Research and characterization of blazar candidates among the Fermi/LAT4FGL catalog using Machine Learning

Mao, Weiming; Lv, Xiankui; Kang, S. J.; Zhu, Kongfu; Hu, Haojin

doi:10.1109/itaic49862.2020.9339103

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Finally, the presence of the terms "active galactic nuclei" and "quasars" is notable within this group; these refer to the ML astronomical application that addresses the morphological classification of nuclei of active galaxies (Chang et al, 2021;Chen et al, 2020;Ma et al, 2019) and the analysis of gamma-ray emissions arising from these (Fidor & Sitarek, 2021) as well as the detection of quasars (Herle et al, 2020;Jin et al, 2019;Schindler et al, 2017) and blazars (Kang et al, 2019;Mao et al, 2020;Sversut & Neto, 2020).…”

Section: Analysis Of Galaxies By Means Of Ai and ML Techniques (Blue ...mentioning

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: Analysis Of Galaxies By Means Of Ai and ML Techniques (Blue ...mentioning

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

View full text Add to dashboard Cite

show abstract

Research and characterization of blazar candidates among the Fermi/LAT4FGL catalog using Machine Learning

Cited by 1 publication

References 14 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

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