The active radio galaxy 1413+135

Nature ◽  
1982 ◽  
Vol 295 (5851) ◽  
pp. 718-718
Author(s):  
H. S. MURDOCH
Keyword(s):  
Radio Galaxy

Evidence of Energy Nonequipartition between Particles and Fields in Lobes of the Radio Galaxy PKS 1343−601 (Centaurus B)

1998 ◽  
Vol 499 (2) ◽  
pp. 713-718 ◽  
Author(s):  
M. Tashiro ◽  
H. Kaneda ◽  
K. Makishima ◽  
N. Iyomoto ◽  
E. Idesawa ◽  
...  
Keyword(s):  
Radio Galaxy

ASCAObservations of the Broad‐Line Radio Galaxy 3C 445

1998 ◽  
Vol 495 (2) ◽  
pp. 749-756 ◽  
Author(s):  
Rita M. Sambruna ◽  
I. M. George ◽  
R. F. Mushotzky ◽  
K. Nandra ◽  
T. J. Turner
Keyword(s):  
Radio Galaxy ◽  
Broad Line

Evidence of Fermi Acceleration of Lyα in the Radio Galaxy 1243+036

1998 ◽  
Vol 505 (2) ◽  
pp. 634-638 ◽  
Author(s):  
Luc Binette ◽  
Benoit Joguet ◽  
John C. L. Wang
Keyword(s):  
Radio Galaxy ◽  
Fermi Acceleration

scholarly journals CNN architecture comparison for radio galaxy classification

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler
Keyword(s):  
Comparative Study ◽  
Galaxy Evolution ◽  
Radio Galaxy ◽  
Recognition Performance ◽  
Model Complexity ◽  
Great Promise ◽  
Radio Sources ◽  
Morphological Classification ◽  
Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

scholarly journals Deducing the orbit of the radio galaxy 3C 129

2000 ◽  
Vol 174 ◽  
pp. 408-411 ◽  
Author(s):  
K. Nilsson ◽  
M. Valtonen ◽  
J.-Q. Zheng ◽  
G. Byrd ◽  
H. Korhonen ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Radio Galaxy ◽  
Optical Spectra

AbstractWe have obtained new optical spectra of the radio galaxy 3C 129 and the giant galaxy close to it. From these spectra we deduce a relative radial velocity of 710 km s−1 between the two galaxies. Using the orbit calculations of Byrd & Valtonen (1978) and the new observations we obtain a new value, 3.3 × 1014M⊙, for the mass of the system.

scholarly journals The giant star forming halo associated with the radio galaxy PKS 1932-46

2005 ◽  
Vol 359 (1) ◽  
pp. L5-L9 ◽  
Author(s):  
M. Villar-Martin ◽  
C. Tadhunter ◽  
R. Morganti ◽  
J. Holt
Keyword(s):  
Radio Galaxy ◽  
Giant Star ◽  
Star Forming

scholarly journals A z = 2.5 protocluster associated with the radio galaxy MRC 2104-242: star formation and differing mass functions in dense environments

2014 ◽  
Vol 440 (4) ◽  
pp. 3262-3274 ◽  
Author(s):  
E. A. Cooke ◽  
N. A. Hatch ◽  
S. I. Muldrew ◽  
E. E. Rigby ◽  
J. D. Kurk
Keyword(s):  
Star Formation ◽  
Radio Galaxy ◽  
Mass Functions

scholarly journals Discovery of H i gas in a young radio galaxy at z = 0.44 using the Australian Square Kilometre Array Pathfinder

2015 ◽  
Vol 453 (2) ◽  
pp. 1249-1267 ◽  
Author(s):  
J. R. Allison ◽  
E. M. Sadler ◽  
V. A. Moss ◽  
M. T. Whiting ◽  
R. W. Hunstead ◽  
...  
Keyword(s):  
Radio Galaxy ◽  
Square Kilometre Array
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