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The Astrophysical Journal (ApJ)

Resumen/Descripción – provisto por la editorial en inglés

The Astrophysical Journal is an open access journal devoted to recent developments, discoveries, and theories in astronomy and astrophysics. Publications in ApJ constitute significant new research that is directly relevant to astrophysical applications, whether based on observational results or on theoretical insights or modeling.

Palabras clave – provistas por la editorial

astronomy; astrophysics

Disponibilidad

Institución detectada	Período	Navegá	Descargá	Solicitá
No detectada	desde jul. 1995 / hasta dic. 2023	IOPScience

Información

Tipo de recurso:

revistas

ISSN impreso

0004-637X

ISSN electrónico

1538-4357

Editor responsable

American Astronomical Society (AAS)

Idiomas de la publicación

inglés

País de edición

Reino Unido

Información sobre licencias CC

https://creativecommons.org/licenses/by/4.0/

Cobertura temática

Ciencias físicas

Tabla de contenidos

doi: 10.3847/1538-4357/ab451b

2 mm GISMO Observations of the Galactic Center. II. A Nonthermal Filament in the Radio Arc and Compact Sources

Johannes Staguhn; Richard G. Arendt; Eli Dwek; Mark R. Morris; Farhad Yusef-Zadeh; Dominic J. Benford; Attila Kovács; Junellie Gonzalez-Quiles

Palabras clave: Space and Planetary Science; Astronomy and Astrophysics.

Pp. 72

doi: 10.3847/1538-4357/ab46bd

Solar Flare Prediction with the Hybrid Deep Convolutional Neural Network

Yanfang Zheng; Xuebao Li; Xinshuo Wang

<jats:title>Abstract</jats:title> <jats:p>We propose a hybrid Convolutional Neural Network (CNN) model (Model 2) and modify a popular CNN model (Model 1) to predict multiclass solar flare occurrence within 24 hr. We collect samples of solar active regions provided by the Space-weather Helioseismic and Magnetic Imager Active Region Patches data from 2010 May to 2018 September. These samples are categorized into four classes (No-flare, C, M, and X), containing 10 separate data sets. Then after training, validating, and testing our models, we compare the results with previous studies in forecast verification metrics with an emphasis on the true skill statistic (TSS). The main results are summarized as follows. (1) This is the first time that the CNN models are used to make multiclass predictions of solar flare. (2) Model 2 has better values of all statistical scores than Model 1 in every class. (3) Model 2 achieves relatively high average scores of TSS = 0.768 for No-flare class, 0.538 for C class, 0.534 for M class, and 0.552 for X class, which are the best results from the existing literatures. (4) Model 2 also can be used to make binary class flare predictions for ≥M-class major flares, and the performance yields a TSS with 0.749 ± 0.079. (5) Model 2 obtains fairly good scores in other metrics for both multiclass flare predictions and ≥M-class major flare predictions. We surmise that some crucial features extracted automatically by our models may have not been excavated before and could provide important clues for studying the mechanism of flare.</jats:p>

Palabras clave: Space and Planetary Science; Astronomy and Astrophysics.

Pp. 73