Related papers: Determining Satellite Infall Times Using Machine L…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
Halo inhabitants are individual stars, stellar streams, star and globular clusters, and dwarf galaxies. Here we compare the two last categories that include objects of similar stellar mass, which are often studied as self-dynamical…
We use a numerical simulation of a loose group containing a Milky Way halo to probe that in the hierarchical universe the Magellanic Clouds and some dSphs have been accreted into the Milky Way halo from a late infalling group of dwarfs. Our…
The vast majority of dwarf satellites orbiting the Milky Way and M31 are quenched, while comparable galaxies in the field are gas-rich and star-forming. Assuming that this dichotomy is driven by environmental quenching, we use the ELVIS…
Studies have shown that the use of pulsar timing arrays (PTAs) is among the approaches with the highest potential to detect very low-frequency gravitational waves in the near future. Although the capture of gravitational waves (GWs) by PTAs…
The dwarf galaxies comparable to the LMC and SMC, with stellar masses $7.5 <{\rm log}(M_{\ast}/M_{\odot})<9.5$, are found in a diversity of environments and have long quenching timescales. We need to understand how this phenomenon is…
Leo T is a gas-rich dwarf located at 414kpc $(1.4R_{\rm vir})$ distance from the Milky Way (MW) and it is currently assumed to be on its first approach. Here, we present an analysis of orbits calculated backward in time for the dwarf with…
We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a…
Context:Halo formation time, which quantifies the mass assembly history of dark-matter halos, directly impacts galaxy properties and evolution. Although not directly observable, it can be inferred through proxies like star formation history…
We investigate the population of dwarf galaxies with stellar masses similar to the Large Magellanic Cloud (LMC) and M33 in the EAGLE galaxy formation simulation. In the field, galaxies reside in haloes with stellar-to-halo mass ratios of…
We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of…
Several occupational distributions for satellite galaxies more massive than ms~4E7 Msun around MW-sized hosts are presented and used to predict their internal dynamics. For this, a galaxy group mock catalog is constructed on the basis of…
Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved…
We compile a large sample of nearby galaxies that are satellites of hosts using a well known SDSS group catalogue. From this sample, we create an `ancient infallers' and `recent infallers' subsample, based on the mean infall time predicted…
Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as…
Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and…
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later accreted. These models, which include examples from…
Dwarf galaxies are ubiquitous throughout the universe and are extremely sensitive to various forms of internal and external feedback. Over the last two decades, the census of dwarf galaxies in the Local Group and beyond has increased…
M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several…
We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral…