Measuring colors of astronomical objects in broad-band or narrow-band images is one of the most basic and common steps in astronomical analysis. Here, we will use Gnuastro’s programs to get a physical scale (area at certain redshifts) of the field we are studying, detect objects in a Hubble Space Telescope (HST) image, measure their colors and identify the ones with the strongest colors, do a visual inspection of these objects and inspect spatial position in the image. After this tutorial, you can also try the Detecting large extended targets tutorial which goes into a little more detail on detecting very low surface brightness signal.
During the tutorial, we will take many detours to explain, and practically demonstrate, the many capabilities of Gnuastro’s programs. In the end you will see that the things you learned during this tutorial are much more generic than this particular problem and can be used in solving a wide variety of problems involving the analysis of data (images or tables). So please don’t rush, and go through the steps patiently to optimally master Gnuastro.
In this tutorial, we’ll use the HSTeXtreme Deep Field dataset. Like almost all astronomical surveys, this dataset is free for download and usable by the public. You will need the following tools in this tutorial: Gnuastro, SAO DS9 24, GNU Wget25, and AWK (most common implementation is GNU AWK26).
This tutorial was first prepared for the “Exploring the Ultra-Low Surface Brightness Universe” workshop (November 2017) at the ISSI in Bern, Switzerland. It was further extended in the “4th Indo-French Astronomy School” (July 2018) organized by LIO, CRAL CNRS UMR5574, UCBL, and IUCAA in Lyon, France. We are very grateful to the organizers of these workshops and the attendees for the very fruitful discussions and suggestions that made this tutorial possible.
Write the example commands manually: Try to type the example commands on your terminal manually 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.
|• Calling Gnuastro's programs||Easy way to find Gnuastro’s programs.|
|• Accessing documentation||Access to manual of programs you are running.|
|• Setup and data download||Setup this template and download datasets.|
|• Dataset inspection and cropping||Crop the flat region to use in next steps.|
|• Angular coverage on the sky||Measure the field size on the sky.|
|• Cosmological coverage||Measure the field size at different redshifts.|
|• Building custom programs with the library||Easy way to build new programs.|
|• Option management and configuration files||Dealing with options and configuring them.|
|• Warping to a new pixel grid||Transforming/warping the dataset.|
|• NoiseChisel and Multiextension FITS files||Running NoiseChisel and having multiple HDUs.|
|• NoiseChisel optimization for detection||Check NoiseChisel’s operation and improve it.|
|• NoiseChisel optimization for storage||Dramatically decrease output’s volume.|
|• Segmentation and making a catalog||Finding true peaks and creating a catalog.|
|• Working with catalogs estimating colors||Estimating colors using the catalogs.|
|• Column statistics color-magnitude diagram||Visualizing column correlations.|
|• Aperture photometry||Doing photometry on a fixed aperture.|
|• Matching catalogs||Easily find corresponding rows from two catalogs.|
|• Finding reddest clumps and visual inspection||Selecting some targets and inspecting them.|
|• Writing scripts to automate the steps||Scripts will greatly help in re-doing things fast.|
|• Citing and acknowledging Gnuastro||How to cite and acknowledge Gnuastro in your papers.|