相关论文: Detecting Galaxy Tidal Features Using Self-Supervi…
Aims: We present a new method that uses luminosity or stellar mass functions combined with clustering measurements to select samples of galaxies at different redshifts likely to follow a progenitor-to-descendant relationship. As the method…
Galaxy-scale strong gravitational lenses are valuable objects for a variety of astrophysical and cosmological applications. Strong lensing galaxies are rare, so efficient search methods, such as convolutional neural networks, are often used…
Merging and interactions can radically transform galaxies. However, identifying these events based solely on structure is challenging as the status of observed mergers is not easily accessible. Fortunately, cosmological simulations are now…
Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties…
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive…
The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral…
We combine a N-body simulation algorithm with a subgrid treatment of galaxy formation, mergers, and tidal destruction, and an observed conditional luminosity function Phi(L|M), to study the origin and evolution of galactic and extragalactic…
We use simulations of merging galaxies to explore the sensitivity of the morphology of tidal tails to variations of the halo mass distributions in the parent galaxies. Our goal is to constrain the mass of dark halos in well-known merging…
We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions…
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is…
Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at…
We analyze high resolution, N-body hydrodynamical simulations of fiducial galaxy clusters to probe tidal stripping of the dark matter subhalos. These simulations include a prescription for star formation allowing us to track the fate of the…
The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here we present a machine…
We present a deep broadband optical imaging study of a complete sample of luminous elliptical galaxies (M_B<-20) at distances 15 Mpc - 50 Mpc, selected from the Tully catalog of nearby galaxies. The images are flat to ~0.35% across the 20'…
The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observations increase, more blended sources will be detected. This…
Self-supervised representation learning is able to learn semantically meaningful features; however, much of its recent success relies on multiple crops of an image with very few objects. Instead of learning view-invariant representation…
Detecting small targets in sea clutter is challenging due to dynamic maritime conditions. Existing solutions either model sea clutter for detection or extract target features based on clutter-target echo differences, including statistical…
Increasingly large areas in cosmic shear surveys lead to a reduction of statistical errors, necessitating to control systematic errors increasingly better. One of these systematic effects was initially studied by Hartlap et al. in 2011,…
We report on a search for new low-surface-brightness galaxies (LSBGs) using Sloan Digital Sky Survey (SDSS) data within the GAMA equatorial fields. The search method consisted of masking objects detected with SDSS photo, combining gri…
Low-level 3D representations, such as point clouds, meshes, NeRFs and 3D Gaussians, are commonly used for modeling 3D objects and scenes. However, cognitive studies indicate that human perception operates at higher levels and interprets 3D…