scholarly journals The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations

2020 ◽  
Vol 501 (1) ◽  
pp. 730-746
Author(s):  
Omri Ginzburg ◽  
Marc Huertas-Company ◽  
Avishai Dekel ◽  
Nir Mandelker ◽  
Gregory Snyder ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Main Sequence ◽  
High Redshift ◽  
High Mass ◽  
Galactic Centre ◽  
Preferential Formation ◽  
Maximum Resolution ◽  
Supernova Feedback ◽  
Disc Galaxies

ABSTRACT We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with ${\sim}25\ \rm {pc}$ maximum resolution, targeting main-sequence galaxies at 1 < z < 3. The clumps are classified as long-lived clumps (LLCs) or short-lived clumps (SLCs) based on their longevity in the simulations. We then train neural networks to detect and classify the simulated clumps in mock, multicolour, dusty, and noisy HST-like images. The clumps are detected using an encoder–decoder convolutional neural network (CNN), and are classified according to their longevity using a vanilla CNN. Tests using the simulations show our detector and classifier to be ${\sim}80{{\ \rm per\ cent}}$ complete and ${\sim}80{{\ \rm per\ cent}}$ pure for clumps more massive than ∼107.5 M⊙. When applied to observed galaxies in the CANDELS/GOODS S+N fields, we find both types of clumps to appear in similar abundances in the simulations and the observations. LLCs are, on average, more massive than SLCs by ∼0.5 dex, and they dominate the clump population above Mc ≳ 107.6 M⊙. LLCs tend to be found closer to the galactic centre, indicating clump migration to the centre or preferential formation at smaller radii. The LLCs are found to reside in high-mass galaxies, indicating better clump survivability under supernova feedback there, due to clumps being more massive in these galaxies. We find the clump masses and radial positions in the simulations and the observations to agree within a factor of 2.

scholarly journals The potential of using KMOS for multi-object massive star spectroscopy

2016 ◽  
Vol 12 (S329) ◽  
pp. 454-454
Author(s):  
Michael Wegner ◽  
Ralf Bender ◽  
Ray Sharples ◽  
Keyword(s):  
Galaxy Formation ◽  
Near Infrared ◽  
High Redshift ◽  
High Mass ◽  
Galactic Centre ◽  
Star Forming ◽  
Star Forming Regions ◽  
Spectral Bands ◽  
Galaxy Formation And Evolution ◽  
Potential Applications

AbstractKMOS, the “K-Band Multi-Object Spectrometer”, was built by a British-German consortium as a second generation instrument for the ESO Paranal Observatory. It is available to the user community since its successful commissioning in 2013 (Sharples et al. 2013). As a multi-object integral field spectrometer for the near infrared, KMOS offers 24 deployable IFUs of 2.8x2.8 arcsec and 14x14 spatial pixels each, which can either be placed individually within a 7.2 arcmin field of view or combined in a Mosaic mode in order to map contiguous fields on sky. The instrument covers the whole range of NIR atmospheric windows (0.8. . .2.5μm) with 5 spectral bands and a resolution of R ≈ 3000. . .4000.Although the main science driver for KMOS was to enable the study of galaxy formation and evolution through multiplexed observations of high-redshift galaxies, KMOS also already exhibited its tremendous potential for the spectroscopy of massive stars: A quantitative study of 27 RSGs in NGC 300 (Gazak et al. 2015) proves its applicability for the spectroscopy of individual stars even beyond the Local Group. A Mosaic observation of the Galactic centre (Feldmeier-Krause et al. 2015) demonstrates how spectra of early-type stars can be extracted from a contiguous field. Other applications include (but need not be limited to) velocity determinations of globular cluster stars, observations of jets/outflows of high mass protostars, or contiguous mapping of star-forming regions.We therefore aim at presenting the excellent capabilities of KMOS to a wider community and indicate potential applications.

scholarly journals Structural diversity of disc galaxies originating in the cold gas inflow from cosmic webs

2020 ◽  
Vol 494 (1) ◽  
pp. L37-L41
Author(s):  
Masafumi Noguchi
Keyword(s):  
High Redshift ◽  
Morphological Diversity ◽  
Structural Diversity ◽  
High Mass ◽  
Cold Flow ◽  
Hubble Sequence ◽  
The Galaxy ◽  
Gas Accretion ◽  
Disc Galaxies ◽  
Cold Gas

ABSTRACT Disc galaxies show a large morphological diversity with varying contribution of three major structural components: thin discs, thick discs, and central bulges. Dominance of bulges increases with the galaxy mass (Hubble sequence), whereas thick discs are more prominent in lower mass galaxies. Because galaxies grow with the accretion of matter, this observed variety should reflect diversity in accretion history. On the basis of the prediction by the cold-flow theory for galactic gas accretion and inspired by the results of previous studies, we put a hypothesis that associates different accretion modes with different components. Namely, thin discs form as the shock-heated hot gas in high-mass haloes gradually accretes to the central part, thick discs grow by the direct accretion of cold gas from cosmic webs when the halo mass is low, and finally bulges form by the inflow of cold gas through the shock-heated gas in high-redshift massive haloes. We show that this simple hypothesis reproduces the mean observed variation of galaxy morphology with the galaxy mass. This scenario also predicts that thick discs are older and poorer in metals than thin discs, in agreement with the currently available observations.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

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