{point}}$ 
\end{latexonly}
\begin{rawhtml}
$$({B_\mathrm{primary}.I_\mathrm{source}}) = I_{\mathrm{point}}$$ 
\end{rawhtml}
at the phase center and 0 elsewhere and the convolution with
a point source is equal to the simple product: 
\begin{latexonly}
$I_{\mathrm{dirty}} = B_{\mathrm{dirty}}.I_{\mathrm{point}} = B_{\mathrm{dirty}}$ 
\end{latexonly}
\begin{rawhtml}
$$I_{\mathrm{dirty}} = B_{\mathrm{dirty}}.I_{\mathrm{point}} = B_{\mathrm{dirty}}$$
\end{rawhtml}
for a point source of intensity $I_{\mathrm{point}} = 1$~Jy.

We note that Fourier transform are in general done through Fast Fourier 
Transform, which implies first a stage of re-interpolation of the 
visibilities on a regular grid in the \uv{} plane, a process called 
\textit{gridding}. This gridding step introduces a convolution in the 
\uv{} space, and thus a multiplication by the Fourier transform of the 
gridding function in the image plane, which needs to be corrected
later by division by this Fourier transform. It can be shown that despite this 
step, the convolution property mentionned before still holds.
 
\subsection{Imaging}
\label{sub:single:imaging}

The process known as {\it imaging} consists in computing the dirty image
and the dirty beam from the measured visibilities and the sampling
function. 

\subsubsection{Image size and pixel size}


\paragraph{Link between image size and \uv{} cell size}

The gridding stage requires at least Nyquist sampling of the \uv{} 
plane to avoid the artifact known as aliasing. This sampling
depends on the source size. 

For the signal, the source size is limited by the primary beam, so that the 
Nyquist sampling in the \uv{} plane is obtained with a size of the grid 
cells equals to half the size of the antenna diameter. (this is the 
smallest spatial frequency that the interferometer can be sensitive to, 
\ie\ the natural resolution in the \uv{} plane). In the image plane, 
this implies to make an image at least twice as large as the primary 
beam size (see Fourier transform property \#2 in Appendix). 

Unfortunately, the spatial frequencies of the noise are not
bound:  the noise increases at the edges of the produced image 
because of the noise aliasing and gridding correction.

Unless you have good reasons (such as a strong confusion source close
to the primary beam), you should not choose too large an image size,
since that would slow down imaging and deconvolution.

\paragraph{Link between pixel size and largest \uv{} spatial frequency}

The largest sampled spatial frequency is directly linked to the synthesized
beam size (\ie\ the interferometer spatial resolution). The pixel size must
be at least 1/2 the synthesized beam size to ensure Nyquist sampling in the
image plane. However, Nyquist sampling is enough only when dealing with
linear processes while deconvolution techniques are non-linear.  It
is thus recommended to select a pixel size between 1/3 and 1/4 of the synthesized
beam to ease the deconvolution. Smaller pixel sizes would lead
to larger images, and unduly slow down the imaging and deconvolution process.

\subsubsection{Weighting and Tapering}

The use of the visibility weights $(1/\sigma^2)$ in the definition of the
sampling function is called natural weighting as it is natural to weight
each visibility by the inverse of noise variance. Natural weighting is also
the way to maximize the point source sensitivity in the final image.
However, the exact scaling of the sampling function is an additional degree
of freedom in the imaging process. In particular, the user may change this
scaling to give more or less weight to the long or short spatial frequencies.

We can thus introduce a weighting function $W(u,v)$ in the definitions of
$B_{\mathrm{dirty}}$ and $I_{\mathrm{dirty}}$
\begin{latexonly}
$$  B_\mathrm{dirty} = \mbox{FT}^{-1} \cbrace{W.S} $$
\end{latexonly}
\begin{rawhtml}
$$  B_\mathrm{dirty} = \mathrm{FT}^{-1} \left(W.S\right) $$
\end{rawhtml}
and
\begin{latexonly}
$$  I_\mathrm{dirty} = \mbox{FT}^{-1} \cbrace{W.S.V} $$
\end{latexonly}
\begin{rawhtml}
$$  I_\mathrm{dirty} = \mathrm{FT}^{-1} \left(W.S.V\right) $$
\end{rawhtml}
There are two main categories of weighting functions
\begin{description}
\item[Robust weighting] In this case, $W$ is computed to enhance the
  contribution of the large spatial frequencies. This is done by first
  computing the natural weight in each cell of the \uv{} plane. Then $W$ is
  derived so that
  \begin{itemize}
  \item The product $W.S$ in a \uv{} cell is set to a constant if the
    natural weight is larger that a given threshold;
  \item $W = 1$ (\ie{} natural weighting) otherwise.
  \end{itemize}
  This decreases the weight of the well measured \uv{} cells (\ie{} very
  low noise cells) while it keeps natural weighting of the noisy cells.  It
  happens that the cells of the outer \uv{} plane (corresponding to the
  large interferometer configurations) are often noisier than the cells of
  the inner \uv{} plane (just because there are less cells in the inner
  \uv{} plane). Robust weighting thus increases the spatial resolution by
  emphasizing the large spatial frequencies at moderate cost in sensitivity 
  for point sources (but with a larger loss for extended sources, see below).
\item[Tapering] is the apodization of the \uv{} coverage by simple
  multiplication by a Gaussian
  \begin{latexonly}
  $$  W = \exp\cbrace{-\frac{\paren{u^2+v^2}}{t^2}}, $$
  \end{latexonly}
  \begin{rawhtml}
  $$  W = \mathrm{exp}\left(-\frac{\left({u^2+v^2}\right)}{t^2}\right), $$
  \end{rawhtml}
  where $t$ is the tapering distance. This multiplication in the \uv{}
  plane translates into a convolution by a Gaussian in the image plane,
  \ie\ a smoothing of the result. The only purpose of this is to increase
  the sensitivity to extended structure. Tapering should never be
  used \textbf{alone} as this somehow implies that you throw away large spatial
  frequencies measured by the interferometer. It is only a way to extract
  the most information from the given data set. If you need more sensitivity
  to extended structures, use compact configuration of the arrays 
  rather than extended configurations and tapering.
\end{description}
For more details on the whole imaging process the interested reader is
referred to \cite{guilloteau00}.

