Related papers: Characterizing the Conditional Galaxy Property Dis…
Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the universe, $\Omega_{\rm m}$, from the features of a single…
The standard picture of galaxy formation motivates the decomposition of the Milky Way into 3--4 stellar populations with distinct kinematic and elemental abundance distributions: the thin disk, thick disk, bulge, and stellar halo. To test…
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in large-scale surveys…
We develop here a semiparametric Gaussian mixture model (SGMM) for unsupervised learning with valuable spatial information taken into consideration. Specifically, we assume for each instance a random location. Then, conditional on this…
Line intensity mapping (LIM) is a promising approach to study star formation and the interstellar medium (ISM) in galaxies by measuring the aggregate line emission from the entire galaxy population. In this work, we develop a simple yet…
To understand physical properties of the interstellar medium (ISM) on various scales, we investigate it at parsec resolution on the kiloparsec scale. Here, we report on the sub-kpc scale Gas Density Histogram (GDH) of the Milky Way. The GDH…
Estimating physical properties of galaxies from wide-field surveys remains a central challenge in astrophysics. While spectroscopy provides precise measurements, it is observationally expensive, and photometry discards morphological…
We consider the consequences of applying general relativity to the description of the dynamics of a galaxy, given the observed flattened rotation curves. The galaxy is modeled as a stationary axially symmetric pressure-free fluid. In spite…
One way of recovering information about the initial conditions of the Universe is by measuring features of the cosmological density field which are preserved during gravitational evolution and galaxy formation. In this paper we study the…
Line-intensity mapping (LIM) is quickly attracting attention as an alternative technique to probe large-scale structure and galaxy formation and evolution at high redshift. LIM one-point statistics are motivated because they provide access…
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an…
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text…
We have analyzed the distributions in the color-magnitude diagram (CMD) of a large sample of face-on galaxies to minimize the effect of dust extinctions on galaxy color. About 300 thousand galaxies with $log(a/b) < $ 0.2 and redshift $z <…
We explore the potential of the Gaussian Mixture Model (GMM), an unsupervised machine learning method, to identify coherent physical structures in the ISM. The implementation we present can be used on any kind of spatially and spectrally…
I present a new approach to recover the primordial density fluctuations and the cosmic web structure underlying a galaxy distribution. The method is based on sampling Gaussian fields which are compatible with a galaxy distribution and a…
This paper proposes a morpho-statistical characterisation of the galaxy distribution through spatial statistical modelling based on inhomogeneous Gibbs point processes. The galaxy distribution is supposed to exhibit two components. The…
Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this area remains…
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.…
We compare predictions of a number of empirical models and numerical simulations of galaxy formation to the conditional stellar mass functions (CSMF)of galaxies in groups of different masses obtained recently by Lan et al. to test how well…
Various studies firmly establish the fact that gamma-ray observations can act as a unique probe to detect the possible cosmic ray (CR) sources, study the CR density distribution and explore the average properties of interstellar medium…