相关论文: Neural Deprojection of Galaxy Stellar Mass Profile…
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…
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…
Measuring the sum of the three active neutrino masses, $M_\nu$, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the…
The Planetary Nebula Spectrograph is a dedicated instrument for measuring radial velocity of individual Planetary Nebulae (PNe) in galaxies. This new instrument is providing crucial data with which to probe the structure of dark halos in…
We introduce a new tool for 'unmasking' the composition of neutron star (NS) interiors which is based on the fact that the state of matter at high densities determines the statistics of both NS observables, the temperature-age (TA) data as…
The dynamical evolution of supermassive black-hole binary systems is tethered on large scales to the merger rate of massive galaxies, and on small scales to the stellar and gaseous environments of galactic cores. The population of these…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
Recent advancements in Neural Architecture Search(NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial…
Precise determination of galaxy cluster masses is crucial for establishing reliable mass-observable scaling relations in cluster cosmology. We employ graph neural networks (GNNs) to estimate galaxy cluster masses from radially sampled…
In the central regions of active galaxies, dense molecular medium are exposed to various types of radiation and energy injections, such as UV, X-ray, cosmic ray, and shock dissipation. With the rapid progress of chemical models and…
Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to…
We have developed a new numerical code to simulate the joint evolution of a massive black hole (MBH) and a surrounding stellar cluster at the center of a galaxy. The physics treated in the simulations include: 2-body relaxation, stellar…
Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50% in the densest regions of the universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities…
In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency of galaxies to naturally align towards overdensities, an…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
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…
We describe AMUSE, the Astrophysical Multipurpose Software Environment, a programming framework designed to manage multi-scale, multi-physics simulations in a hierarchical, extensible, and internally consistent way. Constructed as a…
We present an artificial neural network design in which past and present-day properties of dark matter halos and their local environment are used to predict time-resolved star formation histories and stellar metallicity histories of central…
Evidence for a bar at the center of the Milky Way triggered a renewed enthusiasm for dynamical modelling of the Galactic bar-bulge. Our goal is to compare the kinematics of a sample of tracers, planetary nebulae, widely distributed over the…
Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To…