source: trunk/docs/executionFlow.tex @ 298

\secA{What \duchamp is doing}
\label{sec-flow}

Each of the steps that \duchamp goes through in the course of its
execution are discussed here in more detail. This should provide
enough background information to fully understand what \duchamp is
doing and what all the output information is. For those interested in
the programming side of things, \duchamp is written in C/C++ and makes
use of the \textsc{cfitsio}, \textsc{wcslib} and \textsc{pgplot}
libraries.

\secB{Image input}
\label{sec-input}

The cube is read in using basic \textsc{cfitsio} commands, and stored
as an array in a special C++ class. This class keeps track of the list
of detected objects, as well as any reconstructed arrays that are made
(see \S\ref{sec-recon}). The World Coordinate System
(WCS)\footnote{This is the information necessary for translating the
pixel locations to quantities such as position on the sky, frequency,
velocity, and so on.} information for the cube is also obtained from
the FITS header by \textsc{wcslib} functions \citep{greisen02,
calabretta02}, and this information, in the form of a \texttt{wcsprm}
structure, is also stored in the same class.

A sub-section of a cube can be requested by defining the subsection
with the \texttt{subsection} parameter and setting
\texttt{flagSubsection = true} -- this can be a good idea if the cube
has very noisy edges, which may produce many spurious detections.

There are two ways of specifying the \texttt{subsection} string. The
first is the generalised form
\texttt{[x1:x2:dx,y1:y2:dy,z1:z2:dz,...]}, as used by the
\textsc{cfitsio} library. This has one set of colon-separated numbers
for each axis in the FITS file. In this manner, the x-coordinates run
from \texttt{x1} to \texttt{x2} (inclusive), with steps of
\texttt{dx}. The step value can be omitted, so a subsection of the
form \texttt{[2:50,2:50,10:1000]} is still valid. In fact, \duchamp
does not make use of any step value present in the subsection string,
and any that are present are removed before the file is opened.

If the entire range of a coordinate is required, one can replace the
range with a single asterisk, \eg \texttt{[2:50,2:50,*]}. Thus, the
subsection string \texttt{[*,*,*]} is simply the entire cube. Note
that the pixel ranges for each axis start at 1, so the full pixel
range of a 100-pixel axis would be expressed as 1:100. A complete
description of this section syntax can be found at the
\textsc{fitsio} web site%
\footnote{%
\href%
{http://heasarc.gsfc.nasa.gov/docs/software/fitsio/c/c\_user/node91.html}%
{http://heasarc.gsfc.nasa.gov/docs/software/fitsio/c/c\_user/node91.html}}.


Making full use of the subsection requires knowledge of the size of
each of the dimensions. If one wants to, for instance, trim a certain
number of pixels off the edges of the cube, without examining the cube
to obtain the actual size, one can use the second form of the
subsection string. This just gives a number for each axis, \eg
\texttt{[5,5,5]} (which would trim 5 pixels from the start \emph{and}
end of each axis).

All types of subsections can be combined \eg \texttt{[5,2:98,*]}. 


\secB{Image modification}
\label{sec-modify}

Several modifications to the cube can be made that improve the
execution and efficiency of \duchamp (their use is optional, governed
by the relevant flags in the parameter file).

\secC{BLANK pixel removal}
\label{sec-blank}

If the imaged area of a cube is non-rectangular (see the example in
Fig.~\ref{fig-moment}, a cube from the HIPASS survey), BLANK pixels
are used to pad it out to a rectangular shape. The value of these
pixels is given by the FITS header keywords BLANK, BSCALE and
BZERO. While these pixels make the image a nice shape, they will take
up unnecessary space in memory, and so to potentially speed up the
processing we can trim them from the edge. This is done when the
parameter \texttt{flagTrim = true}. If the above keywords are not
present, the trimming will not be done (in this case, a similar effect
can be accomplished, if one knows where the ``blank'' pixels are, by
using the subsection option).

The amount of trimming is recorded, and these pixels are added back in
once the source-detection is completed (so that quoted pixel positions
are applicable to the original cube). Rows and columns are trimmed one
at a time until the first non-BLANK pixel is reached, so that the
image remains rectangular. In practice, this means that there will be
some BLANK pixels left in the trimmed image (if the non-BLANK region
is non-rectangular). However, these are ignored in all further
calculations done on the cube.