\subsubsection{Implementation (\comm{READ}{UV}, \com{UV\_MAP} and \com{UV\_STAT})}
\label{sub:single:implementation}

In \imager{}, gridding in the \uv{} plane and computation of the dirty
beam and image are coded in the \com{UV\_MAP} command. This command works
on an internal buffer containing the \uv{} table read from a file through
the \comm{READ}{UV} command. 

The \com{UV\_MAP} command is controlled by a set of SIC variables named 
with the prefix \texttt{MAP\_}. Suitable defaults are provided, so that 
only specific cases should require customization by the user. A 
description of the all variables can be obtained through \texttt{HELP UV\_MAP}.
\comm{UV\_MAP}{?} also gives their default and current values.

\textbf{Basic usage - image characterization:}\\
%\hspace{-1.0cm}
\begin{tabular}{lll}
 Name & Dim/Type & Description \\
 \sicvar{BEAM\_STEP} & Int & Number of channels per common dirty beam, if $> 0$. \\
                & & If 0 (default value),  only one beam is produced in total. \\
                & & If $-1$, an automatic guess is performed from the map size and \\
                & & requested precision (\sicvar{MAP\_PRECIS}). \\
 \sicvar{MAP\_CELL}   & [2] Real & Pixel size in arcsecond. Enter 0,0 to let the task find the best values. \\
 \sicvar{MAP\_FIELD}  &[2] Real & Image size in arcsecond.  \sicvar{MAP\_FIELD} has precedence over \\
      & & the number of pixels \sicvar{MAP\_SIZE} to define the  actual map size \\
      & & when both variables are non-zero. \\
 \sicvar{MAP\_SIZE}   & [2] Int  &   Image size in pixels \\
 \sicvar{MAP\_CENTER}  & Char.  &  A character string to specify the new Map center and the new map\\
      & & orientation, see the next subsection related to the definition of the \\
\phantom{MAP\_CONVOLUT}  & \phantom{Dim/Type} & projection center of the image. \\
\end{tabular}

\textbf{Weighting:}\\
\begin{tabular}{lll}
\sicvar{MAP\_ROBUST}  &  Real & Robust weighting factor, in range 0 - $+ \infty$. \\ 
       &  & 0 means  Natural weighting (as $+ \infty$, actually).\\ 
	      &  & 0.5 or 1 is usually a good choice for Robust Weighting. \\
	      &  & Default is 0,i.e. natural weighting. (Old name \sicvar{UV\_CELL[1]}) \\
\sicvar{MAP\_UVTAPER} & [3] Real &  Array of 3 values controlling the UV taper: major/minor axis \\
                & & at 1/e level [m,m] (first two values), and position angle ([${\deg}$],  \\
                & & third value). By default (0,0,0). (Old name \sicvar{UV\_TAPER[3]}). \\
\sicvar{MAP\_UVCELL}  & [2] Real &  UV Cell size for Robust weighting. Default is 0, meaning that  \\
              & & the cell size is derived from the antenna diameter. \\
                & & (Old name \sicvar{UV\_CELL[2]}) \\
\sicvar{MAP\_TAPEREXPO} & Real &  the taper exponent. Default 2, indicating Gaussian function. \\
 \phantom{MAP\_CONVOLUT}          & \phantom{Dim/Type} & (Old name \sicvar{TAPER\_EXPO}). \\
\end{tabular}

\textbf{Advanced:}\\
\begin{tabular}{lll}
\sicvar{MAP\_PRECIS}  & Real & Position precision at the map edge, in fraction of pixel size, \\
                & & used (with the actual image size) to  derive  the  actual number of \\
                & & channels which can share the same beam.  Default value is 0.1. \\
\sicvar{MAP\_POWER}   & Int & Rounding scheme for default image size, to numbers like $2^{n}3^{p} 5^{q}$. \\
      & & $p$ and $q$ are less than or equal to \sicvar{MAP\_POWER}. \\
      & & Default value is 2, for smallest image size. For \sicvar{MAP\_POWER} = 0 \\
      & & \sicvar{MAP\_SIZE} is just a power of 2. \\
\sicvar{MAP\_ROUNDING} & Real & Maximum  error  between optimal size (\sicvar{MAP\_FIELD} / \sicvar{MAP\_CELL}) \\
      & & and rounded (as a power of $2^k 3^p 5^q$) \sicvar{MAP\_SIZE}. \\
\sicvar{MOSAIC\_BEAM} &  Real & For a Mosaic, truncate the primary beam to the specified level\\
\phantom{MAP\_CONVOLUT} & \phantom{Di/Typ} & (in fraction). Default value is 0.2.\\
%\hspace{0.2cm} \= \hspace{4.2cm} \= \kill
\end{tabular}

\textbf{Debug:}\\
\begin{tabular}{lll}
\sicvar{MAP\_CONVOLUTION}  & Int &  Gridding convolution mode in the $uv$ plane. \\
		& & (default 5 for speroidal functions) (old name \sicvar{CONVOLUTION}) \\
\sicvar{MAP\_VERSION} & Int &  Version of code to be used. This is a temporary variable to allow \\
                & &  comparison between the new and old codes without quitting \\
\phantom{MAP\_CONVOLUT}                & \phantom{Dim/Type} & \imager{}. \\
\end{tabular}


% The following variables are obsolescent or obsolete:
%\> MAP\_SHIFT    \>  Logical indicating whether the phase center must be shifted and/or \\
%                \> \> the image rotated (old name UV\_SHIFT). \\
% \> MAP\_ANGLE \>  Position angle of map axis when MAP\_SHIFT is set \\
% \> MAP\_RA      \>   Right ascension of new map center \\
% \> MAP\_DEC     \> Declination of new map center  \\
%\end{tabbing}

%In addition, the SIC variable WCOL indicates the weight channel, and the variable MCOL (a 2 dimension integer) specifies the channel range to be imaged. However, WCOL should in general be set to zero to allow the beam steps to be set. %(EDF: beam steps ??)

