Related papers: GalacticFlow: Learning a Generalized Representatio…
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a…
Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical…
Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty…
Strong gravitational lensing is a powerful tool for probing the nature of dark matter, as lensing signals are sensitive to the dark matter substructure within the lensing galaxy. We present a comparative analysis of strong gravitational…
Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star…
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules. With an initial training set of only 100 small molecules, FastFlows…
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the…
Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match…
Utilizing high-resolution large-scale galaxy formation simulations of the standard cold dark matter model, we examine global trends in the evolution of galaxies due to gravitational shock heating by collapse of large halos and large-scale…
We apply Monte Carlo Markov Chain (MCMC) methods to large-scale simulations of galaxy formation in a LambdaCDM cosmology in order to explore how star formation and feedback are constrained by the observed luminosity and stellar mass…
We present a suite of six high-resolution chemo-dynamical simulations of isolated galaxies, spanning observed disk-dominated environments on the star-forming main sequence, as well as quenched, bulge-dominated environments. We compare and…
We present an analytic formalism that describes the evolution of the stellar, gas, and metal content of galaxies. It is based on the idea, inspired by hydrodynamic simulations, that galaxies live in a slowly-evolving equilibrium between…
Dynamically cold stellar streams from tidally dissolved globular clusters (GCs) serve as excellent tools to measure the Galactic mass distribution and show promise to probe the nature of dark matter. For successful application of these…
We examine the effects of galaxy outflows on the formation of dwarf galaxies in numerical simulations of the high-redshift Universe. Using a Smoothed Particle Hydrodynamic code, we conduct two detailed simulations of a (5.2 Mpc/h)^3…
In this paper, we present a holistic view of the detection, characterization, and origin of stellar streams in the disk of a simulated Milky Way-like galaxy. The star-by-star simulation of the Galaxy evolves stars born in clusters in a…
Galaxies can form in a sufficiently deep gravitational potential so that efficient gas cooling occurs. We estimate that such potential is provided by a halo of mass $M \gtsim M_{c} \approx 7.0 \times 10^{12} ~ (\Delta_{c}(z)…
We use galaxy and dark halo data from the public database for the Millennium Simulation to study the growth of galaxies in the De Lucia et al. (2006) model for galaxy formation. Previous work has shown this model to reproduce many aspects…
High resolution gravity plus smoothed particle hydrodynamics simulations are used to study the formation of galaxies within the context of hierarchical structure formation. The simulations have sufficient dynamic range to resolve from ten…
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations.…
Major progress has been made over the last few years in understanding hydrodynamical processes on cosmological scales, in particular how galaxies get their baryons. There is increasing recognition that a large part of the baryons accrete…