相关论文: Painting galaxies into dark matter halos using mac…

The mass assembly history (MAH) of dark matter halos plays a crucial role in shaping the formation and evolution of galaxies. MAHs are used extensively in semi-analytic and empirical models of galaxy formation, yet current analytic methods…

We explore the interrelationships between the galaxy group halo mass and various observable group properties. We propose a simple scenario that describes the evolution of the central galaxies and their host dark matter halos. Star formation…

Cosmological $N$-body simulations predict dark matter (DM) haloes with steep central cusps (e.g. NFW, Navarro et al. 1996). This contradicts observations of gas kinematics in low-mass galaxies that imply the existence of shallow DM cores.…

(Abridged) By means of high-resolution cosmological simulations in the context of the LCDM scenario, the specific star formation rate (SSFR=SFR/Ms, Ms is the stellar mass)--Ms and stellar mass fraction (Fs=Ms/Mh, Mh is the halo mass)--Ms…

Using tens of thousands of halos realized in the BAHAMAS and MACSIS simulations produced with a consistent astrophysics treatment that includes AGN feedback, we validate a multi-property statistical model for the stellar and hot gas mass…

[Abridged] We analyse the dark matter (DM) halo properties of 127 0.3<z<1.5 star-forming galaxies (SFGs) down to low stellar masses (8<log(Mstar/Msun)<11), using data from the MUSE Hubble Ultra Deep Field Survey and photometry from HST and…

We present an empirical method to measure the halo mass function (HMF) of galaxies. We determine the relation between the \hi\ line-width from single-dish observations and the dark matter halo mass ($M_{200}$) inferred from rotation curve…

Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…

We present the MUFASA suite of cosmological hydrodynamic simulations, which employs the GIZMO meshless finite mass (MFM) code including H2-based star formation, nine-element chemical evolution, two-phase kinetic outflows following scalings…

We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a…

We develop a model to establish the interconnection between galaxies and their dark matter halos. We use Principal Component Analysis (PCA) to reduce the dimensionality of both the mass assembly histories of halos/subhalos and the star…

We set constraints on the dark matter halo mass and concentration of ~22,000 individual galaxies visible both in HI (from the ALFALFA survey) and optical light (from the SDSS). This is achieved by combining two Bayesian models, one for the…

The detection of the 21-cm signal at $z\gtrsim6$ will reveal insights into the properties of the first galaxies responsible for driving reionisation. To extract this information, we perform parameter inference which requires embedding 3D…

We present an artificial neural network design in which past and present-day properties of dark matter halos and their local environment are used to predict time-resolved star formation histories and stellar metallicity histories of central…

Understanding the physical mechanisms that drive star formation is crucial for advancing our knowledge of galaxy evolution. We explore the interrelationships between key galaxy properties associated with star formation, with a particular…

Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…

According to the now strongly supported concordance $\Lambda$CDM model, galaxies may be grossly described as a luminous component embedded in a dark matter halo. The density profile of these mass dominating haloes may be determined by N -…

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce…

Shape estimates that quantify the halo anisotropic mass distribution are valuable parameters that provide information on their assembly process and evolution. Measurements of the mean shapes for a sample of cluster-sized halos can be used…

Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are…