\secC{Baseline removal}

Second, the user may request the removal of baselines from the
spectra, via the parameter \texttt{flagBaseline}. This may be
necessary if there is a strong baseline ripple present, which can
result in spurious detections at the high points of the ripple. The
baseline is calculated from a wavelet reconstruction procedure (see
\S\ref{sec-recon}) that keeps only the two largest scales. This is
done separately for each spatial pixel (\ie for each spectrum in the
cube), and the baselines are stored and added back in before any
output is done. In this way the quoted fluxes and displayed spectra
are as one would see from the input cube itself -- even though the
detection (and reconstruction if applicable) is done on the
baseline-removed cube.

The presence of very strong signals (for instance, masers at several
hundred Jy) could affect the determination of the baseline, and would
lead to a large dip centred on the signal in the baseline-subtracted
spectrum. To prevent this, the signal is trimmed prior to the
reconstruction process at some standard threshold (at $8\sigma$ above
the mean). The baseline determined should thus be representative of
the true, signal-free baseline. Note that this trimming is only a
temporary measure which does not affect the source-detection.

\secC{Ignoring bright Milky Way emission}

Finally, a single set of contiguous channels can be ignored -- these
may exhibit very strong emission, such as that from the Milky Way as
seen in extragalactic \hi cubes (hence the references to ``Milky
Way'' in relation to this task -- apologies to Galactic
astronomers!). Such dominant channels will produce many detections
that are unnecessary, uninteresting (if one is interested in
extragalactic \hi) and large (in size and hence in memory usage), and
so will slow the program down and detract from the interesting
detections. 

The use of this feature is controlled by the \texttt{flagMW}
parameter, and the exact channels concerned are able to be set by the
user (using \texttt{maxMW} and \texttt{minMW} -- these give an
inclusive range of channels). When employed, these channels are
ignored for the searching, and the scaling of the spectral output (see
Fig.~\ref{fig-spect}) will not take them into account. They will be
present in the reconstructed array, however, and so will be included
in the saved FITS file (see \S\ref{sec-reconIO}). When the final
spectra are plotted, the range of channels covered by these parameters
is indicated by a green hashed box.

\secB{Image reconstruction}
\label{sec-recon}

The user can direct \duchamp to reconstruct the data cube using the
\atrous wavelet procedure. A good description of the procedure can be
found in \citet{starck02:book}. The reconstruction is an effective way
of removing a lot of the noise in the image, allowing one to search
reliably to fainter levels, and reducing the number of spurious
detections. This is an optional step, but one that greatly enhances
the source-detection process, with the payoff that it can be
relatively time- and memory-intensive.

\secC{Algorithm}

The steps in the \atrous reconstruction are as follows:
\begin{enumerate}
\item The reconstructed array is set to 0 everywhere.
\item The input array is discretely convolved with a given filter
  function. This is determined from the parameter file via the
  \texttt{filterCode} parameter -- see Appendix~\ref{app-param} for
  details on the filters available.
\item The wavelet coefficients are calculated by taking the difference
  between the convolved array and the input array.
\item If the wavelet coefficients at a given point are above the
  requested threshold (given by \texttt{snrRecon} as the number of
  $\sigma$ above the mean and adjusted to the current scale -- see
  Appendix~\ref{app-scaling}), add these to the reconstructed array.
\item The separation between the filter coefficients is doubled. (Note
  that this step provides the name of the procedure\footnote{\atrous
  means ``with holes'' in French.}, as gaps or holes are created in
  the filter coverage.)
\item The procedure is repeated from step 2, using the convolved array
  as the input array.
\item Continue until the required maximum number of scales is reached.
\item Add the final smoothed (\ie convolved) array to the
  reconstructed array. This provides the ``DC offset'', as each of the
  wavelet coefficient arrays will have zero mean.
\end{enumerate}

The reconstruction has at least two iterations. The first iteration
makes a first pass at the wavelet reconstruction (the process outlined
in the 8 stages above), but the residual array will likely have some
structure still in it, so the wavelet filtering is done on the
residual, and any significant wavelet terms are added to the final
reconstruction. This step is repeated until the change in the measured
standard deviation of the background (see note below on the evaluation
of this quantity) is less than some fiducial amount.

It is important to note that the \atrous decomposition is an example
of a ``redundant'' transformation. If no thresholding is performed,
the sum of all the wavelet coefficient arrays and the final smoothed
array is identical to the input array. The thresholding thus removes
only the unwanted structure in the array.

