Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

Huertas-Company, M.; Primack, Joel R.; Dekel, Avishai; Koo, David C.; Lapiner, Sharon; Ceverino, Daniel; Simons, Raymond C.; Snyder, Gregory F.; Bernardi, Mariangela; Chen, Z.; Sánchez, H. Domínguez; Lee, C. T.; Margalef-Bentabol, Berta; Tuccillo, D.

doi:10.3847/1538-4357/aabfed

Cited by 103 publications

(83 citation statements)

References 61 publications

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“…Our results are also consistent with the predictions from the VELA simulations (Ceverino et al 2015;Tomassetti et al 2016) that the transition of shape occurs in a characteristic mass range, where galaxies tend to undergo a process of wet compaction to a BN, and make a transition from being dark matter dominated to baryon dominated. See also Huertas-Company et al (2018) and Dekel et al (in preparation).…”

Section: O N C L U S I O Nmentioning

confidence: 94%

The evolution of galaxy shapes in CANDELS: from prolate to discy

Zhang

Primack

Faber

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We model the projected b/a-log a distributions of CANDELS star-forming main-sequence galaxies, where a (b) is the half-light semimajor (semiminor) axis of the galaxy images measured by GALFIT. We find that smaller a galaxies are rounder at all stellar masses M * and redshifts, so we include a when analysing b/a distributions. Approximating intrinsic shapes of the galaxies as triaxial ellipsoids and assuming a multivariate normal distribution of galaxy size and two shape parameters, we construct their intrinsic shape and size distributions to obtain the fractions of elongated (prolate), discy (oblate), and spheroidal galaxies in each redshift and mass bin. We find that galaxies tend to be prolate at low M * and high redshifts, and discy at high M * and low redshifts, qualitatively consistent with van der Wel et al., implying that galaxies tend to evolve from prolate to discy. These results are consistent with the predictions from simulations that the transition from prolate to oblate is caused by a compaction event at a characteristic mass range, making the galaxy centre baryon dominated. We give probabilities of a galaxy's being elongated, discy, or spheroidal as a function of its M * , redshift, and projected b/a and a, which can facilitate target selections of galaxies with specific shapes at high redshifts.

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Section: O N C L U S I O Nmentioning

confidence: 94%

The evolution of galaxy shapes in CANDELS: from prolate to discy

Zhang

Primack

Faber

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…The huge amount of photometric astrophysical data available and the highly increasing advancements on hardware and methods to perform automatic classifications has been leveraging related publications (Law et al, 2007;Freeman et al, 2013;Khalifa et al, 2017;Huertas-Company et al, 2018;Barchi et al, 2016;Dieleman et al, 2015;Khan et al, 2018;Huertas-Company et al, 2015;Domínguez Sánchez et al, 2018). Highlight to Domínguez Sánchez et al (2018) who use questions and answers from Galaxy Zoo 2 for replicating the answers from the users, and provide morphology classification by T-Type in their final catalog.…”

Section: Introductionmentioning

confidence: 99%

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

Barchi

Carvalho

Rosa

et al. 2020

Astronomy and Computing

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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

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“…Other applications included predicting the HI content of galaxies based on optical observations (Rafieferantsoa, Andrianomena, & Davé, ), determining physical properties of galaxies from their emission‐line spectra (Ucci, Ferrara, Gallerani, & Pallottini, ), point source detection from radio interferometry surveys (Vafaei Sadr et al, ), and cross‐identification of sources from the Radio Galaxy Zoo (Alger et al, ). Training a CNN on mock images of rare “blue nugget” galaxies from cosmological simulations, such objects were successfully found in an observational sample from the CANDELS survey (Huertas‐Company et al, ). Distance measures . Estimates of the distances to galaxies, quasars and other remote celestial objects has benefited greatly from the adoption of ML.…”

Section: Assessing the Maturity Of Adoptionmentioning

confidence: 99%

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Fluke

Jacobs

2019

WIREs Data Min & Knowl

113

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Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Machine Learning

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Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

Cited by 103 publications

References 61 publications

The evolution of galaxy shapes in CANDELS: from prolate to discy

The evolution of galaxy shapes in CANDELS: from prolate to discy

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

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

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