相关论文: A Deep Learning Approach for Characterizing Major …
Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree'' structure. Previous work has shown these merger trees are…
Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z>1) galaxies but their formation and role in galaxy evolution remain unclear. High-resolution observations of low-redshift…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
Non-parametric morphology statistics have been used for decades to classify galaxies into morphological types and identify mergers in an automated way. In this work, we assess how reliably we can identify galaxy post-mergers with…
We study galaxy mergers using a high-resolution cosmological hydro/N-body simulation with star formation, and compare the measured merger timescales with theoretical predictions based on the Chandrasekhar formula. In contrast to Navarro et…
Traditionally, CNN models possess hierarchical structures and utilize the feature mapping of the last layer to obtain the prediction output. However, it can be difficulty to settle the optimal network depth and make the middle layers learn…
Several studies have suggested that the observed size evolution of massive early-type galaxies (ETGs) can be explained as a combination of dry mergers and progenitor bias, at least since z\sim1. In this paper we carry out a new test of the…
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…
Several deep observations such as those carried out at the Canada-France-Hawaii Telescope (CFHT) have revealed prominent Low Surface Brightness (LSB) fine structures that change the apparent morphology of galaxies. Previous photometry…
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique…
Slow rotator galaxies are distinct amongst galaxy populations, with simulations suggesting that a mix of minor and major mergers are responsible for their formation. A promising path to resolve outstanding questions on the type of merger…
A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of…
Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images. Some researchers have struggled to enhance the parameters' efficiency using grouped convolution. However, the relation between…
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional models, the edge image generated is ambiguous, and the edge lines…
Spatially resolved kinematics have been used to determine the dynamical status of star-forming galaxies with ambiguous morphologies, and constrain the importance of galaxy interactions during the assembly of galaxies. However, measuring the…
We present the results of an Eulerian adaptive mesh refinement (AMR) hydrodynamical and N-body simulation in a $\Lambda$CDM cosmology. The simulation incorporates common cooling and heating processes for primordial gas. A specific halo…
We investigate the effect of dry merging on the color-magnitude relation (CMR) of galaxies and find that the amount of merging predicted by a hierarchical model results in a red sequence that compares well with the observed low-redshift…
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…
Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whether other observable galaxy features, such as their…
Within the next decade, ground based gravitational wave detectors are in principle capable of determining the compact object merger rate per unit volume of the local universe to better than 20% with more than 30 detections. We argue that…