Note that any BLANK pixels that are still in the cube will not be
altered by the reconstruction -- they will be left as BLANK so that
the shape of the valid part of the cube is preserved.

\secC{Note on Statistics}

The correct calculation of the reconstructed array needs good
estimators of the underlying mean and standard deviation of the
background noise distribution. These statistics are estimated using
robust methods, to avoid corruption by strong outlying points. The
mean of the distribution is actually estimated by the median, while
the median absolute deviation from the median (MADFM) is calculated
and corrected assuming Gaussianity to estimate the underlying standard
deviation $\sigma$. The Gaussianity (or Normality) assumption is
critical, as the MADFM does not give the same value as the usual rms
or standard deviation value -- for a Normal distribution
$N(\mu,\sigma)$ we find MADFM$=0.6744888\sigma$, but this will change
for different distributions. Since this ratio is corrected for, the
user need only think in the usual multiples of the rms when setting
\texttt{snrRecon}. See Appendix~\ref{app-madfm} for a derivation of
this value.

When thresholding the different wavelet scales, the value of the rms
as measured from the wavelet array needs to be scaled to account for
the increased amount of correlation between neighbouring pixels (due
to the convolution). See Appendix~\ref{app-scaling} for details on
this scaling.

\secC{User control of reconstruction parameters}

The most important parameter for the user to select in relation to the
reconstruction is the threshold for each wavelet array. This is set
using the \texttt{snrRecon} parameter, and is given as a multiple of
the rms (estimated by the MADFM) above the mean (which for the wavelet
arrays should be approximately zero). There are several other
parameters that can be altered as well that affect the outcome of the
reconstruction.

By default, the cube is reconstructed in three dimensions, using a
3-dimensional filter and 3-dimensional convolution. This can be
altered, however, using the parameter \texttt{reconDim}. If set to 1,
this means the cube is reconstructed by considering each spectrum
separately, whereas \texttt{reconDim=2} will mean the cube is
reconstructed by doing each channel map separately. The merits of
these choices are discussed in \S\ref{sec-notes}, but it should be
noted that a 2-dimensional reconstruction can be susceptible to edge
effects if the spatial shape of the pixel array is not rectangular.

The user can also select the minimum scale to be used in the
reconstruction. The first scale exhibits the highest frequency
variations, and so ignoring this one can sometimes be beneficial in
removing excess noise. The default is to use all scales
(\texttt{minscale = 1}).

Finally, the filter that is used for the convolution can be selected
by using \texttt{filterCode} and the relevant code number -- the
choices are listed in Appendix~\ref{app-param}. A larger filter will
give a better reconstruction, but take longer and use more memory when
executing. When multi-dimensional reconstruction is selected, this
filter is used to construct a 2- or 3-dimensional equivalent.

\secB{Smoothing the cube}
\label{sec-smoothing}

An alternative to doing the wavelet reconstruction is to smooth the
cube.  This technique can be useful in reducing the noise level
slightly (at the cost of making neighbouring pixels correlated and
blurring any signal present), and is particularly well suited to the
case where a particular signal size (\ie a certain channel width or
spatial size) is believed to be present in the data.

There are two alternative methods that can be used: spectral
smoothing, using the Hanning filter; or spatial smoothing, using a 2D
Gaussian kernel. These alternatives are outlined below. To utilise the
smoothing option, set the parameter \texttt{flagSmooth=true} and set
\texttt{smoothType} to either \texttt{spectral} or \texttt{spatial}.

\secC{Spectral smoothing}

When \texttt{smoothType = spectral} is selected, the cube is smoothed
only in the spectral domain. Each spectrum is independently smoothed
by a Hanning filter, and then put back together to form the smoothed
cube, which is then used by the searching algorithm (see below). Note
that in the case of both the reconstruction and the smoothing options
being requested, the reconstruction will take precedence and the
smoothing will \emph{not} be done.

There is only one parameter necessary to define the degree of
smoothing -- the Hanning width $a$ (given by the user parameter
\texttt{hanningWidth}). The coefficients $c(x)$ of the Hanning filter
are defined by
\[
c(x) = 
  \begin{cases}
   \frac{1}{2}\left(1+\cos(\frac{\pi x}{a})\right) &|x| \leq (a+1)/2\\
   0                                               &|x| > (a+1)/2.
  \end{cases},\ a,x \in \mathbb{Z}
\]
Note that the width specified must be an
odd integer (if the parameter provided is even, it is incremented by
one).

