1 | \secA{Introduction and getting going quickly} |
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2 | |
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3 | This document provides a user's guide to \duchamp, an object-finder |
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4 | for use on spectral-line data cubes. The basic execution of \duchamp\ |
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5 | is to read in a FITS data cube, find sources in the cube, and produce |
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6 | a text file of positions, velocities and fluxes of the detections, as |
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7 | well as a postscript file of the spectra of each detection. |
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8 | |
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9 | \secB{What to do} |
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10 | |
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11 | So, you have a FITS cube, and you want to find the sources in it. What |
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12 | do you do? First, you need to get \duchamp: there are instructions in |
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13 | Appendix~\ref{app-install} for obtaining and installing it. Once you |
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14 | have it running, the first step is to make an input file that contains |
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15 | the list of parameters. Brief and detailed examples are shown in |
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16 | Appendix~\ref{app-input}. This file provides the input file name, the |
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17 | various output files, and defines various parameters that control the |
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18 | execution. |
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19 | |
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20 | The standard way to run \duchamp\ is by the command |
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21 | \begin{quote} |
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22 | \texttt{Duchamp -p [parameter file]} |
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23 | \end{quote} |
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24 | replacing \texttt{[parameter file]} with the name of the file listing |
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25 | the parameters. |
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26 | |
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27 | An even easier way is to use the default values for all parameters |
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28 | (these are given in Appendix~\ref{app-param} and in the file |
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29 | \texttt{InputComplete} included in the distribution directory) and use |
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30 | the syntax |
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31 | \begin{quote} |
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32 | \texttt{Duchamp -f [FITS file]} |
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33 | \end{quote} |
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34 | where \texttt{[FITS file]} is the file you wish to search. |
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35 | |
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36 | In either case, the program will then work away and give you the list |
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37 | of detections and their spectra. The program execution is summarised |
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38 | below, and detailed in \S\ref{sec-flow}. Information on inputs is in |
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39 | \S\ref{sec-param} and Appendix~\ref{app-param}, and descriptions of |
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40 | the output is in \S\ref{sec-output}. |
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41 | |
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42 | \secB{Guide to terminology and conventions} |
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43 | |
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44 | First, a brief note on the use of terminology in this guide. \duchamp\ |
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45 | is designed to work on FITS ``cubes''. These are FITS\footnote{FITS is |
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46 | the Flexible Image Transport System -- see \citet{hanisch01} or |
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47 | websites such as |
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48 | \href{http://fits.cv.nrao.edu/FITS.html}{http://fits.cv.nrao.edu/FITS.html} |
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49 | for details.} image arrays with three dimensions -- they are assumed |
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50 | to have the following form: the first two dimensions (referred to as |
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51 | $x$ and $y$) are spatial directions (that is, relating to the position |
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52 | on the sky -- often, but not necessarily, corresponding to Equatorial |
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53 | or Galactic coordinates), while the third dimension, $z$, is the |
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54 | spectral direction, which can correspond to frequency, wavelength, or |
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55 | velocity. The three dimensional analogue of pixels are ``voxels'', or |
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56 | volume cells -- a voxel is defined by a unique $(x,y,z)$ location and |
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57 | has a unique value of flux or intensity or brightness (or something |
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58 | equivalent) associated with it. |
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59 | |
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60 | Note that it is possible for the FITS file to have more than three |
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61 | dimensions (for instance, there could be a fourth dimension |
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62 | representing a Stokes parameter). Only the two spatial dimensions and |
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63 | the spectral dimension are read into the array of pixel values that is |
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64 | searched for objects. All other dimensions are ignored\footnote{This |
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65 | actually means that the first pixel only of that axis is used, and the |
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66 | array is read by the \texttt{fits\_read\_subsetnull} command from the |
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67 | \textsc{cfitsio} library.}. Herein, we discuss the data in terms of |
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68 | the three basic dimensions, but you should be aware it is possible for |
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69 | the FITS file to have more than three. Note that the order of the |
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70 | dimensions in the FITS file does not matter. |
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71 | |
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72 | With this setup, each spatial pixel (a given $(x,y)$ coordinate) can |
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73 | be said to be a single spectrum, while a slice through the cube |
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74 | perpendicular to the spectral direction at a given $z$-value is a |
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75 | single channel (the 2-D image is a channel map). |
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76 | |
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77 | Detection involves locating a contiguous group of voxels with fluxes |
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78 | above a certain threshold. \duchamp\ makes no assumptions as to the |
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79 | size or shape of the detected features, other than having |
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80 | user-selected minimum size criteria. Features that are detected are |
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81 | assumed to be positive. The user can choose to search for negative |
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82 | features by setting an input parameter -- this inverts the cube prior |
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83 | to the search (see \S\ref{sec-detection} for details). |
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84 | |
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85 | Finally, note that it is possible to run \duchamp\ on a |
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86 | two-dimensional image (\ie one with no frequency or velocity |
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87 | information), or indeed a one-dimensional array, and many of the |
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88 | features of the program will work fine. The focus, however, is on |
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89 | object detection in three dimensions, one of which is a spectral |
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90 | dimension. |
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91 | |
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92 | \secB{A summary of the execution steps} |
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93 | |
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94 | The basic flow of the program is summarised here -- all steps are |
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95 | discussed in more detail in the following sections. |
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96 | \begin{enumerate} |
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97 | \item The necessary parameters are recorded. |
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98 | |
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99 | How this is done depends on the way the program is run from the |
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100 | command line. If the \texttt{-p} option is used, the parameter file |
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101 | given on the command line is read in, and the parameters therein are |
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102 | read. All other parameters are given their default values (listed in |
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103 | Appendix~\ref{app-param}). |
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104 | |
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105 | If the \texttt{-f} option is used, all parameters are assigned their |
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106 | default values. |
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107 | |
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108 | \item The FITS image is located and read in to memory. |
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109 | |
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110 | The file given is assumed to be a valid FITS file. As discussed |
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111 | above, it can have any number of dimensions, but \duchamp\ only |
