Related papers: Capturing the physics of MaNGA galaxies with self-…
We study the link between the kinematic-morphology of galaxies, as inferred from integral-field stellar kinematics, and their relation between mass and star formation rate (SFR). Our sample consists of $\sim 3200$ galaxies with…
Star-forming galaxies on the main sequence (MS) are often regarded as a uniform population characterized by similar global star formation properties. However, there exists a diversity in galaxy morphologies at fixed stellar mass and SFR. In…
We study the spatially resolved star formation of 1494 galaxies in the SDSSIV-MaNGA Survey. SFRs are calculated using a two-step process, using $H_\alpha$ in star forming regions and $D_n4000$ in regions identified as AGN/LI(N)ER or…
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves.…
We investigate the interplay between morphology, specific star formation rate (sSFR), and local environment using a sample of 7,408 galaxies from the SDSS-IV MaNGA survey. Our analysis spans stellar masses from dwarf to massive galaxies,…
We study the properties of 66 galaxies with kinematically misaligned gas and stars from MaNGA survey. The fraction of kinematically misaligned galaxies varies with galaxy physical parameters, i.e. M*, SFR and sSFR. According to their sSFR,…
Precise alignment of multi-modal images with inherent feature discrepancies poses a pivotal challenge in deformable image registration. Traditional learning-based approaches often consider registration networks as black boxes without…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
We elaborate on a general method that we recently introduced for characterizing the "natural" structures in complex physical systems via a multiscale network based approach for the data mining of such structures. The approach is based on…
A machine-learning-based method is developed to identify objects with unusual stellar spectra. The method employs an autoencoder, a neural network trained to compress spectral data into a low-dimensional representation and subsequently…
Post-starburst (PSB) galaxies, having recently experienced a starburst followed by rapid quenching, are excellent laboratories to probe physical mechanisms that drive starbursts and shutting down of star formation. Integral-field…
Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to…
Since the turn of the century, astronomers have been exploiting the rich information afforded by combining stellar kinematic maps and imaging in an attempt to recover the intrinsic, three-dimensional (3D) shape of a galaxy. A common…
In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and…
Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a…
Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the UniverseMachine (UM) galaxy-halo connection framework and the Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place…
We investigate the efficient learning of magnetic phases using artificial neural networks trained on synthetic data, combining computational simplicity with physics-informed strategies. Focusing on the diluted Ising model, which lacks an…
Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix…
We perform full spectrum fitting stellar population analysis and Jeans Anisotropic modelling (JAM) of the stellar kinematics for about 2000 early-type galaxies (ETGs) and spiral galaxies from the MaNGA DR14 sample. Galaxies with different…
In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a…