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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…

We present the use of self-supervised learning to explore and exploit large unlabeled datasets. Focusing on 42 million galaxy images from the latest data release of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we…

Techniques using multiple tracers of the large scale structure of the universe show great promise for examining the fundamentals of our Universe's cosmology. Such techniques rely on the different relationship between the overdensity of…

Simulation-based inference (SBI) has become an important tool in cosmology for extracting additional information from observational data using simulations. However, all cosmological simulations are approximations of the actual universe, and…

We propose a new soft clustering scheme for classifying galaxies in different activity classes using simultaneously 4 emission-line ratios; log([NII ]/Ha), log([SII]/Ha), log([OI]/Ha) and log([OIII]/Hb). We fit 20 multivariate Gaussian…

Despite the large number of discoveries made recently by Fermi, the origins of the so called unidentified gamma-ray sources remain unknown. The large number of these sources suggests that among them there could be a population that…

We present the first search for $z\geqslant7$, continuum-confirmed sources with NIRISS/WFS spectroscopy over the Abell 2744 Frontier Fields cluster, as part of the GLASS-JWST ERS survey. With $\sim15$ hrs of pre-imaging and multi-angle…

We present the fourth data release of JADES, the JWST Advanced Deep Extragalactic Survey, providing deep spectroscopic observations in the two GOODS fields. A companion paper presents the target selection, spectroscopic redshifts and…

We present the results of directed searches for continuous gravitational waves from unknown neutron stars in five Milky Way globular clusters. We carry out the searches in the LIGO data from the first eight months of the fourth…

We present an efficient Bayesian SED-fitting framework tailored to multiwavelength pixel photometry from the JWST Advanced Deep Extragalactic Survey (JADES). Our method employs simulation-based inference to enable rapid posterior sampling…

We investigate the environmental dependence of galaxy properties at $z\sim2.5$ using the Ly$\alpha$ Tomography IMACS Survey (LATIS), which provides high-resolution three-dimensional maps of intergalactic medium (IGM) overdensity via…

Much of our current understanding of neutral, atomic gas in galaxies comes from radio observations of the nearby Universe. Until the next generation of instruments allow us to push to much higher redshifts, we must rely mostly upon…

The Cosmic Lens All-Sky Survey (CLASS) is an international collaborative program which has obtained high-resolution radio images of over 10000 flat-spectrum radio sources in order to create the largest and best studied statistical sample of…

We compare the statistical properties of giant gravitationally lensed arcs produced in matched simulated and observed cluster samples. The observed sample consists of 10 X-ray selected clusters at redshifts z ~ 0.2 imaged with HST by Smith…

Taking advantage of the fact that the cardinalities of hidden variables in network scenarios can be assumed to be finite without loss of generality, a numerical tool for finding explicit local models that reproduce a given statistical…

We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions…

We introduce and provide the scientific motivation for a wide-field photometric and spectroscopic chemodynamical survey of nearby early-type galaxies (ETGs) and their globular cluster (GC) systems. The SLUGGS (SAGES Legacy Unifying…

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and…

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in…

We present the results of an unbiased radio search for gravitational lensing events with image separations between 15 and 60 arcsec, which would be associated with clusters of galaxies with masses >10^{13-14}M_{\sun}. A parent population of…