Related papers: GalacticFlow: Learning a Generalized Representatio…
This paper introduces EGG, the Empirical Galaxy Generator, a tool designed within the ASTRODEEP collaboration to generate mock galaxy catalogs for deep fields with realistic fluxes and simple morphologies. The simulation procedure is based…
Theoretical and observational arguments suggest that there is a large amount of hot ($\sim 10^6$ K), diffuse gas residing in the Milky Way's halo, while its total mass and spatial distribution are still unclear. In this work, we present a…
Massive galaxies, such as nearby ellipticals, have relatively low number densities, yet they host the majority of the stellar mass in the universe. Understanding their origin is a central problem of galaxy formation. Age dating of stellar…
We present a generalization of the multiphase chemical evolution model applied to a wide set of theoretical galaxies with different masses and morphological types. This generalized set of models has been computed using the so-called…
This series of papers is intended to present astrocladistics in some detail and evaluate this methodology in reconstructing phylogenies of galaxies. Being based on the evolution of all the characters describing galaxies, it is an objective…
We describe an overall picture of galactic-scale star formation. Recent high-resolution magneto-hydrodynamical simulations of two-fluid dynamics with cooling/heating and thermal conduction have shown that the formation of molecular clouds…
Star formation, together with the associated chemical and energy feedback, is one of the most important processes in galaxy evolution. The star formation activity in galaxies defines and affects many of their fundamental properties, such as…
The physical mechanisms and timescales that determine the morphological signatures and the quenching of star formation of typical (~L*) elliptical galaxies are not well understood. To address this issue, we have simulated the formation of a…
We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i.e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre.…
Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution of interest. Recent work showed that such generative models…
Stars do not form continuously distributed over star forming galaxies. They form in star clusters of different masses. This nature of clustered star formation is taken into account in the theory of the integrated galactic stellar initial…
Galaxies can be classified in two broad sequences which are likely to reflect their formation mechanism. The `main sequence', consisting of spirals, irregulars and all dwarf galaxies, is probably produced by gas settling within dark matter…
The cosmic star formation rate (CSFR), namely the star formation rate in a unitary comoving volume of the Universe, is a fundamental clue to investigate the history of the assembling and evolution of structures in the Universe. Here we…
The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
If we are to develop a comprehensive and predictive theory of galaxy formation and evolution, it is essential that we obtain an accurate assessment of how and when galaxies assemble their stellar populations, and how this assembly varies…
Conditional Normalizing Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and large interdimensional correlations, making them appealing for structured output learning.…
We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic…
Multiple mechanisms are proposed for the formation of giant molecular clouds (GMCs), from gravitational free-fall caused by self-gravity to stellar feedback-driven gas compression. Both the galactic environment and galaxy conditions could…
The radiative cooling time of hot gas in the cool cores of many galaxy clusters and massive elliptical galaxies drops in the centre to below 100 million years. The mass cooling rates inferred from simple modelling of X-ray observations of…