Parameters can be listed by the commands \qom{UV\_MAP ?}  and \qom{CLEAN ?}. More details are
obtained using ?? or ??? as arguments instead of ?.
A thorough description of each parameter can be obtained by typing: \qom{help UV\_MAP}~\sicvar{MAP\_*}  
or \qom{help CLEAN}~\sicvar{CLEAN\_*}.

\begin{verbatim}
IMAGER> UV_MAP ?
 UV_MAP makes a dirty image and a dirty beam from the UV data
 
 Behaviour is controlled by a number of SIC Variables
 - BEAM_STEP and MAP_PRECIS control the dirty beam precision
 - MAP_CENTER controls shifting ang rotation
 - MAP_CELL[2], MAP_SIZE[2], MAP_FIELD[2] control the map sampling
 - MAP_UVTAPER[3], MAP_UVCELL and MAP_ROBUST
     control the beam shape and weighting scheme
 
---- Basic parameters                            Selected       Recommended
  Map Size (pixels)              MAP_SIZE        [ 0 0 ]   [ 0 0 ]
  Field of view (arcsec)         MAP_FIELD       [ 0 0 ]   [ 0 0 ]
  Pixel size (arcsec)            MAP_CELL        [ 0 0 ]   [ 0 0 ]
  Map center                     MAP_CENTER      [  ]      
  Robust weighting parameter     MAP_ROBUST      [ 0 ]
  UV cell size (meter)           MAP_UVCELL      [ 0 ]          [ 0 ]   
  UV Taper (m,m,°)               MAP_UVTAPER     [ 0 0 0 ] 
\end{verbatim}

\subsubsection{Defining the Projection Center of the image}
\label{sub:single:center}

The command \com{UV\_MAP} handles phase tracking center through its arguments, 
or through the string variable \sicvar{MAP\_CENTER}. The syntax of the arguments is the following:

\com{UV\_MAP }\texttt{[CenterX CenterY }\qom{UNIT}\texttt{] [Angle]}  [\qom{/FIELDS} FieldList] [\qom{/TRUNCATE} Percent]

The allowed syntax for \sicvar{MAP\_CENTER} is described by \qom{HELP UV\_MAP MAP\_CENTER}.
    
%The old command UV\_SHIFT was introduced to phase shift Mosaics to a
%common phase center in a Mosaic. It also works for single fields if needed,
%but this command is deprecated as the mechanism should be modified
%for large field mosaics.
%
%The old code can still be executed by first setting MAP\_VERSION = -1. 
%MAP\_VERSION set to 0 (the default) uses the new code, and MAP\_VERSION = 1 allows access to an intermediate version. For phase center shifting, the old code still requires the use of the
%variables MAP\_RA, MAP\_DEC, MAP\_ANGLE and UV\_SHIFT.
%(EDF: clarify whether MAP\_SHIFT variable can be used or not , and in which version of the code -- it exists in version 0, although declared as obsolescent, but MAP\_RA and MAP\_DEC are not described in the help)

\subsubsection{Typical imaging session}
\label{sub:single:example}

\begin{verbatim}
       1 read uv gag_demo:demo-single-imager
       2 uv_map ?
       3 uv_stat weight
       4 let map_robust 0.5
       5 uv_map ?
       6 uv_map
       7 show beam
       8 show dirty
       9 let map_size 128
      10 uv_map
      11 show beam
      12 show dirty
      13 hardcopy demo-dirty /dev eps
      14 write dirty demo
      15 write beam demo
\end{verbatim}
Comments:
\begin{description}\itemsep 0pt
  \item[Step 1] Read the \file{demo-single-imager.uvt} \uv{} table in an internal buffer.
  \item[Step 2] Check current state of the variables that control the 
  imaging.
  \item[Steps 3-5] Select the robust weighting threshold (step 4) from 
  the result of the \com{UV\_STAT} command (step 3) and recheck the 
  current state of the variables that control the imaging (step 5).
  \item[Steps 6-8] Image and plot the dirty beam and image.
  \item[Steps 9-12] Try a smaller size of the map as the default imaged 
  field-of-view looked too large from previous plots.
  \item[Steps 13] Make a Post-Script file from the dirty image.
  \item[Steps 14-15] Write dirty image and beam in \texttt{demo.lmv} 
  and \texttt{demo.beam} files for deconvolution in a future \imager{} 
  session. These steps are optional as you may directly proceed to the 
  deconvolution stage without writing the files.
\end{description}

\subsection{Deconvolution}
\label{sub:single:deconvolution}

Once the dirty beam and the dirty image have been calculated, we want to
derive an astronomically meaningful result, ideally the sky brightness.
However, it is extremely difficult to recover the intrinsic brightness
distribution with an interferometer. Mathematically, the
incomplete sampling of the \uv{} plane implies that there is an infinite
number of intensity distributions which are compatible with the constraints
given by the measured visibilities.  Fortunately, physics allow us to
select some solutions from the infinite number that mathematics authorize.
The goal of deconvolution is thus to find a sensible intensity distribution
compatible with the measured visibilities. To reach this goal,
deconvolution needs 1) some \emph{a priori}, physically valid, assumptions
about the source intensity distribution and 2) as much knowledge as
possible about the dirty beam and the noise properties (in radioastronomy,
both are well known). The best solution would obviously be to avoid
deconvolution, \ie\ to get a Gaussian dirty beam. For instance, the design
of the compact configuration of \ALMA{} has been thought with this goal in
mind. However, this goal is out of reach for today's millimeter
interferometers, even \ALMA{}.

The simplest \emph{a priori} knowledge that the user can feed to
deconvolution algorithm is a rough idea of the emitting region in the
source.  The user defines a support inside which the signal is to be found
while the outside is only made of sidelobes. The definition of a support
considerably helps the convergence of deconvolution algorithms because it
decreases the complexity of the problem (\ie\ the size of the space to be
searched for solutions). However, it can introduce important biases in the
final solution if the support excludes part of the sky region that is really
emitting. Support must be thus used with caution.

\subsection{The family of \clean{} algorithms (\com{HOGBOM}, \com{CLARK},
  \com{MX}, \com{SDI}, \com{MRC}, \com{GCLEAN}, \com{MULTI})}
\label{sub:single:clean}

Radio astronomy interferometry made a significant step forward with the
introduction of a robust deconvolution algorithm, known as \clean{}, by
\cite{hogbom74}.

