Related papers: Morphology-assisted galaxy mass-to-light predictio…

Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze…

We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data…

We present a simple technique to estimate mass-to-light (M/L) ratios of stellar populations based on two broadband photometry measurements, i.e. a color-M/L relation. We apply the color-M/L relation to galaxy rotation curves, using a large…

We describe the application of the `shapelet' linear decomposition of galaxy images to multi-wavelength morphological classification using the $u,g,r,i,$ and $z$-band images of 1519 galaxies from the Sloan Digital Sky Survey. We utilize…

We investigate the dependence of galaxy structure on a variety of galactic and environmental parameters for ~500,000 galaxies at z<0.2, taken from the Sloan Digital Sky Survey data release 7 (SDSS-DR7). We utilise bulge-to-total stellar…

Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification…

Bars are important drivers of galaxy evolution, influencing many physical processes and properties. Characterising bars is a difficult task, especially in large-scale surveys. In this work, we propose a novel morphological segmentation…

We present a deep learning model to predict the r-band bulge-to-total light ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample…

We introduce an empirical methodology to study how the spectral energy distribution (SED) and galaxy morphology constrain each other and implement this on 8000 galaxies from the HST CANDELS survey in the GOODS-South field. We show that the…

We study the relationships between galaxy total luminosity (M_g), morphology, color and environment as a function of redshift. We use a magnitude-limited sample of 65,624 galaxies in the redshift range 0<z<1.3 taken from one of the…

The morphological properties of galaxies between $21 {\rm~mag} < I < 25 {\rm~mag}$ in the {\em Hubble Deep Field} are investigated using a quantitative classification system based on measurements of the central concentration and asymmetry…

Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…

We study how optical galaxy morphology depends on mass and star formation rate (SFR) in the Illustris Simulation. To do so, we measure automated galaxy structures in 10808 simulated galaxies at z=0 with stellar masses 10^9.7 < M_*/M_sun <…

We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…

The correlation between a galaxy's morphology and its observed optical spectrum is investigated. As an example, 4000 galaxies from the 2dF Galaxy Redshift Survey, which possess both good quality spectra and have visually determined…

The optical morphology of galaxies is strongly related to galactic environment, with the fraction of early-type galaxies increasing with local galaxy density. In this work we present the first analysis of the galaxy morphology-density…

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…

We provide fits to the distribution of galaxy luminosity, size, velocity dispersion and stellar mass as a function of concentration index C_r and morphological type in the SDSS. We also quantify how estimates of the fraction of `early' or…

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present…

In this paper we present a new statistic for quantifying galaxy morphology based on measurements of the Gini coefficient of galaxy light distributions. This statistic is easy to measure and is commonly used in econometrics to measure how…