Related papers: Galaxy Model Subtraction with a Convolutional Deno…
In recent years, deep learning models have been successfully employed for augmenting low-resolution cosmological simulations with small-scale information, a task known as "super-resolution". So far, these cosmological super-resolution…
This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation, i.e., point clouds, involves discrete samples of the…
We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics imaging datasets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from…
Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space…
Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…
Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward modelling technique allows one to account for the PSF to effectively study semi-resolved galaxies. However, using a…
Weak gravitational lensing can cause displacements, magnification, rotation and shearing of the images of distant galaxies. Most studies focus on the shear and magnification effects since they are more easily observed. In this paper we…
Generative priors have been shown to provide improved results over sparsity priors in linear inverse problems. However, current state of the art methods suffer from one or more of the following drawbacks: (a) speed of recovery is slow; (b)…
Cosmic shear is a primary cosmological probe for several present and upcoming surveys investigating dark matter and dark energy, such as Euclid or WFIRST. The probe requires an extremely accurate measurement of the shapes of millions of…
We present a new HI modelling tool called \textsc{Galactus}. The program has been designed to perform automated fits of disc-galaxy models to observations. It includes a treatment for the self-absorption of the gas. The software has been…
The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we…
I introduce $Profiler$, a new, user-friendly program written in $Python$ and designed to analyse the radial surface brightness profiles of galaxies. With an intuitive graphical user interface, $Profiler$ can accurately model a wide range of…
Current constraints on models of galaxy evolution rely on morphometric catalogs extracted from multi-band photometric surveys. However, these catalogs are altered by selection effects that are difficult to model, that correlate in non…
Recent advancements in areas such as natural language processing and computer vision rely on intricate and massive models that have been trained using vast amounts of unlabelled or partly labeled data and training or deploying these…
Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented…
We apply the iterative MCS deconvolution method (ISMCS) to near-IR HST archives data of seven gravitationally lensed quasars currently monitored by the COSMOGRAIL collaboration: HE 0047-1756, RX J1131-1231, SDSS J1138+0314, SDSS J1155+6346,…
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional inputs, and generates…
We present a model-independent method of quantifying galaxy evolution in high- resolution images, which we apply to the Hubble Deep Field (HDF). Our procedure is to k-correct the pixels belonging to the images of a complete set of bright…
It has been claimed that traditional models struggle to explain the tentative detection of the 21\,cm absorption trough centered at $z\sim17$ measured by the EDGES collaboration. On the other hand, it has been shown that the EDGES results…
Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory…