Machine learning and Kolmogorov analysis to reveal gravitational lenses

Mirzoyan, S.; Khachatryan, H. G.; Yegorian, G.; Gurzadyan, V. G.

doi:10.1093/mnrasl/slz125

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Another field of application of AI/ML in cosmological analysis, as indicated in this cluster by the term “gravitational lensing,” is the study of gravitational lenses. In this sense, beyond the discoveries produced by AI/ML in this field (Mirzoyan et al, 2019; Ostrovski et al, 2017; Teimoorinia et al, 2020), it should be mentioned that one of the obstacles in applying DL in the search for this peculiar astrophysical phenomenon is the scarcity of data with which to carry out the training phase. For this reason, simulated lenses are often used, usually on a galactic scale.…”

Section: Resultsmentioning

confidence: 99%

On the application of machine learning in astronomy and astrophysics: A text‐mining‐based scientometric analysis

Rodríguez

Rodríguez‐Rodríguez

Woo

2022

WIREs Data Min & Knowl

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Since the beginning of the 21st century, the fields of astronomy and astrophysics have experienced significant growth at observational and computational levels, leading to the acquisition of increasingly huge volumes of data. In order to process this vast quantity of information, artificial intelligence (AI) techniques are being combined with data mining to detect patterns with the aim of modeling, classifying or predicting the behavior of certain astronomical phenomena or objects. Parallel to the exponential development of the aforementioned techniques, the scientific output related to the application of AI and machine learning (ML) in astronomy and astrophysics has also experienced considerable growth in recent years. Therefore, the increasingly abundant articles make it difficult to monitor this field in terms of which research topics are the most prolific or novel, or which countries or authors are leading them. In this article, a text‐mining‐based scientometric analysis of scientific documents published over the last three decades on the application of AI and ML in the fields of astronomy and astrophysics is presented. The VOSviewer software and data from the Web of Science (WoS) are used to elucidate the evolution of publications in this research field, their distribution by country (including co‐authorship), the most relevant topics addressed, and the most cited elements and most significant co‐citations according to publication source and authorship. The obtained results demonstrate how application of AI/ML to the fields of astronomy/astrophysics represents an established and rapidly growing field of research that is crucial to obtaining scientific understanding of the universe. This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning Application Areas > Science and Technology

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Section: Resultsmentioning

confidence: 99%

On the application of machine learning in astronomy and astrophysics: A text‐mining‐based scientometric analysis

Rodríguez

Rodríguez‐Rodríguez

Woo

2022

WIREs Data Min & Knowl

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“…Regarding the gravitational lensing, the most attractive application for ML tools is the possibility for automatic detection of strong gravitational lenses (GL) [4,7,14,16,20,21,25]. One of the main difficulties of this kind of application is that there is not enough big sample available by now (about 200 objects) of real observed strong GL.…”

Section: Introductionmentioning

confidence: 99%

On machine learning search for gravitational lenses

Khachatryan

2021

Preprint

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We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different methods, i.e. two for each class. Then we deploy a convolutional neural network architecture to classify these simulated images. We show that after neural network training process one achieves about 93 percent accuracy. As a simple test for the efficiency of the convolutional neural network, we apply it on an real Einstein cross image. Deployed neural network classifies it as gravitational lens, thus opening a way for variety of lens search applications of the deployed machine learning scheme.

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Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Oytun¹,

Tınazcı²,

Şekeroğlu³

et al. 2020

IEEE Access

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Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players' performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players' athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radialbasis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with R 2 scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies. INDEX TERMS Artificial intelligence, athletic performance, machine learning models, radial-basis function neural network.

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Machine learning and Kolmogorov analysis to reveal gravitational lenses

Cited by 4 publications

References 38 publications

On the application of machine learning in astronomy and astrophysics: A text‐mining‐based scientometric analysis

On the application of machine learning in astronomy and astrophysics: A text‐mining‐based scientometric analysis

On machine learning search for gravitational lenses

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

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