\subsubsection{\clean{} ideas}

The family of \clean{} algorithms is based on the \emph{a priori}
assumption that the sky intensity distribution is a collection of point
sources. The algorithms have three main steps
\begin{description}
\item[Initialization] \mbox{}
  \begin{itemize}
  \item of the residual map to the dirty map;
  \item and of the clean component list to a \texttt{NULL} (\ie\ zero)
    value.
  \end{itemize}
\item[Iterative search] for point sources on the residual map. As those
  point sources are found,
  \begin{itemize}
  \item they are subtracted from the residual map;
  \item and then they are logged in the clean component list.
  \end{itemize}
\item[Restoration] of the clean map 1) by convolution of the clean
  component list with the clean beam, \ie\ a Gaussian whose size matches
  the synthesized beam size and 2) by addition of the residual map.
\end{description}

\paragraph{Stopping criteria}

Several criteria may be used to stop the iterative search of this
``matching pursuit'':
\begin{enumerate}
\item When the maximum of the absolute value of the residual map is lower
  than a fraction of the noise. This stopping criterion is adapted to
  \emph{noise limited situations}, \ie\ when empirical measures of the
  noise in the cleaned image give a value similar to the noise value
  estimated from the system temperatures.
\item When the maximum of the absolute value of the residual map is lower
  than a fraction of the maximum intensity of the original dirty map.  This
  stopping criterion is adapted to \emph{dynamic range limited situations}, \ie\ 
  when some part of the source is so intense that the associated side lobes
  are larger than the thermal noise. In this case, any empirical measure of
  the noise in the cleaned image will give a value larger than the noise
  value estimated from the system temperatures.
\item The total number of clean components. This is a sanity criterium in
  case the other ones would be badly tuned.
\item When the total flux remains stable.
\end{enumerate}
Choosing the good stopping criterion is important because the 
deconvolution must go deep enough to recover weak extended flux  but 
\clean{} algorithms start to diverge when the noise is cleaned too 
deep. Criterium 4 is thus in general preferable, but may lead to 
insufficient cleaning when the dirty beam is poor (by lack of \uv{} 
coverage and/or because of phase noise). If (\# 4) fails, a good 
compromise is to clean down to or slightly below (typically 
$0.8\sigma$) the noise level.

\paragraph{Stability criterion}

The simplest way to control the convergence is the \sicvar{CLEAN\_STOP}~
character variable. It allows to express the stopping threshold in natural units,
e.g. Jy, mJy, K, milliK, Noise or Sigma (the noise levels), or even \% 
for peak percentage.

But Clean convergence can also be controlled by the usual \sicvar{CLEAN\_ARES} (\#1, 
maximum Absolute RESidual value) , \sicvar{CLEAN\_FRES} (\#2, maximum 
Fractional RESidual value) and \sicvar{CLEAN\_NITER} (\#3, maximum Number of 
ITERations) criteria, plus \sicvar{CLEAN\_NCYCLE} for methods with 
major cycles. A fourth criterium  (\#4) is \textit{convergence}, which  
is controlled by \sicvar{CLEAN\_NKEEP}, a number of components. 
Deconvolution of a given channel stops if the cumulative flux at 
iteration number \textit{N} is smaller (resp. larger) than at iteration 
\textit{N-}\texttt{CLEAN\_NKEEP} for positive signals (resp. negative). In 
essence, \sicvar{CLEAN\_NKEEP} is the number of components when the 
signal is just above the noise. Experimentation with various types of 
images has shown that \sicvar{CLEAN\_NKEEP}\texttt{\,=\,70} is a good compromise. 

However, criteria \#1-3 can be set to 0, allowing \imager{} to 
automatically guess when to stop. In this case, \imager{} uses
an absolute residual threshold equals to the noise level (available
in \sicvar{dirty\%gil\%noise}), and estimates a (very conservative) maximum
number of Clean components. 

Using \sicvar{CLEAN\_STOP} has precedence over the other ways of specifying
the stopping criteria.

\paragraph{Formation of the \clean{} map}

The clean component list may be searched on an arbitrarily fine spatial 
grid without too much physical sense as the interferometer has a finite 
spatial resolution.  The convolution by the clean beam thus 
reintroduces the finite resolution of the observation, an information 
which is missing from the list of clean components alone. This step is 
often called \emph{a posteriori} regularization.

The shape (principally its size) of the clean beam used in the 
restoration step plays an important role. The clean beam is usually a 
fit of the main lobe (\ie\ the inner part) of the dirty beam. This 
ensures that 1) the flux density estimation will be correct and 2) the addition of the residual map to 
the convolved list of clean component makes sense (\ie\ the unit of the 
clean and residual maps approximately matches). 

The final addition of the residual map plays a double role. First, it 
is a first order correction to insufficient deconvolution. Second, it 
enables noise estimate on the cleaned image since the residual image 
should be essentially noise when the deconvolution has converged.

Some odd dirty 
beams may lead to incorrect flux measurements. For example, if the 
dirty beam has a very narrow central peak superimposed on a rather 
broad plateau, the volume of the Gaussian fitted to the central peak 
does not match that of the dirty beam, and the flux scale will be 
incorrect. Data-reweighting is required to cure these peculiar 
situations, and this always implies a loss of sensitivity.

Super-resolution is the fact of restoring with a clean beam size 
smaller that the fit of the main lobe of the dirty beam. The underlying 
idea is to get a bit finer spatial resolution. However, it is a bad 
practice because it breaks the flux estimation and the usefulness of 
the addition of the residual maps. It is better to use robust weighting 
to emphasize the largest measured spatial frequencies.


\subsubsection{Basic \clean{} algorithms (\qom{HOGBOM}, \qom{CLARK} and \qom{MX})}

The main difference between the different basic \clean{} algorithms is 
the strategy for searching the point sources.

