Related papers: Deep learning for galaxy surface brightness profil…
In modern computer vision, images are typically represented as a fixed uniform grid with some stride and processed via a deep convolutional neural network. We argue that deforming the grid to better align with the high-frequency image…
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into…
To address the high levels of uncertainty associated with photovoltaic energy, an increasing number of studies focusing on short-term solar forecasting (i.e. nowcasting) have been published. Most of these studies use deep-learning-based…
The measurement of galaxy morphological parameters from astronomical images features in a wide range of modern analyses, including galaxy evolution and cosmological weak lensing studies. The precision and accuracy of morphological parameter…
We present a new method which constructs an HI super-profile of a galaxy which is based on profile decomposition analysis. The decomposed velocity profiles of an HI data cube with an optimal number of Gaussian components are co-added after…
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong…
The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the…
Due to the unprecedented depth of the upcoming ground-based Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, approximately two-thirds of the galaxies are likely to be affected by blending - the overlap of physically…
Encoder-Decoder networks such as U-Nets have been applied successfully in a wide range of computer vision tasks, especially for image segmentation of different flavours across different fields. Nevertheless, most applications lack of a…
With current and upcoming experiments such as WFIRST, Euclid and LSST, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data…
In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. These…
One of the most important properties of a galaxy is the total stellar mass, or equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be estimated from stellar population synthesis. Currently, a galaxy's…
AI-enhanced approaches are becoming common in astronomical data analysis, including in the galaxy morphological classification. In this study we develop an approach that enhances galaxy classification by incorporating an image denoising…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are…
Dense pixel-wise image prediction has been advanced by harnessing the capabilities of Fully Convolutional Networks (FCNs). One central issue of FCNs is the limited capacity to handle joint upsampling. To address the problem, we present a…
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for…
Estimating redshift is a central task in astrophysics, but its measurement is costly and time-consuming. In addition, current image-based methods are often validated on homogeneous datasets. The development and comparison of networks able…
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain…
For a galaxy, given its observed rotation curve, can one directly infer parameters of the dark matter density profile (such as dark matter particle mass $m$, scaling parameter $s$, core-to-envelope transition radius $r_t$ and NFW scale…