arXiv subjects

R. R. Rosa

Publications and source records attributed to R. R. Rosa.

8 recordsLinked to original sources

arXiv2024

Unveiling Galaxy Morphology through an Unsupervised-Supervised Hybrid Approach

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 the vast numbers of galaxies that will be observed. In this work, we introduce a novel classification technique blending unsupervised clustering based on morphological metrics with the scalability of supervised Convolutional Neural Networks. We delve into a comparative analysis between the well-known CAS (Concentration, Asymmetry, and Smoothness) metrics and our newly proposed EGG (Entropy, Gini, and Gradient Pattern Analysis). Our choice of the EGG system stems from its separation-oriented metrics, maximizing morphological class contrast. We leverage relationships between metrics and morphological classes, leading to an internal agreement between unsupervised clustering and supervised classification. Applying our methodology to the Sloan Digital Sky Survey data, we obtain 95% of Overall Accuracy of purely unsupervised classification and when we replicate T-Type and visually classified galaxy catalogs with accuracy of 88% and 89% respectively, illustrating the method's practicality. Furthermore, the application to Hubble Space Telescope data heralds the potential for unsupervised exploration of a higher redshift range. A notable achievement is our 95% accuracy in unsupervised classification, a result that rivals when juxtaposed with Traditional Machine Learning and closely trails when compared to Deep Learning benchmarks.

astro-ph.IM↗

arXiv2019

Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study

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.

astro-ph.IM↗

arXiv2017

Investigating the Relation between Galaxy Properties and the Gaussianity of the Velocity Distribution of Groups and Clusters

We investigate the dependence of stellar population properties of galaxies on group dynamical stage for a subsample of Yang catalog. We classify groups according to their galaxy velocity distribution into Gaussian (G) and Non-Gaussian (NG). Using two totally independent approaches we have shown that our measurement of Gaussianity is robust and reliable. Our sample covers Yang's groups in the redshift range 0.03 $\leq$ z $\leq$ 0.1 having mass $\geq$ 10$^{14} \rm M_{\odot}$. The new method, Hellinger Distance (HD), to determine whether a group has a velocity distribution Gaussian or Non-Gaussian is very effective in distinguishing between the two families. NG groups present halo masses higher than the G ones, confirming previous findings. Examining the Skewness and Kurtosis of the velocity distribution of G and NG groups, we find that faint galaxies in NG groups are mainly infalling for the first time into the groups. We show that considering only faint galaxies in the outskirts, those in NG groups are older and more metal rich than the ones in G groups. Also, examining the Projected Phase Space of cluster galaxies we see that bright and faint galactic systems in G groups are in dynamical equilibrium which does not seem to be the case in NG groups. These findings suggest that NG systems have a higher infall rate, assembling more galaxies which experienced preprocessing before entering the group.

astro-ph.GA↗

arXiv2017

Improving galaxy morphology with machine learning

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA). Except concentration, all parameters performed a image segmentation pre-processing. For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo. With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects balanced dataset, we applied K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.

astro-ph.GA↗

arXiv2004

Structural Flyby Characterization of Nanoporosity

Recently, Ferreira da Silva et al. [3] have performed a gradient pattern analysis of a canonical sample set (CSS) of scanning force microscopy (SFM) images of p-Si. They applied the so-called Gradient Pattern Analysis to images of three typical p-Si samples distinguished by different absorption energy levels and aspect ratios. Taking into account the measures of spatial asymmetric fluctuations they interpreted the global porosity not only in terms of the amount of roughness, but rather in terms of the structural complexity (e.g., walls and fine structures as slots). This analysis has been adapted in order to operate in a OpenGL flyby environment (the StrFB code), whose application give the numerical characterization of the structure during the flyby real time. Using this analysis we compare the levels of asymmetric fragmentation of active porosity related to different materials as p-Si and "porous diamond-like" carbon. In summary we have shown that the gradient pattern analysis technique in a flyby environment is a reliable sensitive method to investigate, qualitatively and quantitatively, the complex morphology of active nanostructures.

physics.comp-ph↗

arXiv2002

Multiscaling and Nonextensivity of Large-scale Structures in the Universe

There has been a trend in the past decade to describe the large-scale structures in the Universe as a (multi)fractal set. However, one of the main objections raised by the opponents of this approach deals with the transition to homogeneity. Moreover, they claim there is not enough sampling space to determine a scaling index which characterizes a (multi)fractal set. In this work we propose an alternative solution to this problem, using the generalized thermostatistics formalism. We show that applying the idea of nonextensivity, intrinsic to this approach, it is possible to derive an expression for the correlation function, describing the scaling properties of large-scale structures in the Universe and the transition to homogeneity, which is in good agreement with observational data.

astro-ph↗

arXiv2000

Generalized Thermostatistical Description of Intermittency and Non-extensivity in Turbulence and Financial Markets

We describe a simple and accurate framework for modeling the statistical behavior of both fully developed turbulence and short-term dynamics of financial markets based on the formalism of Tsallis' generalized non-extensive thermostatistics. Within this framework, we show that intermittency and non-extensivity are naturally linked by the entropic parameter q. Our results, concerning both probability density functions and structure functions exponents are in very good agreement with experimental data.

cond-mat↗

arXiv1999

Intermittency and Nonextensivity in Turbulence and Financial Markets

We present a new framework for modeling the statistical behavior of both fully developed turbulence and short-term dynamics of financial markets based on the nonextensive thermostatistics proposed by Tsallis. We also show that intermittency -- strong bursts in the energy dissipation or clusters of high price volatility -- and nonextensivity -- anomalous scaling of usually extensive properties like entropy -- are naturally linked by a single parameter q, from the nonextensive thermostatistics.

cond-mat↗