Imaging systems for low energy γ-ray astronomy

1984 ◽  
Vol 221 (1) ◽  
pp. 265-277 ◽  
Author(s):  
A.J. Dean
Keyword(s):  
Low Energy ◽  
Imaging Systems ◽  
Γ Ray

The miso low energy γ-ray telescope

1979 ◽  
Vol 158 ◽  
pp. 595-604 ◽  
Author(s):  
R.E. Baker ◽  
R.C. Butler ◽  
A.J. Dean ◽  
G. Di Cocco ◽  
N.A. Dipper ◽  
...  
Keyword(s):  
Low Energy ◽  
Γ Ray

The decay of 237Np

1976 ◽  
Vol 54 (13) ◽  
pp. 1409-1420 ◽  
Author(s):  
M. Skalsey ◽  
R. D. Connor
Keyword(s):  
Level Scheme ◽  
Low Energy ◽  
Coincidence Data ◽  
Γ Rays ◽  
Γ Ray

The decay of 237Np has been investigated from singles and coincidence γ-ray spectra acquired using Ge(Li) detectors. Forty-two γ rays have been observed and all but one have been placed in the level scheme of 233Pa. The presence of unobserved transitions of low energy has been confirmed by the coincidence data. Five new γ rays are reported here and a new level in 233Pa at 303.8 keV is proposed.

Feasibility studies of low energy γ-ray techniques for on-line determination of ash content of coal on conveyors

1983 ◽  
Vol 34 (1) ◽  
pp. 37-44 ◽  
Author(s):  
R.A. Fookes ◽  
V.L. Gravitis ◽  
J.S. Watt ◽  
C.E. Campbell ◽  
E. Steffner
Keyword(s):  
Feasibility Studies ◽  
Ash Content ◽  
Low Energy ◽  
On Line ◽  
Γ Ray

Low energy γ-ray observation of NGC4151

Nature ◽  
1977 ◽  
Vol 270 (5635) ◽  
pp. 319-320 ◽  
Author(s):  
G. DI COCCO ◽  
G. BOELLA ◽  
F. PEROTTI ◽  
R. STIGLITZ ◽  
G. VILLA ◽  
...  
Keyword(s):  
Low Energy ◽  
Γ Ray

scholarly journals Probing the unidentified Fermi blazar-like population using optical polarization and machine learning

2019 ◽  
Vol 486 (3) ◽  
pp. 3415-3422 ◽  
Author(s):  
I Liodakis ◽  
D Blinov
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
High Energy ◽  
Point Sources ◽  
Low Energy ◽  
Optical Polarization ◽  
Sky Surveys ◽  
Γ Ray ◽  
Energy Processes ◽  
The Universe

ABSTRACT The Fermi γ-ray space telescope has revolutionized our view of the γ-ray sky and the high-energy processes in the Universe. While the number of known γ-ray emitters has increased by orders of magnitude since the launch of Fermi, there is an ever increasing number of, now more than a thousand, detected point sources whose low-energy counterpart is to this day unknown. To address this problem, we combined optical polarization measurements from the RoboPol survey as well as other discriminants of blazars from publicly available all-sky surveys in machine learning (ML, random forest and logistic regression) frameworks that could be used to identify blazars in the Fermi unidentified fields with an accuracy of >95 per cent. Out of the potential observational biases considered, blazar variability seems to have the most significant effect reducing the predictive power of the frameworks to ${\sim }80\hbox{-}85{{\ \rm per\ cent}}$. We apply our ML framework to six unidentified Fermi fields observed using the RoboPol polarimeter. We identified the same candidate source proposed by Mandarakas et al. for 3FGL J0221.2 + 2518.

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