Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varying sizes, either through visual inspection or more recently with deep learning. Detailed analyses including photometry have been carried out for a small number of objects, essentially because of the lack of convenient tools able to precisely characterize tidal structures around large samples of galaxies. Aims. Our goal is to characterize in detail, and in particular obtain quantitative measurements, of LSB structures identified in deep images of samples consisting of hundreds of galaxies. Methods. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures with precision, as well as artifacts or foreground structures. All parameters are automatically stored in a database which may be queried to retrieve quantitative measurements. We annotated LSB structures around 352 nearby massive galaxies with deep images obtained with the Canada-France-Hawaii Telescope as part of two large programs: Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) and Ultraviolet Near Infrared Optical Northern Survey (UNIONS)/Canada-France Imaging Survey (CFIS). Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated with a disrupted satellite, tails that formed in major mergers, ghost reflections, or cirrus. Results. From our database containing 8441 annotations, the area, size, median surface brightness, and distance to the host of 228 structures were computed. The results confirm the fact that tidal structures defined as streams are thinner than tails, as expected by numerical simulations. In addition, tidal tails appear to exhibit a higher surface brightness than streams (by about 1 mag), which may be related to different survival times for the two types of collisional debris. We did not detect any tidal feature fainter than 27.5 mag arcsec −2 , while the nominal surface brightness limits of our surveys range between 28.3 and 29 mag arcsec −2 , a difference that needs to be taken into account when estimating the sensitivity of future surveys to identify LSB structures. Conclusions. We compiled an annotation database of observed LSB structures around nearby massive galaxies including tidal features that may be used for quantitative analysis and as a training set for machine learning algorithms.

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Extensive and exhaustive cataloguing of astronomical objects is imperative for studies seeking to understand mechanisms which drive the universe. Such cataloguing tasks can be tedious, time consuming and demand a high level of domain specific knowledge. Past astronomical imaging surveys have been catalogued through mostly manual effort. Immi-nent imaging surveys, however, will produce a magnitude of data that cannot be feasibly processed through manual cataloguing. Furthermore, these surveys will capture objects fainter than the night sky, termed low surface brightness objects, and at unprecedented spatial resolution owing to advancements in astronomical imaging. In this thesis, we in-vestigate the use of deep learning to automate cataloguing processes, such as detection, classification and segmentation of objects. A common theme throughout this work is the adaptation of machine learning methods to challenges specific to the domain of low surface brightness imaging.We begin with creating an annotated dataset of structures in low surface brightness images. To facilitate supervised learning in neural networks, a dataset comprised of input and corresponding ground truth target labels is required. An online tool is presented, allowing astronomers to classify and draw over objects in large multi-spectral images. A dataset produced using the tool is then detailed, containing 227 low surface brightness images from the MATLAS survey and labels made by four annotators. We then present a method for synthesising images of galactic cirrus which appear similar to MATLAS images, allowing pretraining of neural networks.A method for integrating sensitivity to orientation in convolutional neural networks is then presented. Objects in astronomical images can present in any given orientation, and thus the ability for neural networks to handle rotations is desirable. We modify con-volutional filters with sets of Gabor filters with different orientations. These orientations are learned alongside network parameters during backpropagation, allowing exact optimal orientations to be captured. The method is validated extensively on multiple datasets and use cases.We propose an attention based neural network architecture to process global contami-nants in large images. Performing analysis of low surface brightness images requires plenty of contextual information and local textual patterns. As a result, a network for processing low surface brightness images should ideally be able to accommodate large high resolu-tion images without compromising on either local or global features. We utilise attention to capture long range dependencies, and propose an efficient attention operator which significantly reduces computational cost, allowing the input of large images. We also use Gabor filters to build an attention mechanism to better capture long range orientational patterns. These techniques are validated on the task of cirrus segmentation in MAT-LAS images, and cloud segmentation on the SWIMSEG database, where state of the art performance is achieved.Following, cirrus segmentation in MATLAS images is further investigated, and a com-prehensive study is performed on the task. We discuss challenges associated with cirrus segmentation and low surface brightness images in general, and present several tech-niques to accommodate them. A novel loss function is proposed to facilitate training of the segmentation model on probabilistic targets. Results are presented on the annotated MATLAS images, with extensive ablation studies and a final benchmark to test the limits of the detailed segmentation pipeline.Finally, we develop a pipeline for multi-class segmentation of galactic structures and surrounding contaminants. Techniques of previous chapters are combined with a popu-lar instance segmentation architecture to create a neural network capable of segmenting localised objects and extended amorphous regions. The process of data preparation for training instance segmentation models is thoroughly detailed. The method is tested on segmentation of five object classes in MATLAS images. We find that unifying the tasks of galactic structure segmentation and contaminant segmentation improves model perfor-mance in comparison to isolating each task.

In this paper, we address the challenge of segmenting global contaminants in large images. The precise delineation of such structures requires ample global context alongside understanding of textural patterns. CNNs specialise in the latter, though their ability to generate global features is limited. Attention measures long range dependencies in images, capturing global context, though at a large computational cost. We propose a gridded attention mechanism to address this limitation, greatly increasing efficiency by processing multiscale features into smaller tiles. We also enhance the attention mechanism for increased sensitivity to texture orientation, by measuring correlations across features dependent on different orientations, in addition to channel and positional attention. We present results on a new dataset of astronomical images, where the task is segmenting large contaminating dust clouds.