CLASSIFICATION OF JETS INACTIVE GALACTIC NUCLEI
Section: HIGH ENERGY ASTROPHYSICS
Authors:
1.
Moscow Institute of Physics and Technology
2.
Astro Space Center of P. N. Lebedev Physical Institute of the Russian Academy of Sciences
Institute of Physics, Pontifical Catholic University of Valparaiso
Institute of Physics, Pontifical Catholic University of Valparaiso
Type:
Сonference article
Published:
27.12.2024
Subject area:
UDK 52 Астрономия. Геодезия
UDK 53 Физика
UDK 520 Инструменты, приборы и методы астрономических наблюдений, измерений и анализа
UDK 521 Теоретическая астрономия. Небесная механика. Фундаментальная астрономия. Теория динамической и позиционной астрономии
UDK 523 Солнечная система
UDK 524 Звезды и звездные системы. Вселенная Солнце и Солнечная система
UDK 52-1 Метод изучения
UDK 52-6 Излучение и связанные с ним процессы
GRNTI 41.00 АСТРОНОМИЯ
GRNTI 29.35 Радиофизика. Физические основы электроники
GRNTI 29.31 Оптика
GRNTI 29.33 Лазерная физика
GRNTI 29.27 Физика плазмы
GRNTI 29.05 Физика элементарных частиц. Теория полей. Физика высоких энергий
OKSO 03.06.01 Физика и астрономия
OKSO 03.05.01 Астрономия
OKSO 03.04.03 Радиофизика
BBK 2 ЕСТЕСТВЕННЫЕ НАУКИ
BBK 223 Физика
TBK 614 Астрономия
TBK 6135 Оптика
BISAC SCI004000 Astronomy
BISAC SCI005000 Physics / Astrophysics
UDK 53 Физика
UDK 520 Инструменты, приборы и методы астрономических наблюдений, измерений и анализа
UDK 521 Теоретическая астрономия. Небесная механика. Фундаментальная астрономия. Теория динамической и позиционной астрономии
UDK 523 Солнечная система
UDK 524 Звезды и звездные системы. Вселенная Солнце и Солнечная система
UDK 52-1 Метод изучения
UDK 52-6 Излучение и связанные с ним процессы
GRNTI 41.00 АСТРОНОМИЯ
GRNTI 29.35 Радиофизика. Физические основы электроники
GRNTI 29.31 Оптика
GRNTI 29.33 Лазерная физика
GRNTI 29.27 Физика плазмы
GRNTI 29.05 Физика элементарных частиц. Теория полей. Физика высоких энергий
OKSO 03.06.01 Физика и астрономия
OKSO 03.05.01 Астрономия
OKSO 03.04.03 Радиофизика
BBK 2 ЕСТЕСТВЕННЫЕ НАУКИ
BBK 223 Физика
TBK 614 Астрономия
TBK 6135 Оптика
BISAC SCI004000 Astronomy
BISAC SCI005000 Physics / Astrophysics
Language:
English
Keywords:
galaxies: active; quasars: general; techniques: high angular resolution; methods: machine learning
Abstract (English):
This work explores the application of machine learning techniques to classifying jetted active galactic nuclei (AGN) based on Very-Long-Baseline Interferometry (VLBI) observations at frequencies 1-90 GHz. Building upon previous work by Fanaroff and Riley, who classified relativistic jets in radio galaxies on kiloparsec scales, we extend this classification to parsec scales, closer to the central supermassive black hole. This approach enables detailed study of jet spatial structures and can help enhancing accuracy in global positioning systems. We define four morphological classes: single Gaussian source, double Gaussian source, and sources with single or double-sided jets. Synthetic models of AGN jets were generated to create a training dataset for a convolutional neural network (CNN). The CNN was trained on these synthetic data and subsequently applied to classify 130 thousand AGN jet images from the Astrogeo database. The distribution of images into designated classes, predicted by CNN, qualitatively matches the expected outcome.
This work explores the application of machine learning techniques to classifying jetted active galactic nuclei (AGN) based on Very-Long-Baseline Interferometry (VLBI) observations at frequencies 1-90 GHz. Building upon previous work by Fanaroff and Riley, who classified relativistic jets in radio galaxies on kiloparsec scales, we extend this classification to parsec scales, closer to the central supermassive black hole. This approach enables detailed study of jet spatial structures and can help enhancing accuracy in global positioning systems. We define four morphological classes: single Gaussian source, double Gaussian source, and sources with single or double-sided jets. Synthetic models of AGN jets were generated to create a training dataset for a convolutional neural network (CNN). The CNN was trained on these synthetic data and subsequently applied to classify 130 thousand AGN jet images from the Astrogeo database. The distribution of images into designated classes, predicted by CNN, qualitatively matches the expected outcome.
Keywords:
galaxies: active; quasars: general; techniques: high angular resolution; methods: machine learning
galaxies: active; quasars: general; techniques: high angular resolution; methods: machine learning
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