Related papers: Capturing the physics of MaNGA galaxies with self-…
We examine the effectiveness of identifying distinct evolutionary histories in IllustrisTNG-100 galaxies using unsupervised machine learning with Gaussian Mixture Models. We focus on how clustering compressed metallicity histories and star…
The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and…
Here, we present a machine vision approach, combining a VAE framework with PCA, to decipher galaxy images. Using mock gri-band images from the EAGLE simulation, the VAE finds that around 35 features are needed to describe the images. Adding…
Many galaxies display clear bulges and discs, and understanding how these components form is a vital step towards understanding how the galaxy has evolved into what we see today. The BUDDI-MaNGA project aims to study galaxy evolution and…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
Gravitational lensings provide unique opportunities to study distant galaxies. Despite their usefulness, only a dozens of gravitational arcs have been reported up-to-date, hampered by the low resolution imaging from ground-based surveys. We…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
The Large-Scale Structure (LSS) of the Universe is a homogeneous network of galaxies separated in dense complexes, the superclusters of galaxies, and almost empty voids. The superclusters are young structures that did not have time to…
We develop a novel semi-analytic spectral fitting approach to quantify the star-formation histories (SFHs) and chemical enrichment histories (ChEHs) of individual galaxies. We construct simple yet general chemical evolution models that…
We examine a general framework for visualizing datasets of high (> 2) dimensionality, and demonstrate it using the morphology of galaxies at moderate redshifts. The distributions of various populations of such galaxies are examined in a…
Being able to distinguish between galaxies that have recently undergone major merger events, or are experiencing intense star formation, is crucial for making progress in our understanding of the formation and evolution of galaxies. As…
Galactic rotation curves are crucial for understanding the distribution of mass in galaxies. Despite advances in precision observations, there are discrepancies between the inferred mass from luminosity and the observed rotational…
Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods,…
We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a SDSS-IV survey that will obtain spatially resolved spectroscopy from 3600 \AA\ to 10300 \AA\ for a representative sample of over 10000 nearby galaxies. In this paper we…
The MaNGA Survey (Mapping Nearby Galaxies at Apache Point Observatory) is one of three core programs in the Sloan Digital Sky Survey IV. It is obtaining integral field spectroscopy (IFS) for 10K nearby galaxies at a spectral resolution of…
The many unique properties of galaxies are shaped by physical processes that affect different components of the galaxy - like the bulges and discs - in different ways, and leave characteristic imprints on the light and spectra of these…