The outer wings of large and extended objects can sink into the noise very gradually and can have a large variety of shapes (for example due to tidal interactions). Therefore separating the outer boundaries of the galaxies from the noise can be particularly tricky. Besides causing an under-estimation in the total estimated brightness of the target, failure to detect such faint wings will also cause a bias in the noise measurements, thereby hampering the accuracy of any measurement on the dataset. Therefore even if they don’t constitute a significant fraction of the target’s light, or aren’t your primary target, these regions must not be ignored. In this tutorial, we’ll walk you through the strategy of detecting such targets using NoiseChisel.
Don’t start with this tutorial: If you haven’t already completed General program usage tutorial, we strongly recommend going through that tutorial before starting this one. Basic features like access to this book on the command-line, the configuration files of Gnuastro’s programs, benefiting from the modular nature of the programs, viewing multi-extension FITS files, or using NoiseChisel’s outputs are discussed in more detail there.
We’ll try to detect the faint tidal wings of the beautiful M51 group39 in this tutorial.
We’ll use a dataset/image from the public Sloan Digital Sky Survey, or SDSS.
Due to its more peculiar low surface brightness structure/features, we’ll focus on the dwarf companion galaxy of the group (or NGC 5195).
To get the image, you can use SDSS’s Simple field search tool.
As long as it is covered by the SDSS, you can find an image containing your desired target either by providing a standard name (if it has one), or its coordinates.
To access the dataset we will use here, write
NGC5195 in the “Object Name” field and press “Submit” button.
Type the example commands: Try to type the example commands on your terminal and use the history feature of your command-line (by pressing the “up” button to retrieve previous commands). Don’t simply copy and paste the commands shown here. This will help simulate future situations when you are processing your own datasets.
You can see the list of available filters under the color image. For this demonstration, we’ll use the r-band filter image. By clicking on the “r-band FITS” link, you can download the image. Alternatively, you can just run the following command to download it with GNU Wget40. To keep things clean, let’s also put it in a directory called ngc5195. With the -O option, we are asking Wget to save the downloaded file with a more manageable name: r.fits.bz2 (this is an r-band image of NGC 5195, which was the directory name).
$ mkdir ngc5195 $ cd ngc5195 $ topurl=https://dr12.sdss.org/sas/dr12/boss/photoObj/frames $ wget $topurl/301/3716/6/frame-r-003716-6-0117.fits.bz2 -Or.fits.bz2
This server keeps the files in a Bzip2 compressed file format.
So we’ll first decompress it with the following command.
By convention, compression programs delete the original file (compressed when uncompressing, or uncompressed when compressing).
To keep the original file, you can use the --keep or -k option which is available in most compression programs for this job.
Here, we don’t need the compressed file any more, so we’ll just let
bunzip delete it for us and keep the directory clean.
$ bunzip2 r.fits.bz2
|• NoiseChisel optimization||Optimize NoiseChisel to dig very deep.|
|• Achieved surface brightness level||Measure how much you detected.|
To make the command easier to view on screen or in a page, we have defined the top URL of the image as the
topurl shell variable.
You can just replace the value of this variable with
$topurl in the