scholarly journals Mass Estimation of Galaxy Clusters with Deep Learning II. Cosmic Microwave Background Cluster Lensing

2021 ◽  
Vol 923 (1) ◽  
pp. 96
Author(s):  
N. Gupta ◽  
C. L. Reichardt
Keyword(s):  
Deep Learning ◽  
Cosmic Microwave Background ◽  
Galaxy Clusters ◽  
Gravitational Lensing ◽  
Galaxy Cluster ◽  
Learning Model ◽  
Potential Bias ◽  
Microwave Background ◽  
The Masses ◽  
Deep Learning Model

Abstract We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from images of the microwave sky and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a feed-forward deep-learning network, mResUNet, for both steps of the analysis. The first deep-learning model, mResUNet-I, is trained to reconstruct foreground and noise-suppressed CMB maps from a set of simulated images of the microwave sky that include signals from the CMB, astrophysical foregrounds like dusty and radio galaxies, instrumental noise as well as the cluster’s own thermal Sunyaev–Zel’dovich signal. The second deep-learning model, mResUNet-II, is trained to estimate cluster masses from the gravitational-lensing signature in the reconstructed foreground and noise-suppressed CMB maps. For SPTpol-like noise levels, the trained mResUNet-II model recovers the mass for 104 galaxy cluster samples with a 1σ uncertainty Δ M 200 c est / M 200 c est = 0.108 and 0.016 for input cluster mass M 200 c true = 10 14 M ⊙ and 8 × 1014 M ⊙, respectively. We also test for potential bias on recovered masses, finding that for a set of 105 clusters the estimator recovers M 200 c est = 2.02 × 10 14 M ⊙ , consistent with the input at 1% level. The 2σ upper limit on potential bias is at 3.5% level.

scholarly journals Deep learning for Sunyaev–Zel’dovich detection in Planck

2020 ◽  
Vol 634 ◽  
pp. A81
Author(s):  
V. Bonjean
Keyword(s):  
Deep Learning ◽  
Galaxy Clusters ◽  
Large Scale ◽  
Galaxy Cluster ◽  
Proof Of Concept ◽  
Microwave Background ◽  
Large Scale Structures ◽  
Hot Gas ◽  
Scale Structures ◽  
The Universe

The Planck collaboration has extensively used the six Planck HFI frequency maps to detect the Sunyaev–Zel’dovich (SZ) effect with dedicated methods, for example by applying (i) component separation to construct a full-sky map of the y parameter or (ii) matched multi-filters to detect galaxy clusters via their hot gas. Although powerful, these methods may still introduce biases in the detection of the sources or in the reconstruction of the SZ signal due to prior knowledge (e.g. the use of the generalised Navarro, Frenk, and White profile model as a proxy for the shape of galaxy clusters, which is accurate on average but not for individual clusters). In this study, we use deep learning algorithms, more specifically, a U-net architecture network, to detect the SZ signal from the Planck HFI frequency maps. The U-net shows very good performance, recovering the Planck clusters in a test area. In the full sky, Planck clusters are also recovered, together with more than 18 000 other potential SZ sources for which we have statistical indications of galaxy cluster signatures, by stacking at their positions several full-sky maps at different wavelengths (i.e. the cosmic microwave background lensing map from Planck, maps of galaxy over-densities, and the ROSAT X-ray map). The diffuse SZ emission is also recovered around known large-scale structures such as Shapley, A399–A401, Coma, and Leo. Results shown in this proof-of-concept study are promising for potential future detection of galaxy clusters with low SZ pressure with this kind of approach, and more generally, for potential identification and characterisation of large-scale structures of the Universe via their hot gas.

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.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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