Օբյեկտ

Վերնագիր: Astronomical objects classification based on the Digitized First Byurakan Survey low-dispersion spectra

Հեղինակ:

Astsatryan Hrachya ; Mickaelian A.

Տեսակ:

article

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:

Astsatryan, H., Knyazyan, A., Mickaelian, A., Sargsyan, L., 2010. Web portal for thearmenian virtual observatory based on armenian national grid infrastructure.Parallel Comput. Control Probl. 109–114. ; Astsatryan, H., Sahakyan, V., Shoukourian, Y., Dongarra, J., Cros, P.-H., Dayde, M.,Oster, P., 2015. Strengthening compute and data intensive capacities of armenia.In: 14th RoEduNet International Conference-Networking in Educationand Research (RoEduNet NER). IEEE, pp. 28–33. ; Buades, A., Coll, B., Morel, J.-M., 2005. A review of image denoising algorithms,with a new one. Multiscale Model. Simul. 4 (2), 490–530. ; Demleitner, M., Gufler, B., Kim, J., Lemson, G., Nickelt-Czycykowski, I., Rauch, T.,Stampa, U., Steinmetz, M., Voges, W., Wambsganss, J., 2007. The germanastrophysical virtual observatory (gavo): archives and applications, statusand services. Astron. Nachr. 328 (7), 713. ; Genova, F., Allen, M.G., Arviset, C., Lawrence, A., Pasian, F., Solano, E., Wambsganss,J., 2015. Euro-vo—Coordination of virtual observatory activities ineurope. Astron. Comput. 11, 181–189. ; Hajian, A., Alvarez, M.A., Bond, J.R., 2015. Machine learning etudes in astrophysics:selection functions for mock cluster catalogs. J. Cosmol. Astropart.Phys. 2015 (01), 038. ; Hanisch, R.J., 2012. Science initiatives of the US virtual astronomical observatory.Open Astron. 21 (3), 301–308. ; Hanisch, R.J., 2014. The virtual observatory: I. Astron. Comput. 7, 1–2. ; Hanisch, R.J., Farris, A., Greisen, E.W., Pence, W.D., Schlesinger, B.M., Teuben, P.J.,Thompson, R.W., Warnock, A., 2001. Definition of the flexible image transportsystem (fits). Astron. Astrophys. 376 (1), 359–380. ; Huchra, J.P., 1977. The nature of markarian galaxies. Astrophys. J. Suppl. Ser. 35,171–195. ; Indolia, S., Goswami, A.K., Mishra, S., Asopa, P., 2018. Conceptual understandingof convolutional neural network-a deep learning approach. Procedia Comput.Sci. 132, 679–688. ; Kohonen, T., 1982. Analysis of a simple self-organizing process. Biol. Cybern. 44(2), 135–140. ; MacQueen, J., et al., 1967. Some methods for classification and analysisof multivariate observations. In: Proceedings of the Fifth Berkeley Symposiumon Mathematical Statistics and Probability. Oakland, CA, USA,pp. 281–297. ; Massaro, E., Mickaelian, A., Nesci, R., Weedman, D., 2008. The digitized firstbyurakan survey. In: Aracne. p. 78. ; Mickaelian, A., Astsatryan, H., Knyazyan, A., Magakian, T.Y., Mikayelyan, G.,Erastova, L., Hovhannisyan, L., Sargsyan, L., Sinamyan, P., 2016. Ten yearsof the armenian virtual observatory. Astron. Soc. Pac. Conf. Ser. 505,16–23. ; Mickaelian, A., Nesci, R., Rossi, C., Weedman, D., Cirimele, G., Sargsyan, L.,Gaudenzi, S., 2007. The digitized first byurakan survey — Dfbs. Astron.Astrophys. 464 (3), 1177–1180. ; Mickaelian, A.M., Sargsyan, L.A., Astsatryan, H.V., Cirimele, G., Nesci, R., 2009.The dfbs spectroscopic database and the armenian virtual observatory. DataSci. J. 0905280112. ; Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted boltzmannmachines. ICML. ; Pavlidis, T., 2012. Algorithms for graphics and image processing. Springer Science& Business Media. ; Quinn, P.J., Barnes, D.G., Csabai, I., Cui, C., Genova, F., Hanisch, B., Kembhavi,A., Kim, S.C., Lawrence, A., Malkov, O., et al., 2004. The internationalvirtual observatory alliance: recent technical developments and the roadahead. In: Optimizing Scientific Return for Astronomy Through InformationTechnologies, Vol. 5493. International Society for Optics and Photonicspp. 137–145. ; Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations byback-propagating errors. Nature 323 (6088), 533–536. ; Scaife, A., 2020. Big telescope, big data: towards exascale with the squarekilometre array. Phil. Trans. R. Soc. A 378 (2166), 20190060. ; Zhang, M., 2019. Use density-based spatial clustering of applications with noise(dbscan) algorithm to identify galaxy cluster members. IOP Conf. Ser.: EarthEnviron. Sci. 242 (4), 042033.

Ինդեքսավորում:

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

Օբյեկտի հավաքածուներ:

Վերջին անգամ ձևափոխված:

Apr 23, 2021

Մեր գրադարանում է սկսած:

Apr 23, 2021

Օբյեկտի բովանդակության հարվածների քանակ:

48

Օբյեկտի բոլոր հասանելի տարբերակները:

https://noad.sci.am/publication/149765

Ցույց տալ նկարագրությունը RDF ձևաչափով:

RDF

Ցույց տալ նկարագրությունը OAI-PMH ձևաչափով։

OAI-PMH

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