Related papers: Capturing the physics of MaNGA galaxies with self-…
Stellar counterrotating (CR) galaxies are systems hosting two large-scale stellar components rotating in opposite directions -- a main, preexisting galaxy body with an older stellar population and a younger CR stellar disk likely formed…
The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is…
Searching in the MaNGA IFU survey, I identify 9 galaxies that have strong Balmer absorption lines and weak nebular emission lines measured from the spectra integrated over the entire IFUs. The spectral features measured from the bulk of the…
By means of the fossil record method implemented through Pipe3D, we reconstruct the global and radial stellar mass growth histories (MGHs) of an unprecedentedly large sample of galaxies, ranging from dwarf to giant objects, from the…
Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on visual clues. Therefore, it is helpful to leverage additional…
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique…
We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical…
The SDSS-IV/MaNGA Survey data provide an unprecedented opportunity to study the internal motions of galaxies and, in particular, represent the largest sample of barred galaxy kinematic maps obtained to date. We present results from Nirvana,…
The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar…
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed…
We present the MaNGA Visual Morphology (MVM) catalogue, featuring a visual morphological classification of 10,059 galaxies in the final MaNGA sample. By combining SDSS and DESI Legacy Survey (DLS) images, we classified galaxies into 13…
This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the…
Here I present results from individual galaxy studies and galaxy surveys in the Local Universe with particular emphasis on the spatially resolved properties of neutral hydrogen gas. The 3D nature of the data allows detailed studies of the…
We study 379 central and 159 satellite early-type galaxies with two-dimensional kinematics from the integral-field survey Mapping Nearby Galaxies at APO (MaNGA) to determine how their angular momentum content depends on stellar and halo…
Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these…
A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The…
This review summarizes popular unsupervised learning methods, and gives an overview of their past, current, and future uses in astronomy. Unsupervised learning aims to organise the information content of a dataset, in such a way that…
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these…
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However, due to a lack of interpretability and the associational…
Extragalactic globular clusters (GCs) are important tracers of galaxy formation and evolution. Obtaining GC catalogues from photometric data involves several steps which will likely become too time-consuming to perform on the large data…