\paragraph{\qom{HOGBOM}}

The simplest strategy of the iterative search was introduced by
\cite{hogbom74}. It works as follows
\begin{enumerate}
\item Localization of the strongest intensity pixel in the current residual map: 
  $\mathrm{max}(|{I_\mathrm{res}}|)$.
\item Add $\gamma.\mathrm{max}(|I_\mathrm{res}|)$ and its spatial position to
  the clean component list.
\item Convolution of $\gamma.\mathrm{max}(|{I_\mathrm{res}}|)$ by the dirty
  beam.
\item Subtract the resulting convolution from the residual map in order to
  clean out the side lobes associated to the localized clean component.
\end{enumerate}
$\gamma$ is the loop gain. It controls the convergence of the method. In
theory, $ 0 < \gamma < 2$. $ \gamma =1$ would in principle give
faster convergence, since the remaining flux at one position is 
$ \propto (1-\gamma)^n$,
where $n$ is the number of
clean components found at this position. But, in practice, one should use 
$\gamma \simeq 0.1 - 0.2$, depending on sidelobe levels, source structure
and dynamic range.  Indeed, deviations (such as thermal noise, phase noise
or calibration errors) from an ideal convolution equation force to use low
gain values in order to avoid non linear amplifications of errors.

An important property of \qom{HOGBOM} algorithm is that only the inner
quarter of the dirty image can be properly cleaned when dirty beam and
images are computed on the same spatial grid. Indeed, the subtraction of
the dirty sidelobes associated to any clean component is possible only in
the spatial extent of the dirty beam image. When the user defines a support
(\emph{a priori} knowledge), the cleaned region becomes even smaller than
the inner quarter of the dirty map.

\paragraph{\qom{CLARK}}

The most popular variant to the \qom{HOGBOM} algorithm is due to
\cite{clark80}. The iterative search for point sources involves minor and
major cycles.
\begin{description}
\item[In minor cycles,] an \qom{HOGBOM} search is performed with two
  limitations: 1) Only the brightest pixels are considered in the above
  step 1, and 2) the convolution of the found point sources (step 3 above)
  is done with a spatially truncated dirty beam 
  \footnote{It is also theoretically possible to do so with an intensity truncated beam.}. 
  Both limitations fasten
  the search but may lead to difficult convergence in cases where the
  secondary side lobes are a large fraction (\eg\ 40\%) of the main side
  lobe.
\item[In major cycles,] the clean components found in the last minor cycle
  are removed in a single step from the residual map in the Fourier plane.
  The use of the Fourier transform enable to clean slightly more than the
  inner quarter of the map.
\end{description}
\qom{CLARK} is faster than \qom{HOGBOM}, but less stable.

\paragraph{\qom{MX}}

The \qom{MX} (or Cotton-Schwab, from the names of its authors) 
algorithm, due to \cite{schwab84}, is a variant of the
\qom{CLARK} algorithm in which the clean components are removed from the
\uv{} table at each major cycle. This is the most precise way of removing
the found clean components because it avoids aliasing of the dirty
sidelobes. A direct consequence is that this method enables to clean the
largest region of the dirty map. However, this may be a relatively slow
algorithm because the imaging step must be redone at each major cycle,
although this speed issue could be compensated by the ability to
use smaller images.

\subsubsection{Advanced \clean{} algorithms to deal with extended
  structures (\qom{SDI}, \qom{MRC}, \qom{GCLEAN} and \qom{MULTI})}

When the spatial dynamic of the imaged source is large (\ie\ when the ratio
of the largest source structure over the synthesized resolution is large),
the basic \clean{} algorithms may (rarely) turn smooth area of the source
into a serie of ridges and stripes. Indeed, when the dirty beam pattern is
subtracted from a smooth feature of the dirty map, the sidelobes patterns
appear in the residual map. The search for the next clean component will
then pick first the pixels in the sidelobes pattern amplifying this
pattern. Several variants of \clean{} have been devised to solve this
problem.

\paragraph{\qom{SDI}}

In the \clean{} variant proposed by \cite{steer84}, extended features
(instead of point sources) around the current maximum of the residual map
are selected and removed in a single step. The simplest implementation
redefines the notion of minor and major cycles of the \qom{CLARK}
algorithm. In the minor cycles, only the selection of the clean components
is done by including all the pixels in the residual map that rise above a
contour set at some fraction of the current peak level. In major cycles,
all those components are removed together in the Fourier plane. The
\qom{SDI} algorithm may be instable if the fraction used for the selection
of the clean components is badly chosen. \qom{SDI} is very sensitive
to the support selection.

\paragraph{\qom{MRC}}

The Multi Resolution Clean \texttt{MRC} from \citet{wakker88} is the first
attempt to introduce the notion of cleaning at different scales. \qom{MRC}
works on two intermediate maps (strictly speaking \qom{MRC} is a
double--resolution \clean{} algorithm).  The first map is a smoothed
version of the dirty map and the second map, called difference map, is
obtain by subtraction of the smoothed map from the original dirty map.
Since the measurement equation is linear, both maps can be cleaned
independently (using a smoothed and a difference dirty beam, respectively).
The underlying idea is that extended sources in the dirty map will look
like more "point-like" with respect to the smoothed dirty beam in the
smoothed map. \qom{MRC} is faster than the basic \clean{} algorithms
because fewer clean components are needed to reproduce an extended source
feature in the smoothed map than in the original map.

\paragraph{\qom{GCLEAN}}

This Multi-scale \clean\ algorithm  has also been % (\cite{cornwell0X})
designed to improve the performance of \clean\ for extended sources.  
It is a straightforward extension of \clean\ that models the sky 
brightness not only by point sources, but also by Gaussian of
different (user-selected) sizes. It is equivalent to simultaneously deconvolve 
images obtained  with different synthesized beams derived from the highest resolution 
one by Gaussian convolution kernels. This algorithms works simultaneously in a 
range of specified scales. The \qom{GCLEAN} command uses the \qom{GAUSS} \clean\ method
and can use up to 5 different scales (specified in \sicvar{CLEAN\_SCALES} array,
with values in arcseconds, in the command line or using a default value). 
It produces good images even with a loop gain of 0.5.

On average, \qom{GCLEAN} requires significantly less components than \qom{HOGBOM}
or \qom{CLARK}, recovers more flux in total, but is somewhat slower because of 
the need of simultaneous deconvolution at different scales.

\paragraph{\qom{MULTI}}

This Multi-scale \clean\ algorithm is similar to that of the \qom{GAUSS} 
method, but uses discrete kernel. The implementation takes care of sharp edges, but
is much slower than the \qom{GCLEAN} command.
 
