An active galactic nucleus recognition model based on deep neural network

Chen, Bo-Han; Goto, Tomotsugu; Kim, Seong‐Jin; Wang, Ting Wen; Santos, Daryl Joe D.; Ho, S. Y.; Hashimoto, Tetsuya; Poliszczuk, Artem; Pollo, A.; Trippe, Sascha; Miyaji, T.; Toba, Yoshiki; Malkan, Matthew A.; Serjeant, S.; Pearson, Chris; Hwang, Ho Seong; Kim, Eunbin; Shim, Hyunjin; Lu, Ting Yi; Hsiao, Yu-Yang; Huang, Ting-Chi; Herrera-Endoqui, M.; Bravo-Navarro, Blanca; Matsuhara, Hideo

doi:10.1093/mnras/staa3865

Cited by 15 publications

(8 citation statements)

References 52 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

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“…Recently, Machine Learning has found a broad application in cosmology; parameter estimation [45][46][47][48][49][50][51][52], fore-ground cleaning [42,43,53,54], feature extraction [55][56][57][58], inpainting [59][60][61], filtering [62], and modeling of physical processes [43,[63][64][65][66][67][68][69]. Deep Learning (DL) is a subset of Machine Learning that fits multiple non-linear functions to the input data using neural networks.…”

Section: Introductionmentioning

confidence: 99%

MillimeterDL: Deep Learning Simulations of the Microwave Sky

Han,

Sehgal,

Villaescusa-Navarro

2021

Preprint

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We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being optimized to match the power spectra. The procedure we develop in this work enables the capability to mass produce independent full-sky realizations from a single expensive full-sky simulation, when ordinarily the latter would not provide enough training data. We also circumvent a common limitation of high-resolution DL simulations that they be confined to small sky areas, often due to memory or GPU issues; we do this by developing a "stitching" procedure that can faithfully recover the high-order statistics of a full-sky map without discontinuities or repeated features. In addition, since our network takes as input a full-sky lensing convergence map, it can in principle take a full-sky lensing convergence map from any large-scale structure (LSS) simulation and generate the corresponding lensed CMB and correlated foreground components at millimeter wavelengths; this is especially useful in the current era of combining results from both CMB and LSS surveys, which require a common set of simulations.

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“…In recent years, several papers made use of machine learning techniques also in different problems of astrophysics (e.g. Lee 2019; Giri et al 2020b;Yoshiura et al 2020;Chen et al 2020) and cosmology (e.g. Jeffrey et al 2020;Sadr & Farsian 2020;Guzman & Meyers 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for identification of HII regions during reionization in 21-cm observations

Bianco,

Giri,

Iliev

et al. 2021

Preprint

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The upcoming Square Kilometre Array (SKA-Low) will map the distribution of neutral hydrogen during reionization, and produce a tremendous amount of 3D tomographic data. These images cubes will be subject to instrumental limitations, such as noise and limited resolution. Here we present SegU-Net, a stable and reliable method for identification of neutral and ionized regions in these images. SegU-Net is a U-Net architecture based convolutional neural network (CNN) for image segmentation. It is capable of segmenting our image data into meaningful features (ionized and neutral regions) with greater accuracy compared to previous methods. We can estimate the true ionization history from our mock observation of SKA with an observation time of 1000 h with more than 87 per cent accuracy. We also show that SegU-Net can be used to recover various topological summary statistics, such as size distributions and Betti numbers, with a relative difference of only a few per cent. These summary statistics characterise the non-Gaussian nature of the reionization process.

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An active galactic nucleus recognition model based on deep neural network

Cited by 15 publications

References 52 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

MillimeterDL: Deep Learning Simulations of the Microwave Sky

Deep learning approach for identification of HII regions during reionization in 21-cm observations

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