To help new users have a smooth and easy start with Gnuastro, in this chapter several thoroughly elaborated tutorials, or cookbooks, are provided. These tutorials demonstrate the capabilities of different Gnuastro programs and libraries, along with tips and guidelines for the best practices of using them in various realistic situations.
We strongly recommend going through these tutorials to get a good feeling of how the programs are related (built in a modular design to be used together in a pipeline), very similar to the core Unix-based programs that they were modeled on. Therefore these tutorials will help in optimally using Gnuastro’s programs (and generally, the Unix-like command-line environment) effectively for your research.
The first three tutorials (General program usage tutorial and Detecting large extended targets and Building the extended PSF) use real input datasets from some of the deep Hubble Space Telescope (HST) images, the Sloan Digital Sky Survey (SDSS) and the Javalambre Photometric Local Universe Survey (J-PLUS) respectively. Their aim is to demonstrate some real-world problems that many astronomers often face and how they can be solved with Gnuastro’s programs. The fourth tutorial (Sufi simulates a detection) focuses on simulating astronomical images, which is another critical aspect of any analysis!
The ultimate aim of General program usage tutorial is to detect galaxies in a deep HST image, measure their positions, magnitude and select those with the strongest colors. In the process, it takes many detours to introduce you to the useful capabilities of many of the programs. So please be patient in reading it. If you do not have much time and can only try one of the tutorials, we recommend this one.
Detecting large extended targets deals with a major problem in astronomy: effectively detecting the faint outer wings of bright (and large) nearby galaxies to extremely low surface brightness levels (roughly one quarter of the local noise level in the example discussed). Besides the interesting scientific questions in these low-surface brightness features, failure to properly detect them will bias the measurements of the background objects and the survey’s noise estimates. This is an important issue, especially in wide surveys. Because bright/large galaxies and stars20, cover a significant fraction of the survey area.
Building the extended PSF tackles an important problem in astronomy: how the extract the PSF of an image, to the largest possible extent, without assuming any functional form. In Gnuastro we have multiple installed scripts for this job. Their usage and logic behind best tuning them for the particular step, is fully described in this tutorial, on a real dataset. The tutorial concludes with subtracting that extended PSF from the science image; thus giving you a cleaner image (with no scattered light of the brighter stars) for your higher-level analysis.
Sufi simulates a detection has a fictional21 setting! Showing how Abd al-rahman Sufi (903 – 986 A.D., the first recorded description of “nebulous” objects in the heavens is attributed to him) could have used some of Gnuastro’s programs for a realistic simulation of his observations and see if his detection of nebulous objects was trust-able. Because all conditions are under control in a simulated/mock environment/dataset, mock datasets can be a valuable tool to inspect the limitations of your data analysis and processing. But they need to be as realistic as possible, so this tutorial is dedicated to this important step of an analysis (simulations).
There are other tutorials also, on things that are commonly necessary in astronomical research: In Detecting lines and extracting spectra in 3D data, we use MUSE cubes (an IFU dataset) to show how you can subtract the continuum, detect emission-line features, extract spectra and build pseudo narrow-band images. In Color channels in same pixel grid we demonstrate how you can warp multiple images into a single pixel grid (often necessary with multi-wavelength data), and build a single color image. In Moiré pattern in stacking and its correction we show how you can avoid the unwanted Moiré pattern which happens when warping separate exposures to build a stacked/co-add deeper image. In Zero point of an image we review the process of estimating the zero point of an image using a reference image or catalog. Finally, in Pointing pattern design we show the process by which you can simulate a dither pattern to find the best observing strategy for your next exciting scientific project.
In these tutorials, we have intentionally avoided too many cross references to make it more easy to read. For more information about a particular program, you can visit the section with the same name as the program in this book. Each program section in the subsequent chapters starts by explaining the general concepts behind what it does, for example, see Convolve. If you only want practical information on running a program, for example, its options/configuration, input(s) and output(s), please consult the subsection titled “Invoking ProgramName”, for example, see Invoking NoiseChisel. For an explanation of the conventions we use in the example codes through the book, please see Conventions.
Stars also have similarly large and extended wings due to the point spread function, see Point spread function.
The two historically motivated tutorials (Sufi simulates a detection is not intended to be a historical reference (the historical facts of this fictional tutorial used Wikipedia as a reference).) This form of presenting a tutorial was influenced by the PGF/TikZ and Beamer manuals. They are both packages in TeX and LaTeX, the first is a high-level vector graphic programming environment, while with the second you can make presentation slides. On a similar topic, there are also some nice words of wisdom for Unix-like systems called Rootless Root. These also have a similar style but they use a mythical figure named Master Foo. If you already have some experience in Unix-like systems, you will definitely find these Unix Koans entertaining/educative.