Համահեղինակ(ներ):

Gevorgyan G. ; Knyazyan Aram ; Mikayelyan G.A.

Ամփոփում:

The Digitized First Byurakan Survey is the largest and the first systematic objective-prism survey of the extragalactic sky. The detection, extraction, and classification of about 40 million spectra of about 20 million astronomical objects available in the survey require distinguishing the pixels containing photons from the source and the noise pixels per object. This paper aims at developing a service to classify the spectra of UV-excess galaxies, quasars, compact galaxies, and other objects in the survey. Supervised and unsupervised convolutional neural network deep learning algorithms have been developed and studied.

Հրատարակիչ:

Elsevier

Հանձնման ամսաթիվը:

29 July 2020

Ընդունման ամսաթիվը:

4 December 2020

Նույնականացուցիչ:

oai:noad.sci.am:136200

DOI:

10.1016/j.ascom.2020.100442

Լեզու:

English

Ամսագրի կամ հրապարակման վերնագիր:

Astronomy and Computing

Հատոր:

34

URL:

լրացուցիչ տեղեկատվություն:

The paper is supported by the European Union’s Horizon 2020research infrastructures programme under grant agreement No857645, project NI4OS Europe (National Initiatives for Open Sciencein Europe).

Կազմակերպության անվանում:

Institute for Informatics and Automation Problems of the National Academy of Sciences of the Republic of Armenia ; V. Ambartsumian Byurakan Astrophysical Observatory of the National Academy of Sciences of the Republic of Armenia

Տարի:

2020

Հիշատակումներ:

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Ինդեքսավորում:

Arts and Humanities Citation Index ; Astrophysics Data System ; INSPEC ; Science Citation Index Expanded ; Scopus ; Web of Science