Related papers: Machine-guided Exploration and Calibration of Astr…
To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables.…
We present results from seventy-one zoom simulations of a Milky Way-sized (MW) halo, exploring the parameter space for a widely-used star formation and feedback model in the {\tt Enzo} simulation code. We propose a novel way to match…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
We present our recently developed {\em galcon} approach to hydrodynamical cosmological simulations of galaxy clusters - a subgrid model added to the {\em Enzo} adaptive mesh refinement code - which is capable of tracking galaxies within the…
This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical…
Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very…
We implement a sample-efficient method for rapid and accurate emulation of semi-analytical galaxy formation models over a wide range of model outputs. We use ensembled deep learning algorithms to produce a fast emulator of an updated…
Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
We use the Millennium Simulation, a 10 billion particle simulation of the growth of cosmic structure, to construct a new model of galaxy clustering. We adopt a methodology that falls midway between the traditional semi-analytic approach and…
We present a suite of high-resolution cosmological zoom-in simulations to $z=4$ of a $10^{12}\,{\rm M}_{\odot}$ halo at $z=0$, obtained using seven contemporary astrophysical simulation codes widely used in the numerical galaxy formation…
Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-driven parameterizations lack interpretability, physical…
Understanding the non-linear dynamics of satellite halos (a.k.a. "sub-halos") is important for predicting the abundance and distribution of dark matter substructures and satellite galaxies, and for distinguishing among microphysical dark…
The interplay of star formation and supernova (SN) feedback in galaxy formation is a key element for understanding galaxy evolution. Since these processes occur at small scales, it is necessary to have sub-grid models that recover their…
The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume…
We describe a subgrid model for including galaxies into hydrodynamical cosmological simulations of galaxy cluster evolution. Each galaxy construct- or galcon- is modeled as a physically extended object within which star formation, galactic…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses…