Related papers: Introducing the Descriptive Parametric Model: Gase…
We use a suite of cosmological simulations to study the mass-concentration-redshift relation, $c({\rm M},z)$, of dark matter halos. Our simulations include standard $\Lambda$-cold dark matter (CDM) models, and additional runs with truncated…
In the Halo21 absorption modeling challenge we generated synthetic absorption spectra of the circumgalactic medium (CGM), and attempted to estimate the metallicity, temperature, and density (Z, T, and nH) of the underlying gas using…
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on…
The fuzzy dark matter (FDM) scenario has received increased attention in recent years due to the small-scale challenges of the vanilla Lambda cold dark matter ($\Lambda$CDM) cosmological model and the lack of any experimental evidence for…
We introduce a new technique for following the formation and evolution of galaxies in cosmological N-body simulations. Dissipationless simulations are used to track the formation and merging of dark matter halos as a function of redshift.…
Upcoming Stage-IV surveys will deliver measurements of distribution of matter with unprecedented precision, demanding highly accurate theoretical models for cosmological parameter inference. A major source of modeling uncertainty lies in…
The massive dark matter halos that host groups and clusters of galaxies have observable properties that appear to be log-normally distributed about power-law mean scaling relations in halo mass. Coupling this assumption with either…
The Local Universe (D < 120 Mpc) has been intensely studied for decades, with highly complete galaxy redshift surveys now publicly available. These data have driven density reconstructions of the underlying matter density field, as well as…
We introduce an analytic surface density profile for dark matter halos that accurately reproduces the structure of simulated halos of mass $M_{\rm vir} = 10^{7-11}\ M_\odot$, making it useful for modeling line-of-sight perturbers in strong…
This work explores the ability of computer vision algorithms to characterise dark matter haloes formed in different models of structure formation. We produce surface mass density maps of the most massive haloes in a suite of eight numerical…
Deep learning models in the Earth Observation domain heavily rely on the availability of large-scale accurately labeled satellite imagery. However, obtaining and labeling satellite imagery is a resource-intensive endeavor. While generative…
In the standard Cold Dark Matter (CDM) scenario, the density profiles of dark matter haloes are well described by analytical models linking their concentration to halo mass. Alternative scenarios, such as warm dark matter (WDM) and…
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a…
Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with accurate score estimates, SGMs can efficiently generate samples…
We study dark-matter halo density profiles in a high-resolution N-body simulation of an LCDM cosmology. Our statistical sample contains ~5000 haloes in the range 10^{11}-10^{14} M_sun. The profiles are parameterized by an NFW form with two…
Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used for image, audio, and video generation as well as many more…
Diffusion-based generative models represent the current state-of-the-art for image generation. However, standard diffusion models are based on Euclidean geometry and do not translate directly to manifold-valued data. In this work, we…
We propose the Binary Diffusion Probabilistic Model (BDPM), a generative framework specifically designed for data representations in binary form. Conventional denoising diffusion probabilistic models (DDPMs) assume continuous inputs, use…
Popular models for describing the luminosity-density profiles of dynamically hot stellar systems (e.g., Jaffe, Hernquist, Dehnen) were constructed to match the deprojected form of de Vaucouleurs' $R^{1/4}$ light-profile. However, we now…
We discuss various aspects of the inner structure formation in virialized dark matter (DM) halos that form as primordial density inhomogeneities evolve in the cosmological standard model. The main focus is on the study of central…