\secC{Spatial smoothing}

When \texttt{smoothType = spatial} is selected, the cube is smoothed
only in the spatial domain. Each channel map is independently smoothed
by a two-dimensional Gaussian kernel, put back together to form the
smoothed cube, and used in the searching algorithm (see below). Again,
reconstruction is always done by preference if both techniques are
requested.

The two-dimensional Gaussian has three parameters to define it,
governed by the elliptical cross-sectional shape of the Gaussian
function: the FWHM (full-width at half-maximum) of the major and minor
axes, and the position angle of the major axis. These are given by the
user parameters \texttt{kernMaj, kernMin} \& \texttt{kernPA}. If a
circular Gaussian is required, the user need only provide the
\texttt{kernMaj} parameter. The \texttt{kernMin} parameter will then
be set to the same value, and \texttt{kernPA} to zero.  If we define
these parameters as $a,b,\theta$ respectively, we can define the
kernel by the function
\[ 
k(x,y) = \exp\left[-0.5 \left(\frac{X^2}{\sigma_X^2} + 
                              \frac{Y^2}{\sigma_Y^2}   \right) \right] 
\]
where $(x,y)$ are the offsets from the central pixel of the gaussian
function, and 
\begin{align*}
X& = x\sin\theta - y\cos\theta&
  Y&= x\cos\theta + y\sin\theta\\
\sigma_X^2& = \frac{(a/2)^2}{2\ln2}&
  \sigma_Y^2& = \frac{(b/2)^2}{2\ln2}\\
\end{align*}

\secB{Input/Output of reconstructed/smoothed arrays}
\label{sec-reconIO}

The smoothing and reconstruction stages can be relatively
time-consuming, particularly for large cubes and reconstructions in
3-D (or even spatial smoothing). To get around this, \duchamp provides
a shortcut to allow users to perform multiple searches (\eg with
different thresholds) on the same reconstruction/smoothing setup
without re-doing the calculations each time.

To save the reconstructed array as a FITS file, set
\texttt{flagOutputRecon = true}. The file will be saved in the same
directory as the input image, so the user needs to have write
permissions for that directory.

The filename will be derived from the input filename, with extra
information detailing the reconstruction that has been done. For
example, suppose \texttt{image.fits} has been reconstructed using a
3-dimensional reconstruction with filter \#2, thresholded at $4\sigma$
using all scales. The output filename will then be
\texttt{image.RECON-3-2-4-1.fits} (\ie it uses the four parameters
relevant for the \atrous reconstruction as listed in
Appendix~\ref{app-param}). The new FITS file will also have these
parameters as header keywords. If a subsection of the input image has
been used (see \S\ref{sec-input}), the format of the output filename
will be \texttt{image.sub.RECON-3-2-4-1.fits}, and the subsection that
has been used is also stored in the FITS header.

Likewise, the residual image, defined as the difference between the
input and reconstructed arrays, can also be saved in the same manner
by setting \texttt{flagOutputResid = true}. Its filename will be the
same as above, with \texttt{RESID} replacing \texttt{RECON}.

If a reconstructed image has been saved, it can be read in and used
instead of redoing the reconstruction. To do so, the user should set
the parameter \texttt{flagReconExists = true}. The user can indicate
the name of the reconstructed FITS file using the \texttt{reconFile}
parameter, or, if this is not specified, \duchamp searches for the
file with the name as defined above. If the file is not found, the
reconstruction is performed as normal. Note that to do this, the user
needs to set \texttt{flagAtrous = true} (obviously, if this is
\texttt{false}, the reconstruction is not needed).

To save the smoothed array, set \texttt{flagOutputSmooth = true}. The
name of the saved file will depend on the method of smoothing used. It
will be either \texttt{image.SMOOTH-1D-a.fits}, where a is replaced by
the Hanning width used, or \texttt{image.SMOOTH-2D-a-b-c.fits}, where
the Gaussian kernel parameters are a,b,c. Similarly to the
reconstruction case, a saved file can be read in by setting
\texttt{flagSmoothExists = true} and either specifying a file to be
read with the \texttt{smoothFile} parameter or relying on \duchamp to
find the file with the name as given above.