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112 | reads in the two spatial and the one spectral dimensions. A subset |
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113 | of the FITS array can be given (see \S\ref{sec-input} for details). |
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114 | |
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115 | \item If requested, a FITS file containing a previously reconstructed |
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116 | or smoothed array is read in. |
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117 | |
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118 | When a cube is either Hanning-smoothed or reconstructed with the |
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119 | \atrous\ wavelet method, the result can be saved to a FITS file, so |
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120 | that subsequent runs of \duchamp\ can read it in to save having to |
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121 | re-do the reconstruction (as it can be relatively time-intensive). |
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122 | |
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123 | \item \label{step-blank} If requested, BLANK pixels are trimmed from |
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124 | the edges, and the baseline of each spectrum is removed. |
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125 | |
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126 | When BLANK pixels are present, they can adversely affect the |
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127 | reconstruction algorithms, as well as increasing the size in memory |
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128 | of the array. This step trims them from the spatial edges, recording |
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129 | the amount trimmed so that they can be added back in later. |
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130 | |
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131 | A spectral baseline can be removed at this point as well. This may |
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132 | be necessary if there is a ripple or other large-scale feature |
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133 | present that will hinder detection of faint sources. |
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134 | |
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135 | \item If the reconstruction method is requested, and the reconstructed |
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136 | array has not been read in at Step 3 above, the cube is |
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137 | reconstructed using the \atrous\ wavelet method. |
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138 | |
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139 | This step uses the \atrous\ method to determine the amount of |
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140 | structure present at various scales. A simple thresholding technique |
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141 | then removes random noise from the cube, leaving the significant |
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142 | signal. This process can greatly reduce the noise level in the cube, |
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143 | enhancing the detectability of sources. |
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144 | |
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145 | \item Alternatively (and if requested), the each spectral channel is |
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146 | Hanning-smoothed by a desired amount. |
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147 | |
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148 | This step considers each spectrum individually, and convolves it |
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149 | with a Hanning filter (with width chosen by the user). This can help |
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150 | to reduce the amount of noise visible in the cube. |
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151 | |
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152 | \item A threshold for the cube is then calculated, based on the pixel |
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153 | statistics (unless a threshold is manually specified by the user). |
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154 | |
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155 | The threshold can either be chosen as a simple $n\sigma$ threshold |
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156 | (\ie so many standard deviations above the mean), or calculated via |
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157 | the ``False Discovery Rate'' method. Alternatively, the threshold |
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158 | can be specified as a simple flux value, without care as to the |
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159 | statistical significance (\eg ``I want every source brighter than |
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160 | 10mJy''). |
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161 | |
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162 | \item Searching for objects then takes place, using the requested |
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163 | thresholding method. |
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164 | |
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165 | The cube is searched in the following manner: each 1-D spectrum is |
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166 | searched, followed by each 2-D image. Any objects detected in each |
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167 | search are added to a master list, or combined with objects already |
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168 | in that list. |
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169 | |
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170 | \item The list of objects is condensed by merging neighbouring objects |
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171 | and removing those deemed unacceptable. |
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172 | |
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173 | There are certain criteria the user can specify that objects must |
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174 | meet: minimum numbers of spatial pixels and spectral channels, and |
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175 | minimum separations between neighbouring objects. Those that do not |
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176 | meet these criteria are either deleted from the list, or merged with |
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177 | their nearby neighbours. |
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178 | |
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179 | \item The baselines and trimmed pixels are replaced prior to output. |
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180 | |
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181 | This is just the inverse of step~\#\ref{step-blank}. |
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182 | |
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183 | \item The details of the detections are written to screen and to the |
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184 | requested output file. |
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185 | |
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186 | Crucial properties of each detection are provided, showing its |
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187 | location, extent, and flux. These are presented in both pixel |
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188 | coordinates and world coordinates (\eg sky position and |
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189 | velocity). Any warning flags are also printed, showing detections to |
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190 | be wary of. |
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191 | |
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192 | \item Maps showing the spatial location of the detections are written. |
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193 | |
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194 | These are 2-dimensional maps, showing where each detection lies on |
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195 | the spatial coverage of the cube. This is provided as an aid to the |
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196 | user so that a quick idea of the distribution of object positions |
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197 | can be gained \eg are all the detections on the edge? |
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198 | |
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199 | Two maps are provided: one is a 0th moment map, showing the 0th |
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200 | moment of each detection in its appropriate position, while the |
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201 | second is a ``detection map'', showing the number of times each |
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202 | spatial pixel was detected in the searching routines. |
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203 | |
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204 | These maps are written to postscript files, and the 0th moment map |
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205 | can also be displayed in a PGPLOT X-window. |
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206 | |
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207 | \item The integrated or peak spectra of each detection are written to a |
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208 | postscript file. |
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209 | |
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210 | The spectral equivalent of the maps -- what is the spectral profile |
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211 | of each detection? Also provided here are basic information for each |
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212 | object (a summary of the information in the results file), as well |
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213 | as a 0th moment map of the detection. |
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214 | |
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215 | \item If requested, the reconstructed or smoothed array can be written |
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216 | to a new FITS file. |
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217 | |
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218 | If either of these procedures were done, the resulting array can be |
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219 | saved as a FITS file for later use. The FITS header will be the same |
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220 | as the input file, with a few additional keywords to identify the |
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221 | file. |
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222 | |
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223 | \end{enumerate} |
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224 | |
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