Related papers: Learning the galaxy-environment connection with gr…
The lack of tangible evidence for non-gravitational interactions between dark and visible sectors drives the need for exploring new avenues of inferring dark matter properties through purely gravitational probes. In particular, addressing…
Galaxy angular momenta (spins) contain valuable cosmological information, complementing their positions and velocities. The baryonic spin direction of galaxies has been probed as a reliable tracer of their host halos and the primordial spin…
Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the intergalactic medium. Yet, it remains unclear how feedback, through its connection to key galaxy…
We train a novel deep learning architecture to perform likelihood-free inference on the value of the cosmological parameters from halo catalogs of the Quijote N-body simulations. Our model takes as input a halo catalog where each halo is…
Intrinsic alignments of galaxies are a significant astrophysical systematic affecting cosmological constraints from weak gravitational lensing. Obtaining numerical predictions from hydrodynamical simulations of expected survey volumes is…
In the standard cosmological model, the assembly of galaxies is primarily driven by the growth of their host dark matter halos. At the center of these halos, however, baryonic processes take over, leading to the plethora of observed galaxy…
Machine learning has the potential to improve the reconstruction of the dark matter profile of galaxies with respect to traditional methods, like rotation curves. We demonstrate on the simulation suite Illustris-TNG that a steerable…
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…
Over the past decade, empirical constraints on the galaxy-dark matter halo connection have significantly advanced our understanding of galaxy evolution. Past techniques have focused on connections between halo properties and galaxy stellar…
We present a new model to describe the galaxy-dark matter connection across cosmic time, which unlike the popular subhalo abundance matching technique is self-consistent in that it takes account of the facts that (i) subhalos are accreted…
We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star-formation rate.…
We demonstrate that generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. Leveraging mock observations from the Illustris simulations, we develop and validate a supervised…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…
We use the IllustrisTNG100 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary subhalo and galactic properties. We construct galaxy mock catalogues adopting a standard…
We explore two widely used empirical models for the galaxy-halo connection, subhalo abundance matching (SHAM) and the halo occupation distribution (HOD) and compare their predictions with the hydrodynamical simulation IllustrisTNG (TNG) for…
We present a new, galaxy-halo model of large-scale structure, in which the galaxies entering a given sample are the fundamental objects. Haloes attach to galaxies, in contrast to the standard halo model, in which galaxies attach to haloes.…
Halo models provide a simple and computationally inexpensive way to investigate the connection between galaxies and their dark matter haloes. However, these models rely on the assumption that the role of baryons can be easily parametrized…
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypotheses in astrophysics and other areas. We use a neural…
Different properties of dark matter haloes, including growth rate, concentration, interaction history, and spin, correlate with environment in unique, scale-dependent ways. While these halo properties are not directly observable, galaxies…
Astronomical objects in our universe that are too faint to be directly detectable exist and are important - an obvious example being dark matter. The same can also apply to very faint baryonic objects, such as low luminosity dwarf galaxies…