Gradient pattern analysis applied to galaxy morphology

Astronomy and Computing

Rosa

et al. 2020

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.ETGs have T-Type ≤ 0 and LTGs have T-Type > 0 (de Vaucouleurs, 1963). T-Type considers ellipticity and spiral arms strength but does not reflect the presence or absence of the bar feature in spirals.Morphology reveals structural, intrinsic and environmental properties of galaxies. In the local universe, ETGs are mostly situated in the center of galaxy clusters, have a larger mass, less gas, higher velocity dispersion, and older stellar populations than LTGs, which are rich star-forming systems (Roberts and Haynes, 1994;Blanton and Moustakas, 2009;Pozzetti et al., 2010). By mapping where the ETGs are, it is possible to map the large-scale structure of the universe. Therefore, galaxy morphology is of paramount importance for extragalactic research as it relates to stellar properties and key aspects of the evolution and structure of the universe.Astronomy has become an extremely data-rich field of knowledge with the advance of new technologies in recent decades. Nowadays it is impossible to rely on human classification given the huge flow of data attained by current research

Section: Advances In Non-parametric Galaxy Morphology -Cy-morphmentioning

confidence: 99%

Section: Advances In Non-parametric Galaxy Morphology -Cy-morphmentioning

confidence: 99%

Section: Geometric Histogram Separation (δ Ghs )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine and Deep Learning applied to galaxy morphology - A comparative study

Barchi

Astronomy and Computing

Rosa

et al. 2020

“…The well known T-Type and eclass 2 , plus G 2 . The parameter G 2 is the second gradient moment within the GPA (Gradient Pattern Analysis) formalism (Rosa et al 1999;Andrade et al 2006;Rosa et al 2018;Barchi et al 2020). After a comparative analysis considering other morphometric parameters, Rosa et al (2018) find that G 2 is the one with best performance (90% success) to separate elliptical and spiral galaxies, motivating our choice to use it.…”

Section: A Physically Intermediate Population?mentioning

confidence: 99%

Classification and evolution of galaxies according to the dynamical state of host clusters and galaxy luminosities

Morell

Ribeiro

Monthly Notices of the Royal Astronomical Society

et al. 2020

Self Cite

We analyze the dependence of galaxy evolution on cluster dynamical state and galaxy luminosities for a sample of 146 galaxy clusters from the Yang SDSS catalog. Clusters were split according to their velocity distribution in Gaussians (G) and Non-Gaussians (NG), and further divided by luminosity regime. We performed a classification in the Age-SSFR plane providing three classes: star-forming (SF), passive (PAS), and intermediate (GV -green valley). We show that galaxies evolve in the same way in G and NG systems, but also suggest that their formation histories leads to different mixtures of galactic types and infall patterns. Separating the GV into star-forming and passive components, we find more bright galaxies in the passive mode of NG than in G systems. We also find more intermediate faint galaxies in the star-forming component of NG than in G systems. Our results suggest the GV as the stage where the transition from types Sab and Scd to S0 must be taking place, but the conversion between morphological types is independent of the dynamical stage of the clusters. Analyzing the velocity dispersion profiles, we find that objects recently infalling in clusters have a different composition between G and NG systems. While all galaxy types infall onto G systems, Sab and Scd dominate the infall onto NG systems. Finally, we find that faint Scd in the outskirts of NG systems present higher asymmetries relative to the mean asymmetry of field galaxies, suggesting environmental effects acting on these objects.

Quenching, bursting, and galaxy shapes: colour transformation as a function of morphology

Sá-Freitas

Gonçalves

Monthly Notices of the Royal Astronomical Society

et al. 2021

Self Cite

Different mechanisms for quenching star formation in galaxies are commonly invoked in the literature, but the relative impact of each one at different cosmic epochs is still unknown. In particular, the relation between these processes and morphological transformation remains poorly understood. In this work, we measure the effectiveness of changes in star formation rates by analysing a new parameter, the Star Formation Acceleration (SFA), as a function of galaxy morphology. This methodology is capable of identifying both bursting and quenching episodes that occurred in the preceding 300 Myrs. We use morphological classification catalogs based on Deep learning techniques. Our final sample has ∼14,200 spirals and ∼2,500 ellipticals. We find that elliptical galaxies in the transition region have median shorter quenching timescales (τ < 1 Gyr) than spirals (τ ≥ 1 Gyr). This result conforms to the scenario in which major mergers and other violent processes play a fundamental role in galaxy evolution for most ellipticals, not only quenching star formation more rapidly but also playing a role in morphological transformation. We also find that ∼two thirds of galaxies bursting in the green valley in our sample are massive spirals (M⋆ ≥ 1011.0 M⊙) with signs of disturbance. This is in accordance with the scenario where low mass galaxies are losing their gas in a interaction with a massive galaxy: while the former is quenching, the last is being refueled and going through a burst, showing signs of recent interaction.