\secB{Searching the image}
\label{sec-detection}

\secC{Technique}

The basic idea behind detection in \duchamp is to locate sets of
contiguous voxels that lie above some threshold. No size or shape
requirement is imposed upon the detections -- that is, \duchamp does
not fit \eg a Gaussian profile to each source. All it does is find
connected groups of bright voxels.

One threshold is calculated for the entire cube, enabling calculation
of signal-to-noise ratios for each source (see
Section~\ref{sec-output} for details). The user can manually specify a
value (using the parameter \texttt{threshold}) for the threshold,
which will override the calculated value. Note that this only applies
for the first of the two cases discussed below -- the FDR case ignores
any manually-set threshold value.

The cube is searched one channel map at a time, using the
2-dimensional raster-scanning algorithm of \citet{lutz80} that
connects groups of neighbouring pixels. Such an algorithm cannot be
applied directly to a 3-dimensional case, as it requires that objects
are completely nested in a row (when scanning along a row, if an
object finishes and other starts, you won't get back to the first
until the second is completely finished for the
row). Three-dimensional data does not have this property, hence the
need to treat the data on a 2-dimensional basis.

Although there are parameters that govern the minimum number of pixels
in a spatial and spectral sense that an object must have
(\texttt{minPix} and \texttt{minChannels} respectively), these
criteria are not applied at this point. It is only after the merging
and growing (see \S\ref{sec-merger}) is done that objects are rejected
for not meeting these criteria.

Finally, the search only looks for positive features. If one is
interested instead in negative features (such as absorption lines),
set the parameter \texttt{flagNegative = true}. This will invert the
cube (\ie multiply all pixels by $-1$) prior to the search, and then
re-invert the cube (and the fluxes of any detections) after searching
is complete. All outputs are done in the same manner as normal, so
that fluxes of detections will be negative.

\secC{Calculating statistics}

A crucial part of the detection process is estimating the statistics
that define the detection threshold. To determine a threshold, we need
to estimate from the data two parameters: the middle of the noise
distribution (the ``noise level''), and the width of the distribution
(the ``noise spread''). For both cases, we again use robust methods,
using the median and MADFM.

The choice of pixels to be used depend on the analysis method. If the
wavelet reconstruction has been done, the residuals (defined
in the sense of original $-$ reconstruction) are used to estimate the
noise spread of the cube, since the reconstruction should pick out
all significant structure. The noise level (the middle of the
distribution) is taken from the original array.

If smoothing of the cube has been done instead, all noise parameters
are measured from the smoothed array, and detections are made with
these parameters. When the signal-to-noise level is quoted for each
detection (see \S\ref{sec-output}), the noise parameters of the
original array are used, since the smoothing process correlates
neighbouring pixels, reducing the noise level.

If neither reconstruction nor smoothing has been done, then the
statistics are calculated from the original, input array. 

The parameters that are estimated should be representative of the
noise in the cube. For the case of small objects embedded in many
noise pixels (\eg the case of \hi surveys), using the full cube will
provide good estimators. It is possible, however, to use only a
subsection of the cube by setting the parameter \texttt{flagStatSec =
true} and providing the desired subsection to the \texttt{StatSec}
parameter. This subsection works in exactly the same way as the pixel
subsection discussed in \S\ref{sec-input}. Note that this subsection
applies only to the statistics used to determine the threshold. It
does not affect the calculation of statistics in the case of the
wavelet reconstruction.

\secC{Determining the threshold}

Once the statistics have been calculated, the threshold is determined
in one of two ways. The first way is a simple sigma-clipping, where a
threshold is set at a fixed number $n$ of standard deviations above
the mean, and pixels above this threshold are flagged as detected. The
value of $n$ is set with the parameter \texttt{snrCut}. As before, the
value of the standard deviation is estimated by the MADFM, and
corrected by the ratio derived in Appendix~\ref{app-madfm}.

The second method uses the False Discovery Rate (FDR) technique
\citep{miller01,hopkins02}, whose basis we briefly detail here. The
false discovery rate (given by the number of false detections divided
by the total number of detections) is fixed at a certain value
$\alpha$ (\eg $\alpha=0.05$ implies 5\% of detections are false
positives). In practice, an $\alpha$ value is chosen, and the ensemble
average FDR (\ie $\langle FDR \rangle$) when the method is used will
be less than $\alpha$.  One calculates $p$ -- the probability,
assuming the null hypothesis is true, of obtaining a test statistic as
extreme as the pixel value (the observed test statistic) -- for each
pixel, and sorts them in increasing order. One then calculates $d$
where
\[
d = \max_j \left\{ j : P_j < \frac{j\alpha}{c_N N} \right\},
\]
and then rejects all hypotheses whose $p$-values are less than or
equal to $P_d$. (So a $P_i<P_d$ will be rejected even if $P_i \geq
j\alpha/c_N N$.) Note that ``reject hypothesis'' here means ``accept
the pixel as an object pixel'' (\ie we are rejecting the null
hypothesis that the pixel belongs to the background).