The implementation of Multi-scale \clean\ in  \imager{} slightly 
differs from that of \casa\ . It is less optimized in terms of speed, 
but uses a better convergence scheme in which the scale chosen at each 
iteration is the one with best signal to noise ratio. Accordingly, it 
is very stable. Only 3 scales are used so far in \imager , with a size 
ratio controlled by \sicvar{CLEAN\_SMOOTH}.

\subsubsection{Implementation and typical use}

%\subsection{CLEAN} from SG doc
Deconvolution parameters are controlled by \sicvar{CLEAN\_*} variables. 
Progress has been made on automatic guess for Cleaning parameters. The 
table below presents the current naming scheme, with previous or 
equivalent names mentioned in parentheses, since these names were (or 
are still) used by several older packages such as \mapping{}, \aips{} 
or \casa{}. The equivalent ''old'' names (mentioned in Upper case 
below) will remain as aliases, while those mentioned in mixed case have 
disappeared as they were seldom used before. 
 
\begin{tabular}{lll}
  \sicvar{CLEAN\_STOP} &  Stopping value and its Unit \\
   & \\
  \sicvar{CLEAN\_ARES} &    Absolute residual  (ARES) \\
  \sicvar{CLEAN\_FRES} &    Fractional residual (FRES) \\
  \sicvar{CLEAN\_GAIN} &    Loop gain  (GAIN) \\
  \sicvar{CLEAN\_INFLATE} &  Inflation factor allowed to display MultiScale clean components \\
  \sicvar{CLEAN\_METHOD} &   Cleaning Method  (METHOD) \\
  \sicvar{CLEAN\_NCYCLE} &   Maximum number of Major cycles (Nmajor) \\
  \sicvar{CLEAN\_NITER} &   Maximum number of iterations (NITER) \\
  \sicvar{CLEAN\_NGOAL} &  A number of components for ALMA joint deconvolution only (Ngoal)\\
  \sicvar{CLEAN\_NKEEP} &   Number of iterations used to check convergence (see below)\\
  \sicvar{CLEAN\_POSITIVE} &  Minimum number of positive Clean components \\
  \sicvar{CLEAN\_RATIO} &  Ratio for  Dual Resolution clean (Ratio) \\
  \sicvar{CLEAN\_SCALES} &  Gaussian sizes for GAUSS method, in arcsecond. \\  
  \sicvar{CLEAN\_SEARCH} &   Minimum primary beam threshold for searching (Search\_W) \\
  \sicvar{CLEAN\_SMOOTH} &  Smoothing factor for Multi Scale Clean (Smooth) \\
  \sicvar{CLEAN\_SPEEDY} &  Speeding factor for Clark (Spexp) \\
  \sicvar{CLEAN\_TRUNCATE} &   Minimum primary beam threshold for restoring  (Restore\_W) \\
  \sicvar{CLEAN\_WORRY} &  "Worry" factor for Clark (Worry) \\
\end{tabular}


\paragraph{Implementation}

In \imager{}, the variants of the \clean{} algorithms discussed above are
coded as the following commands: \qom{HOGBOM}, \qom{CLARK}, \qom{MX},
\qom{SDI}, \qom{GCLEAN}, \qom{MULTI} and \qom{MRC}. All those commands work on two
internal buffers containing the dirty beam and dirty image. Both buffers
are created directly from \uv{} table through the \com{UV\_MAP} command, or
they can be loaded from files through the \comm{READ}{BEAM} and
\comm{READ}{DIRTY} commands. The behavior of those commands is controlled
through the following common \sic{} variables:
\begin{description}\itemsep 0pt
\item[Iterative search] \mbox{}
  \begin{description}\itemsep 0pt
  \item[\sicvar{CLEAN\_POSITIVE}] Number of positive clean components to be found
    before enabling the search for negative components. Default is 0.
  \item[\sicvar{CLEAN\_GAIN}] Loop gain. Default is 0.2, good compromise between
    stability and speed.
  \end{description}

\item[Stopping criteria] \mbox{}
  \begin{description}\itemsep 0pt
  \item[\sicvar{CLEAN\_STOP}] Compact way to specify the stopping criterium. Defaut
    is currently empty, but might be set to \qom{2 SIGMA} as a good general default.
  \item[\sicvar{CLEAN\_NITER}] Maximum number of clean components. Default is 0.
  \item[\sicvar{CLEAN\_FRES}] Maximum amplitude of the absolute value of the
    residual image. This maximum is expressed as a fraction of the peak
    intensity of the dirty image. Default value is 0.
  \item[\sicvar{CLEAN\_ARES}] Maximum amplitude of the absolute value of the
    residual image. This maximum is expressed in the image units (Jy/Beam).
    Default value is 0.
    \item[\sicvar{CLEAN\_NKEEP}] Minimum number of Clean components be-
    fore testing if Cleaning has converged. Default value is 70. 
  \end{description}
  
\item[Support] \mbox{}
  \begin{description}\itemsep 0pt
  \item[\sicvar{BLC} and \sicvar{TRC}] Bottom Left Corner and Top Right
    Corner of a square support in pixel units. Default is 0, which means 
    using only the inner quarter if no other support is defined.
  \item[\com{SUPPORT}] A command that defines the support where
    to search for clean components. The support can be a Mask, or a
    Polygon. For a Polygon, the definition can be interactive,
    using the \greg{}  cursor. This definition can be stored in a file
    through the \comm{WRITE}{SUPPORT} command and read back in memory from
    the file with the \com{SUPPORT} command. The polygon support definition is
    stored in the \sicvar{SUPPORT\%} structure. Command \comm{SUPPORT}{/MASK}
    instructs \imager{} to use the Mask instead of the polygon for the
    Clean support.
  \item[\com{MASK}]  Command  \qom{MASK} is used to define a Mask-like
    support. This can be interactive, or automatic using a thresholding
    technique in command \comm{MASK}{THRESHOLD}. The computed Mask 
    can be saved by command \comm{WRITE}{MASK}. The Mask can also be
    read by command \comm{READ}{MASK}. Command \comm{MASK}{USE} is 
    equivalent to command \comm{SUPPORT}{/MASK}, and instructs 
    \imager{} to use the Mask instead of the polygon for the Clean 
    support.
  \end{description}

