Context. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a need for objective quality criteria, and automated methods to optimise parameter settings for these software tools. Aims. We present a comparison of several tools developed to perform this task: namely SExtractor, ProFound, NoiseChisel, and MTObjects. In particular, we focus on evaluating performance in situations that present challenges for detection. For example, faint and diffuse galaxies; extended structures, such as streams; and objects close to bright sources. Furthermore, we develop an automated method to optimise the parameters for the above tools. Methods. We present four different objective segmentation quality measures, based on precision, recall, and a new measure for the correctly identified area of sources. Bayesian optimisation is used to find optimal parameter settings for each of the four tools when applied to simulated data, for which a ground truth is known. After training, the tools are tested on similar simulated data in order to provide a performance baseline. We then qualitatively assess tool performance on real astronomical images from two different surveys. Results. We determine that when area is disregarded, all four tools are capable of broadly similar levels of detection completeness, while only NoiseChisel and MTObjects are capable of locating the faint outskirts of objects. MTObjects achieves the highest scores on all tests for all four quality measures, whilst SExtractor obtains the highest speeds. No tool has sufficient speed and accuracy to be well suited to large-scale automated segmentation in its current form.

Context. Low surface brightness (LSB) dwarf galaxies in galaxy clusters are an interesting group of objects as their contribution to the galaxy luminosity function and their evolutionary paths are not yet clear. Increasing the completeness of our galaxy catalogs is crucial for understanding these galaxies, which have effective surface brightnesses below 23 mag arcsec−2 (in optical). Progress is continuously being made via the performance of deep observations, but detection depth and the quantification of the completeness can also be improved via the application of novel approaches in object detection. For example, the Fornax Deep Survey (FDS) has revealed many faint galaxies that can be visually detected from the images down to a surface brightness level of 27 mag arcsec−2, whereas traditional detection methods, such as using Source Extractor (SE), fail to find them. Aims. In this work we use a max-tree based object detection algorithm (Max-Tree Objects, MTO) on the FDS data in order to detect previously undetected LSB galaxies. After extending the existing Fornax dwarf galaxy catalogs with this sample, our goal is to understand the evolution of LSB dwarfs in the cluster. We also study the contribution of the newly detected galaxies to the faint end of the luminosity function. Methods. We test the detection completeness and parameter extraction accuracy of MTO using simulated and real images. We then apply MTO to the FDS images to identify LSB candidates. The identified objects are fitted with 2D Sérsic models using GALFIT and classified as imaging artifacts, likely cluster members, or background galaxies based on their morphological appearance, colors, and structure. Results. With MTO, we are able to increase the completeness of our earlier FDS dwarf catalog (FDSDC) 0.5–1 mag deeper in terms of total magnitude and surface brightness. Due to the increased accuracy in measuring sizes of the detected objects, we also add many small galaxies to the catalog that were previously excluded as their outer parts had been missed in detection. We detect 265 new LSB dwarf galaxies in the Fornax cluster, which increases the total number of known dwarfs in Fornax to 821. Using the whole cluster dwarf galaxy population, we show that the luminosity function has a faint-end slope of α = −1.38 ± 0.02. We compare the obtained luminosity function with different environments studied earlier using deep data but do not find any significant differences. On the other hand, the Fornax-like simulated clusters in the IllustrisTNG cosmological simulation have shallower slopes than found in the observational data. We also find several trends in the galaxy colors, structure, and morphology that support the idea that the number of LSB galaxies is higher in the cluster center due to tidal forces and the age dimming of the stellar populations. The same result also holds for the subgroup of large LSB galaxies, so-called ultra-diffuse galaxies.

Context. Low surface brightness (LSB) dwarf galaxies in galaxy clusters are an interesting group of objects as their contribution to the galaxy luminosity function and their evolutionary paths are not yet clear. Increasing the completeness of our galaxy catalogs is crucial for understanding these galaxies, which have effective surface brightnesses below 23 mag arcsec −2 (in optical). Progress is continuously being made via the performance of deep observations, but detection depth and the quantification of the completeness can also be improved via the application of novel approaches in object detection. For example, the Fornax Deep Survey (FDS) has revealed many faint galaxies that can be visually detected from the images down to a surface brightness level of 27 mag arcsec −2 , whereas traditional detection methods, such as using Source Extractor (SE), fail to find them. Aims. In this work we use a max-tree based object detection algorithm (Max-Tree Objects, MTO) on the FDS data in order to detect previously undetected LSB galaxies. After extending the existing Fornax dwarf galaxy catalogs with this sample, our goal is to understand the evolution of LSB dwarfs in the cluster. We also study the contribution of the newly detected galaxies to the faint end of the luminosity function. Methods. We test the detection completeness and parameter extraction accuracy of MTO using simulated and real images. We then apply MTO to the FDS images to identify LSB candidates. The identified objects are fitted with 2D Sérsic models using GALFIT and classified as imaging artifacts, likely cluster members, or background galaxies based on their morphological appearance, colors, and structure.Results. With MTO, we are able to increase the completeness of our earlier FDS dwarf catalog (FDSDC) 0.5-1 mag deeper in terms of total magnitude and surface brightness. Due to the increased accuracy in measuring sizes of the detected objects, we also add many small galaxies to the catalog that were previously excluded as their outer parts had been missed in detection. We detect 265 new LSB dwarf galaxies in the Fornax cluster, which increases the total number of known dwarfs in Fornax to 821. Using the whole cluster dwarf galaxy population, we show that the luminosity function has a faint-end slope of α=-1.38±0.02. We compare the obtained luminosity function with different environments studied earlier using deep data but do not find any significant differences. On the other hand, the Fornax-like simulated clusters in the IllustrisTNG cosmological simulation have shallower slopes than found in the observational data. We also find several trends in the galaxy colors, structure, and morphology that support the idea that the number of LSB galaxies is higher in the cluster center due to tidal forces and the age dimming of the stellar populations. The same result also holds for the subgroup of large LSB galaxies, so-called ultra-diffuse galaxies.

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