Related papers: A Deep Learning Approach for Characterizing Major …
We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the…
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across…
We study how to measure the galaxy merger rate from the observed close pair count. Using a high-resolution N-body/SPH cosmological simulation, we find an accurate scaling relation between galaxy pair counts and merger rates down to a…
Galaxy mergers play a crucial role in shaping the morphology, the star formation, and the mass growth of galaxies across cosmic time. While mergers have been extensively investigated in the local Universe, the evolution of their frequency…
Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy…
We measure the number of companions per galaxy (Nc) as a function of r-band absolute magnitude for both the Sloan Digital Sky Survey and the Croton et al. (2006) semi-analytic catalog applied to the Millennium Run simulation. For close…
A six-dimensional parameter space based on high-resolution numerical simulations of isolated binary galaxy collisions has been constructed to investigate the dynamical friction timescales, $\tau_{\rm mer}$, for major mergers. Our…
Determining how galaxies accumulate stellar mass is paramount to understanding the Universe. Two primary mechanisms drive this process: star-formation (SF) & mergers. Our understanding of star formation, and to some degree the processes…
We use a large sample of $\sim 350,000$ galaxies constructed by combining the UKIDSS UDS, VIDEO/CFHT-LS, UltraVISTA/COSMOS and GAMA survey regions to probe the major merging histories of massive galaxies ($>10^{10}\ \mathrm{M}_\odot$) at…
Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature…
Imaging spectroscopy using the new Palomar Integral Field Spectrograph is presented for the Pa-alpha line in four ultraluminous infrared galaxies. In most cases, we are able to establish the geometry of the merger, and thus estimate the…
Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers, and their properties can answer questions about the progenitor galaxies involved in the…
We have constructed merger trees for galaxies in the Illustris Simulation by directly tracking the baryonic content of subhalos. These merger trees are used to calculate the galaxy-galaxy merger rate as a function of descendant stellar…
We investigate whether combining gas and stellar kinematic maps provides measurable advantages in recovering galaxy mass profiles, compared to using single-component maps alone. While traditional methods struggle to integrate multi-tracer…
We measure the role of major and minor mergers in forming the stellar masses of galaxies over $0<z<3$ using a combination of $\sim 3.25$ deg$^{2}$ of the deepest ground based near-infrared imaging taken to date as part of the REFINE survey.…
Model merging is an effective strategy to merge multiple models for enhancing model performances, and more efficient than ensemble learning as it will not introduce extra computation into inference. However, limited research explores if the…
[abridged]We calculate multi-wavelength spectral energy distributions (SEDs) (spanning optical to millimeter wavelengths) from simulations of major galaxy mergers with black hole feedback which produce submillimeter bright galaxies (SMGs),…
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…
Gravitational waves inform about the probable distances at which an observed signal originated. This information when combined over multiple observations is used in the modeling of the redshift evolution of the merger rate. This is an…