\item[Clean beam parameters] \mbox{}
  \begin{description}\itemsep 0pt
  \item[\sicvar{BEAM\_SIZE}] is a 3-element array that gives the FWHM size of the major
    and minor axes (in arcsec) and position angle (in degree) of the
    Gaussian used to restore the clean image from the clean component list.
    Default is all parameters at 0, meaning use the fit of the main lobe of
    the dirty image. Changing the default value of those parameters is
    dangerous. The beam size effectively used is available in \sicvar{BEAM\_FITTED}.
  \end{description}
\end{description}

Other variables control specific aspects of a subclass of the \clean{}
algorithm:
\begin{description}\itemsep 0pt
\item[\sicvar{CLEAN\_NCYCLE}] Maximum number of major cycles in all algorithms
  using this notion (\qom{CLARK}, \qom{MX}, \qom{SDI}). Default is 50.
\item[\sicvar{BEAM\_PATCH}] Size (in pixel units) of the dirty beam used to
  deconvolve the residual image in minor cycles. It is used in \qom{CLARK} and 
  \qom{MRC} algorithms only. Default value is 0. This is for development only.
\item[\sicvar{CLEAN\_SMOOTH}] Smoothing factor between different scales in 
the MULTISCALE methods. Default value is sqrt(3).
\item[\sicvar{CLEAN\_RATIO}] Smoothing factor between different scales in 
the MRC method. Default value is 0, for which the code automatically
derives the best power of 2 adequate for the current problem.
\end{description}

\subsubsection{Restoring step: \com{UV\_RESTORE}}

\com{CLEAN} (except for \qom{MRC}) has three outputs: the \sicvar{CLEAN} image itself,
the \sicvar{RESIDUAL} image,  and the list of point sources (the Clean Components) that 
reproduce the observed visibilities, the \sicvar{CCT} table.

An aesthetically better results, with better noise properties, can be obtained after \qom{CLEAN}
by removing the Clean Components from the measured visibilities, and re-imaging this to
produce the \sicvar{RESIDUAL} image. Aliasing at map edges is then minimized, as it only 
concerns noise if the deconvolution was reasonable. As mentioned previously, this is 
implicitely done when using \com{MX} instead of \qom{CLEAN}.  It can also be done after \qom{CLEAN}
using command \com{UV\_RESTORE}. 

This step can also be used to properly scale the residuals when the synthesized beam 
is not well fit by a Gaussian, or when all channels do not share the same beam (see Section \ref{sec:sub:beamstep}). This
is done through the use of the so-called \index{JvM factor} \sicvar{JvM factor}. This factor, introduced in \cite{jvm1995},
estimates the ratio of clean beam area to dirty beam area, and is stored in variable
\sicvar{BEAM\_JVM} and (on a per beam basis) \sicvar{BEAM\_VALUES[4]} which are computed by \comm{FIT}{/JVM\_FACTOR}. 
Residuals are multiplied by this factor (on a channel per channel basis if needed), allowing
to first order to scale the residuals to the same unit as the Cleaned data, i.e. Jy/beam area.


In general, this number is fairly close to 1. However, when several observing configurations
have been merged together, the synthesized beam often exhibits a central peak over a larger
plateau, because of the higher weights of short baselines. Robust weighting limits the effect,
but is not able to suppress it totally. In such cases, the \sicvar{JvM factor} can be of 
order 0.5 to 0.6.  

\begin{itemize}
\item{\textbf{Caveat:}} \textit{\bf This scaling is only meaningful to first order for 
(reasonably) extended sources and well-behaved beams. It should \textbf{NOT} be used to analyze 
data for point sources.}
\end{itemize}


\qom{UV\_RESTORE} uses the \texttt{JvM factor} if \qomm{FIT}{/JVM\_FACTOR} has been used before.
It also uses the mean Clean beam geometry  defined by \sicvar{BEAM\_FITTED}.

\subsubsection{Typical deconvolution session}

\begin{verbatim}
       1 read beam demo
       2 read dirty demo
       3 clean ?
       4 hogbom /flux 0 1
       5 show residual
       6 show clean
       7 write clean demo
       8 let name demo
       9 show noise
      10 let ares 0.5*noise
      11 clean ?
      12 hogbom /flux 0 1
      13 let niter 2000
      14 clean ?
      15 hogbom /flux 0 1
      16 show residual
      17 show clean
      18 for iplane 1 to 10
      19    show clean iplane
      20    support
      21    hogbom iplane /flux 0 1
      22    write support "demo-"'iplane'
      23 next iplane
      24 show residual
      25 view cct
      26 view clean
      27 write residual demo
      28 write clean demo
      29 write cct demo
\end{verbatim}
Comments:
\begin{description}\itemsep 0pt
\item[Steps 1-2] Read dirty beam and dirty image from the
  \file{demo.beam} and \file{demo.lmv} files. Those steps are not
  needed if the dirty beam and image are already stored in the internal
  buffer, \ie\ if you have imaged the \uv{} table just before in the same
  \imager{} session.
\item[Steps 3-6] Print the current state of the control parameters,
  deconvolve the dirty image using the \qom{HOGBOM} algorithm (step 3) and
  look at the results (residual and clean images).  The \qom{/flux 0 1}
  option pop-up the visualization of the cumulative flux deconvolved as the
  clean components are found.
\item[Steps 8-12] Estimate the empirical noise through the \comm{SHOW}{NOISE}
  command after this first deconvolution and set the \texttt{ares} stopping
  criterion accordingly. Check that the new value of \texttt{ares} has been
  correctly set (step 11) and restart deconvolution.
\item[Steps 13-17] Increase the number of clean components as the previous
  deconvolution stopped before the residual image reached the
  \texttt{ares} value. Restart deconvolution and look at results.
\item[Steps 18-23] Attempt to improve deconvolution by definition of a
  support per plane and deconvolve this plane accordingly. The support is
  stored in a file for further re-use. The deconvolution results are then displayed.
\item[Steps 24-26] Display the residual images, visualize the cumulative 
  flux as a function of the clean component number and visualize the clean 
  spectra cube in an interactive way.
\item[Steps 27-29] Write residual image, clean image and clean component
  list in \file{demo.lmv-res}, \file{demo.lmv-clean} and
  \file{demo.cct} files for later use. 
\end{description}
Typical deconvolution session using other \clean{} algorithm would look
very similar. The main difference would be the possible tuning of other
control parameters. A deconvolution session using \qom{MX} would start
differently as the imaging and deconvolution are done in the same step:
\begin{verbatim}
       1 read uv demo
       2 mx ?
       3 mx /flux 0 1
       4 show residual
       5 show clean
       6 write * demo
%       6 write beam demo
%       7 write dirty demo
%       8 write clean demo
%       9 write residual demo
%      10 write cct demo
\end{verbatim}
Comments:
\begin{description}\itemsep 0pt
\item[Step 1] Read the \texttt{demo.uvt} \uv{} table in an internal buffer.
\item[Step 2] Check current state of the variables that control the
  imaging and deconvolution.
\item[Steps 3-5] Deconvolve and look at the results.
\item[Steps 6-10] Write all the internal buffers on disk files. 
\end{description}
All the tuning of the typical imaging and deconvolution sessions could be
used in this \qom{MX} session although they are not repeated here.

