Related papers: Populating Galaxies Into Halos Via Machine Learnin…
We compare the galaxy population of an SPH simulation to those predicted by the GalICS semi-analytic model and a stripped down version without supernova and AGN feedback. The SPH simulation and the no-feedback GalICS model make similar…
We present a set-based machine learning framework that infers posterior distributions of galaxy cluster masses from projected galaxy dynamics. Our model combines Deep Sets and conditional normalizing flows to incorporate both positional and…
Intrinsic galaxy alignments constitute the major astrophysical systematic of forthcoming weak gravitational lensing surveys but also yield unique insights into galaxy formation and evolution. We build analytic models for the distribution of…
Understanding the physical connection between cluster galaxies and massive haloes is key to mitigating systematic uncertainties in next-generation cluster cosmology. We develop a novel method to infer the level of conformity between the…
We present a new algorithm (PINOCCHIO, PINpointing Orbit-Crossing Collapsed HIerarchical objects) to predict accurately the formation and evolution of individual dark matter haloes in a given realization of an initial linear density field.…
Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation process. Aims. We explore whether supervised machine learning…
We present a deep machine learning (ML)-based technique for accurately determining $\sigma_8$ and $\Omega_m$ from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of $N$-body simulations, which comprises 40…
Recent observations of UV-/optically selected, massive star forming galaxies at z~2 indicate that the baryonic mass assembly and star formation history is dominated by continuous rapid accretion of gas and internal secular evolution, rather…
Stellar mass is a fundamental quantity that determines the properties and evolution of stars. However, estimating stellar masses in star-forming regions is challenging because young stars are obscured by dense gas and the regions are highly…
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves.…
We present Classification of Cluster GAlaxy MEmbers (C$^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using…
The halo model, combined with halo occupation distribution (HOD) prescriptions, is widely used to interpret cosmic infrared background (CIB) anisotropies and connect star-forming galaxies to large-scale structure. Recent implementations…
The formation of Hoag-type galaxies with central spheroidal galaxies and outer stellar rings has yet to be understood in astronomy. We consider that these unique objects were formed from the past interaction between elliptical galaxies and…
The metallicity structure of stellar halos encodes the fossil record of galaxy assembly, tracing the chemical evolution and dynamical imprint of past mergers. Using five Milky Way-mass halos from the Aquarius simulations, we introduce an…
Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been…
We use a series of high-resolution N-body simulations of a `Milky-Way' halo, coupled to semi-analytic techniques, to study the formation of our own Galaxy and of its stellar halo. Our model Milky Way galaxy is a relatively young system…
The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of the standard cosmological model. First discovered in 2005 from the Millennium Run…
We present a comparison of galaxy atomic and molecular gas properties in three recent cosmological hydrodynamic simulations, Simba, EAGLE, and Illustris-TNG, versus observations from $z\sim 0-2$. These simulations all rely on similar…
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the…
Aims. We aim to investigate if the environment (characterised by the host dark matter halo mass) plays any role in shaping the galaxy star formation main sequence (MS). Methods. The Galaxy and Mass Assembly project (GAMA) combines a…