arXiv subjects

Felix Richards

Publications and source records attributed to Felix Richards.

5 recordsLinked to original sources

arXiv2025

Low Surface Brightness structures from annotated deep CFHT images: effects of the host galaxy's properties and environment

Hierarchical galactic evolution models predict that mergers drive galaxy growth, producing low surface brightness (LSB) tidal features that trace galaxies' late assembly. These faint structures encode information about past mergers and are sensitive to the properties and environment of the host galaxy. We investigated the relationships between LSB features and their hosts in a sample of 475 nearby massive galaxies spanning diverse environments (field, groups, Virgo cluster) using deep optical imaging from the Canada-France-Hawaii Telescope (MATLAS, UNIONS/CFIS, VESTIGE, NGVS). Using Jafar, an online annotation tool, we manually annotated tidal features and extended stellar haloes, including 199 tidal tails and 100 streams. Geometric and photometric measurements were extracted to analyse their dependence on galaxy mass, environment, and internal kinematics. At our surface brightness limit of 29 mag$\,$arcsec$^{-2}$, tidal features and stellar haloes contribute 2% and 10% of total galaxy luminosity, respectively. Tidal features are detected in 36% of galaxies, with none fainter than 27.8 mag$\,$arcsec$^{-2}$. The most massive galaxies are twice as likely to host tidal debris, and for early-type galaxies their halos are twice as luminous as those in lower-mass systems, a trend not observed in late-type galaxies. Although small-scale interactions increase the frequency of tidal features, the large-scale environment does not influence it. An anticorrelation between this frequency and rotational support is found, but may reflect the mass-driven effect. We release our database of annotated features for deep learning applications. Our findings confirm that galaxy mass is the dominant factor influencing tidal feature prevalence, consistent with hierarchical formation models.

astro-ph.GA↗

arXiv2024

Multi-scale gridded Gabor attention for cirrus segmentation

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 multi-scale 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.

cs.CV↗

arXiv2024

Panoptic Segmentation of Galactic Structures in LSB Images

We explore the use of deep learning to localise galactic structures in low surface brightness (LSB) images. LSB imaging reveals many interesting structures, though these are frequently confused with galactic dust contamination, due to a strong local visual similarity. We propose a novel unified approach to multi-class segmentation of galactic structures and of extended amorphous image contaminants. Our panoptic segmentation model combines Mask R-CNN with a contaminant specialised network and utilises an adaptive preprocessing layer to better capture the subtle features of LSB images. Further, a human-in-the-loop training scheme is employed to augment ground truth labels. These different approaches are evaluated in turn, and together greatly improve the detection of both galactic structures and contaminants in LSB images.

cs.CV↗

arXiv2022

Characterization of Low Surface Brightness structures in annotated deep images

The characterization of Low Surface Brightness (LSB) stellar structures around galaxies such as tidal debris of on-going or past collisions is essential to constrain models of galactic evolution. Our goal is to obtain quantitative measurements of LSB structures identified in deep images of samples consisting of hundreds of galaxies. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures, as well as artefacts 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 CFHT as part of two large programs: MATLAS and UNIONS/CFIS. Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated to a disrupted satellite, tails formed in major mergers, ghost reflections or cirrus. 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. Our annotation database of observed LSB structures may be used for quantitative analysis and as a training set for machine learning algorithms (abbreviated).

astro-ph.GA↗

arXiv2021

Learnable Gabor modulated complex-valued networks for orientation robustness

Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance. While translation invariance and equivariance is a documented phenomenon of CNNs, sensitivity to other transformations is typically encouraged through data augmentation. We investigate the modulation of complex valued convolutional weights with learned Gabor filters to enable orientation robustness. The resulting network can generate orientation dependent features free of interpolation with a single set of learnable rotation-governing parameters. By choosing to either retain or pool orientation channels, the choice of equivariance versus invariance can be directly controlled. Moreover, we introduce rotational weight-tying through a proposed cyclic Gabor convolution, further enabling generalisation over rotations. We combine these innovations into Learnable Gabor Convolutional Networks (LGCNs), that are parameter-efficient and offer increased model complexity. We demonstrate their rotation invariance and equivariance on MNIST, BSD and a dataset of simulated and real astronomical images of Galactic cirri.

cs.CV↗