\subsection{Practical advices}
\label{sub:single:advice}

\subsubsection{Comparison of deconvolution algorithms}

\qom{HOGBOM} is the basic \clean{} algorithm. It is very robust, but 
somewhat slow although this is partially compensated by good parallel
programming. 
\qom{CLARK} introduces a faster strategy for the search and removal of 
clean component. However, it may converge more slowly and can even 
be instable when dirty sidelobes are 
high, or the phase noise still significant. \qom{MX} cleans the largest 
region of the dirty map because the source removal happens in the \uv{} 
plane. For the same map size, it is slower than \qom{CLARK} because of 
the repeated imaging step, but smaller image sizes can be used.
It shares some of the \qom{CLARK}  instabilities because it uses 
the same search strategy, but the removal strategy counteracts this.
\qom{MX} can be very slow for large number of visibilities.

\qom{HOGBOM}, \qom{CLARK} and \qom{MX} may introduce artifacts as parallel
stripes in the clean map when dealing with smooth, extended structures.
\qom{GAUSS}, \qom{SDI}, \qom{MRC} and \qom{MULTI} introduce a different (in principle better)
handling of those extended sources. \qom{SDI} is a rough attempt and requires
a good prior of the support to work correctly. \qom{MRC}
has unfortunately no notion of Clean Components. \qom{GAUSS} and
\qom{MULTI} are the most advanced tools, but are slower than the other methods,
although the \qom{GAUSS} implementation is quit efficient.

\subsubsection{A few (obvious) practical recommendations}
\begin{description}\itemsep 0pt
\item[Map size] Make an image about twice the size of the primary beam (\eg\ 
  $2\times55''$ at 90~GHz and $2\times22''$ at 230~GHz for \NOEMA{} antenna)
  to ensure that all the area of the primary beam (inner quarter of the
  dirty map) will be cleaned whatever the deconvolution algorithm is used.
  However, avoid making a too large dirty image because the \clean{}
  algorithms will then try to deconvolve region outside the primary beam
  area where the noise dominates.
\item[Support] Start your first deconvolution \emph{without} any support to
  avoid biasing your clean image. If the source is spatially bound, you
  can define a support around the source and restart the deconvolution with
  this \emph{a priori} information. Be careful to check that there is no
  low signal-to-noise extended structure that could contain a large
  fraction of the source flux outside your support... Avoid defining a
  support too close to the natural edges of your source. Indeed,
  deconvolving noisy regions around your source is advisable because it
  ensures that you do not bias your deconvolution too much.
\item[Stopping criterion] Choose the right stopping criterion.\\
  Use the stability \sicvar{CLEAN\_NKEEP} parameter preferentially, combined
  with a \sicvar{CLEAN\_STOP = r SIGMA} with \texttt{r} between 1 or 2. 
  That keeps \sicvar{CLEAN\_ARES}, \sicvar{CLEAN\_FRES} and \sicvar{CLEAN\_NITER}
  to zero. If it does not work, then
  \begin{itemize}\itemsep 0pt
  \item Estimate an empirical noise on your first deconvolved cleaned image
    with \com{STATISTIC}, \com{CLEAN}, or \comm{SHOW}{NOISE}.
  \item If this empirical noise value is similar to the value computed from the
    visibility weights (this noise value is one of the outputs of the
    \com{UV\_MAP} command), your observation is not dynamic range limited.
    Apart from using a user-specified support (\qom{SUPPORT} or \qom{MASK}) 
    there is not much you can do to improve your result,
  \item If not, you are dynamic range limited. You may use \sicvar{CLEAN\_STOP = r \%}
    where \texttt{r} depends on the dynamic range as stopping criterium. Alternatively,
    you can select the effective noise level as the true Sigma,  \sicvar{CLEAN\_STOP = r 'clean\%rms'}
    with \texttt{r} being 1 to 2. The problem in such cases is that the noise level may be channel
    dependent, an issue that is not well handled.
  \end{itemize}
\item[Convergence checks] Ensure that your deconvolution converged by
  checking that:
  \begin{itemize}\itemsep 0pt
  \item The cumulative flux as a function of the number of clean component
    has reached a roughly constant level (use \qom{/FLUX} option of the
    deconvolution commands to see this curves, or \comm{SHOW}{CCT} or \comm{VIEW}{CCT}).
  \item The residuals are similar or smaller in the source region (where Clean
  components were found) compared to elsewhere.
  \end{itemize}
  If not, change the values of the stopping criterion, whichever you used.
\item[Deconvolution methods] If you want a robust result in all cases,
  start with \qom{HOGBOM}. If you prefer obtaining a quick result, use
  \qom{CLARK} but you then first need to check that the dirty sidelobes are
  not too large on the dirty beam. If you obtain stripes in your Clean
  image:
  \begin{itemize}\itemsep 0pt
  \item First check that your deconvolution converged.
  \item Then check that there are no spurious visibilities that should 
   be flagged : use command \com{UV\_FLAG} as a last resort.
  \item If it is clear that you have an extended source structure, you
    should first ask yourself whether you are in the wide-field imaging
    case and act accordingly (see next chapter). Else you can try a
    \qom{CLEAN} variant which better deals with cases that implies a large
    spatial dynamic. This is rare at \NOEMA{}, but may happen with \ALMA .
  \end{itemize}
\item[Outside help] Always consult an expert until you become one.
\end{description}

