Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey

Khan, Asad; Huerta, E. A.; Wang, Sibo; Gruendl, R. A.; Jennings, Elise; Zheng, Huihuo

doi:10.1016/j.physletb.2019.06.009

Cited by 52 publications

(45 citation statements)

References 33 publications

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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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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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“…We will focus in this work on a supervised learning approach, in which we train our classifiers with labelled sample data -in this case spectra simulated with and without the effects of ALPs. We note in passing that unsupervised learning, in which the classifiers are not given training data, may also have potential for physics discovery [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Accelerating the search for axion-like particles with machine learning

Day¹,

Krippendorf²

2020

J. Cosmol. Astropart. Phys.

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Machine learning (ML) techniques have been applied with tremendous success in many areas of physics. In this work, we use ML to place bounds on the coupling between photons and axion-like particles (ALPs). This coupling causes ALPs and photons to interconvert in the presence of a background magnetic field. This would lead to modulations in the spectra of point sources shining through the magnetic fields of galaxy clusters. This effect has already been used to place world-leading bounds on the ALP-photon coupling using conventional statistical methods. We train ML classification algorithms on simulated spectra from the Chandra X-ray telescope for a range of point sources and ALP-photon couplings. We then use the response of these algorithms to the real Chandra spectra to place bounds on ALP-photon interactions. We obtain bounds at a similar level to those based on other techniques, but find improvements on an individual source basis. We expect such search techniques to become increasingly important for ALP searches with future telescopes that will offer substantially higher energy resolution.

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“…The neural network models and data used to study characterize black hole mergers, and to classify galaxy images, are are readily available at the Deep Learning Hub (DLHub) [30,31] hosted by Argonne National Laboratory (ANL) [60,61].…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

et al. 2020

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Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

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Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey

Cited by 52 publications

References 33 publications

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

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

Accelerating the search for axion-like particles with machine learning

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

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