The $c_N$ value here is a normalisation constant that depends on the
correlated nature of the pixel values. If all the pixels are
uncorrelated, then $c_N=1$. If $N$ pixels are correlated, then their
tests will be dependent on each other, and so $c_N = \sum_{i=1}^N
i^{-1}$. \citet{hopkins02} consider real radio data, where the pixels
are correlated over the beam. For the calculations done in \duchamp,
$N=2B$, where $B$ is the beam size in pixels, calculated from the FITS
header (if the correct keywords -- BMAJ, BMIN -- are not present, the
size of the beam is taken from the parameter \texttt{beamSize}). The
factor of 2 comes about because we treat neighbouring channels as
correlated. In the case of a two-dimensional image, we just have
$N=B$. 

The theory behind the FDR method implies a direct connection between
the choice of $\alpha$ and the fraction of detections that will be
false positives. These detections, however, are individual pixels,
which undergo a process of merging and rejection (\S\ref{sec-merger}),
and so the fraction of the final list of detected objects that are
false positives will be much smaller than $\alpha$. See the discussion
in \S\ref{sec-notes}.

%\secC{Storage of detected objects in memory}
%
%It is useful to understand how \duchamp stores the detected objects in
%memory while it is running. This makes use of nested C++ classes, so
%that an object is stored as a class that includes the set of detected
%pixels, plus all the various calculated parameters (fluxes, WCS
%coordinates, pixel centres and extrema, flags,...). The set of pixels
%are stored using another class, that stores 3-dimensional objects as a
%set of channel maps, each consisting of a $z$-value and a
%2-dimensional object (a spatial map if you like). This 2-dimensional
%object is recorded using ``run-length'' encoding, where each row (a
%fixed $y$ value) is stored by the starting $x$-value and the length

\secB{Merging and growing detected objects}
\label{sec-merger}

The searching step produces a list of detected objects that will have
many repeated detections of a given object -- for instance, spectral
detections in adjacent pixels of the same object and/or spatial
detections in neighbouring channels. These are then combined in an
algorithm that matches all objects judged to be ``close'', according
to one of two criteria.

One criterion is to define two thresholds -- one spatial and one in
velocity -- and say that two objects should be merged if there is at
least one pair of pixels that lie within these threshold distances of
each other. These thresholds are specified by the parameters
\texttt{threshSpatial} and \texttt{threshVelocity} (in units of pixels
and channels respectively).

Alternatively, the spatial requirement can be changed to say that
there must be a pair of pixels that are \emph{adjacent} -- a stricter,
but perhaps more realistic requirement, particularly when the spatial
pixels have a large angular size (as is the case for 
\hi surveys). This 
method can be selected by setting the parameter
\texttt{flagAdjacent} to 1 (\ie \texttt{true}) in the parameter
file. The velocity thresholding is done in the same way as the first
option.

Once the detections have been merged, they may be ``grown''. This is a
process of increasing the size of the detection by adding nearby
pixels (according to the \texttt{threshSpatial} and
\texttt{threshVelocity} parameters) that are above some secondary
threshold. This threshold is lower than the one used for the initial
detection, but above the noise level, so that faint pixels are only
detected when they are close to a bright pixel. The value of this
threshold is a possible input parameter (\texttt{growthCut}), with a
default value of $2\sigma$.

The use of the growth algorithm is controlled by the
\texttt{flagGrowth} parameter -- the default value of which is
\texttt{false}. If the detections are grown, they are sent through the
merging algorithm a second time, to pick up any detections that now
overlap or have grown over each other.

Finally, to be accepted, the detections must span \emph{both} a
minimum number of channels (enabling the removal of any spurious
single-channel spikes that may be present), and a minimum number of
spatial pixels. These numbers, as for the original detection step, are
set with the \texttt{minChannels} and \texttt{minPix} parameters. The
channel requirement means there must be at least one set of
\texttt{minChannels} consecutive channels in the source for it to be
accepted.