Related papers: IAEmu: Learning Galaxy Intrinsic Alignment Correla…
Models of the spatial distribution of dark matter halos must achieve new levels of precision and accuracy in order to satisfy the requirements of upcoming experiments. In this work, we present a halo bias emulator for modeling the…
We propose a new technique for weak gravitational lensing in the radio band making use of polarization information. Since the orientation of a galaxy's polarized emission is both unaffected by lensing and is related to the galaxy's…
AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of…
We examine the properties of atomic hydrogen (HI) associated with galaxies in the EAGLE simulations of galaxy formation. EAGLE's feedback parameters were calibrated to reproduce the stellar mass function and galaxy sizes at $z=0.1$, and we…
We study the optical morphology of galaxies in a large-scale hydrodynamic cosmological simulation, the EAGLE simulation. Galaxy morphologies were characterized using non-parametric statistics (Gini, $M_{20}$, Concentration and Asymmetry)…
This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the…
Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, accurate automated systems to support clinical decision-making during delivery are currently…
Intrinsic alignments of galaxies have been shown to contaminate weak gravitational lensing observables on linear scales, $r>$ 10 $h^{-1}$Mpc, but studies of alignments in the non-linear regime have thus far been inconclusive. We present an…
Photometric classification of Type Ia supernovae (SNe Ia) is critical for cosmological studies but remains difficult due to class imbalance and observational noise. While deep learning models have been explored, they are often…
Blending of galaxies has a major contribution in the systematic error budget of weak lensing studies, affecting photometric and shape measurements, particularly for ground-based, deep, photometric galaxy surveys, such as the Rubin…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
Current and upcoming imaging galaxy surveys are pushing galaxy samples to higher and higher redshifts. This push will be more pronounced for lens galaxies, for which we only need to measure galaxy positions, not shapes. As a result, we will…
Subject of this paper are intrinsic ellipticity correlations between galaxies, their statistical properties, their observability with future surveys and their interference with weak gravitational lensing measurements. Using an angular…
We measure the projected galaxy clustering and galaxy-galaxy lensing signals using the Galaxy And Mass Assembly (GAMA) survey and Kilo-Degree Survey (KiDS) to study galaxy bias. We use the concept of non-linear and stochastic galaxy biasing…
AI models have demonstrated strong predictive capabilities for various tokamak instabilities--including tearing modes (TM), ELMs, and disruptive event--but their opaque nature raises concerns about safety and trustworthiness when applied to…
Using the stellar kinematic maps and ancillary imaging data from the Sydney AAO Multi Integral field (SAMI) Galaxy Survey, the intrinsic shape of kinematically-selected samples of galaxies is inferred. We implement an efficient and…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
Four-variable-independent-regression localization losses, such as Smooth-$\ell_1$ Loss, are used by default in modern detectors. Nevertheless, this kind of loss is oversimplified so that it is inconsistent with the final evaluation metric,…
Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embedding space. However, comparing such spaces across models remains difficult: changes in…
The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and…