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

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):  

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler

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.


2000 ◽  
Vol 174 ◽  
pp. 408-411 ◽  
Author(s):  
K. Nilsson ◽  
M. Valtonen ◽  
J.-Q. Zheng ◽  
G. Byrd ◽  
H. Korhonen ◽  
...  

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.


Author(s):  
M. Villar-Martin ◽  
C. Tadhunter ◽  
R. Morganti ◽  
J. Holt
Keyword(s):  

2014 ◽  
Vol 440 (4) ◽  
pp. 3262-3274 ◽  
Author(s):  
E. A. Cooke ◽  
N. A. Hatch ◽  
S. I. Muldrew ◽  
E. E. Rigby ◽  
J. D. Kurk

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

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