相关论文: Exploring the Dependence of Gas Cooling and Heatin…
Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a…
Cooling and heating functions of cosmic gas are a crucial ingredient for any study of gas dynamics and thermodynamics in the interstellar and intergalactic medium. As such, they have been studied extensively in the past under the assumption…
Gas cooling and heating rates are vital components of hydrodynamic simulations. However, they are computationally expensive to evaluate exactly with chemical networks or photoionization codes. We compare two different approximation schemes…
Accurate models of radiative cooling are a fundamental ingredient of modern cosmological simulations. Without cooling, accreted baryons will not efficiently dissipate their energy and collapse to the centres of haloes to form stars. It is…
Radiative cooling is central to a wide range of astrophysical problems. Despite its importance, cooling rates are generally computed using very restrictive assumptions, such as collisional ionization equilibrium and solar relative…
Cooling and heating functions describe how radiative processes impact the thermal state of a gas as a function of its temperature and other physical properties. In a most general case the functions depend on the detailed distributions of…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
Models of galaxy formation should be able to predict the properties of clusters of galaxies, in particular their gas fractions, metallicities, X-ray luminosity-temperature relation, temperature function and mass-deposition-rate function.…
The non-spherical shapes of dark matter and gas distributions introduce systematic uncertainties that affect observable-mass relations and selection functions of galaxy groups and clusters. However, the triaxial gas distributions depend on…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to…
Novel machine learning methods for tabular data generation are often developed on small datasets which do not match the scale required for scientific applications. We investigate a recent proposal to use XGBoost as the function approximator…
In petroleum engineering, it is essential to determine the ultimate recovery factor, RF, particularly before exploitation and exploration. However, accurately estimating requires data that is not necessarily available or measured at early…
The discrepancy between expected and observed cooling rates of X-ray emitting gas has led to the {\it cooling flow problem} at the cores of clusters of galaxies. A variety of models have been proposed to model the observed X-ray spectra and…
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizing machine learning techniques to perform clustering and regression tasks. We examine five…
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are…
Quantitative understanding of rare earth element (REE) mineralization mechanisms, crucial for improving industrial separation, remains limited. This study leverages 1239 hydrothermal synthesis datapoints from material science as a surrogate…
To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and halos accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the…
Intrinsic colors (ICs) of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the ICs with the atmospheric parameters $T_{\rm eff}$, ${\rm log}\,g$, and $\rm…
The addition of metals to any gas can significantly alter its evolution by increasing the rate of radiative cooling. In star-forming environments, enhanced cooling can potentially lead to fragmentation and the formation of low-mass stars,…