Related papers: Capturing the physics of MaNGA galaxies with self-…
The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of…
Galaxy morphology is a fundamental quantity, that is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology. While a rich literature exists on…
We present the measurement of total and stellar/dark matter decomposed mass density profile around a sample of galaxy groups and clusters with dynamical masses derived from integral-field stellar kinematics from the MaNGA survey in Paper I…
The large Integral Field Spectroscopy (IFS) surveys have allowed the classification of ionizing sources of emission lines on sub-kpc scales. In this work, we define two non-parametric parameters, quiescence (f$_{q}$) and its concentration…
MaNGA (Mapping Nearby Galaxies at APO) is a galaxy integral-field spectroscopic survey within the fourth generation Sloan Digital Sky Survey (SDSS-IV). It will be mapping the composition and kinematics of gas and stars in 10,000 nearby…
By applying spectroscopic decomposition methods to a sample of MaNGA early-type galaxies, we separate out spatially and kinematically distinct stellar populations, allowing us to explore the similarities and differences between galaxy…
Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a…
Internal kinematics of galaxies, traced through the stellar rotation curve or two dimensional velocity map, carry important information on galactic structure and dark matter. With upcoming surveys, the velocity map may play a key role in…
Galaxy formation and evolution simulations are essential tools to probe poorly known astrophysics processes, but particular care is needed to compare simulations with galaxy observations, as observed data need to be modelled as well. We…
The spatial extent of the environment's impact on galaxies marks a transitional region between cluster and field galaxies. We present a data-driven method to identify this region in galaxy clusters with masses $M_{200\rm ,mean}>10^{13}…
We investigate the 3D spin alignment of galaxies with respect to the large-scale filaments using the MaNGA survey. The cosmic web is reconstructed from the Sloan Digital Sky Survey using Disperse and the 3D spins of MaNGA galaxies are…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is an integral-field spectroscopic survey of 10,000 nearby galaxies that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). MaNGA's 17…
Spectra of galaxies contain a wealth of information about the stellar populations from which they are made. With integral-field unit (IFU) surveys, such data can be used to map out stellar population properties across the face of a galaxy,…
In this work we carry out an analysis of star-formation and nuclear activity in the different stages during a galaxy merger identified in isolated systems (isolated galaxies, isolated pairs, and isolated triplets) using integral field…
We present an extensive catalog of non-parametric structural properties derived from optical and mid-infrared imaging for 4585 galaxies from the MaNGA survey. DESI and WISE imaging are used to extract surface brightness profiles in the g,…
We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample based on deep optical DESI photometry processed with…
Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the…
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…