Results 11 to 20 of about 9,733 (158)

Analysis of EMCCD gain estimation techniques for use in astronomical instruments [PDF]

, 2023
Electron-multiplied Charge Coupled Devices (EMCCDs) are used in many astro nomical instruments where they provide much improved signal to noise compared to conventional CCDs.
McErlean, Donal
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Toward an Internally Consistent Astronomical Distance Scale [PDF]

, 2017
Accurate astronomical distance determination is crucial for all fields in astrophysics, from Galactic to cosmological scales. Despite, or perhaps because of, significant efforts to determine accurate distances, using a wide range of methods, tracers, and
Monelli, Matteo, Oguri, Masamune, De Grijs, Richard, Masamune Oguri, Matteo Monelli, Suyu, Sherry H., Courbin, F., Sherry H. Suyu, Martínez-Vázquez, C., Monelli, M., Clara E. Martínez-Vázquez, Martinez-Vazquez, Clara E., Suyu, S., Oguri, M., Richard de Grijs, Courbin, Frederic, Frédéric Courbin, de Grijs, R.   +17 more
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EMU:Evolutionary Map of the Universe [PDF]

, 2011
EMU is a wide-field radio continuum survey planned for the new Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The primary goal of EMU is to make a deep (rms ∼10Jy/beam) radio continuum survey of the entire Southern sky at 1.3GHz ...
Ian Smail, Röttgering, H., Mao, Minnie Y., Feain, I, Hopkins, A., Pimbblet, K. A., Norris, Ray P. (R17893), Jarvis, Matthew, Jackson, C., Lopez-Sanchez, Angel R., E. Ibar, D. Bacon, I. Feain, Hopkins, Andrew M., B. S. Koribalski, Messias, H., J. V. Wall, Ibar, E., Urquhart, J. S., Alexander, D.M., Saikia, D.J., Thompson, M., Thompson, Mark, Hopkins, AM, O. B. Slee, Brown, S., Brown, S, Jarvis, Matt J., E. Lenc, Ivison, R., Thompson, MA, Umana, G, Eales, S, Afonso, J, Afonso, J., Reiprich, T.H., Mao, M. Y., Brown, MJI, Edge, A. C., Andernach, H., Sharp, R.G., Umana, Grazia, S. Eales, Alexander, David M., Hollow, R, Messias, H, D. M. Alex, Pimbblet, Kevin A., Sharp, R. G., Johnston-Hollitt, M., R€ottgering, H., Jarvis, M.J., Bacon, David, Ciliegi, P., Hollow, R., M. Jarvis, H. Andernach, I. G. Roseboom, Lenc, A., Middelberg, E, J. J. Condon, H. Röttgering, Umana, Grazia M., Rudnick, Lawrence R., P. Hancock, Padovani, P., Murphy, T., Dunne, L., Roettgering, H, M. A. Thompson, Zhao, G.-B., Brown, Michael J. I., Smail, I., Murphy, T, Lenc, Emil, Rottgering, Huub J. A., Dunne, L, M. T. Huynh, Huynh, Minh T., Urquhart, J., P. Ciliegi, Brown, M.J.I., Padovani, P, Prandoni, Isabella, Condon, J., Jarvis, M., Raccanelli, A, M. Johnston-hollitt, Umana, G., Middelberg, Enno, Randall, K. E., Afonso, Jose M., Zhao, G. -B., Appleton, P. N., N. Seymour, Kimball, A., Zhao, Gong-Bo, Urquhart, J.S., Bacon, D., Hopkins, A. M., Becker, W, Koribalski, B.S., Rudnick, L., Roseboom, I., Kimball, AE, López-Sánchez, Á. R., Norris, R.P., A. C. Edge, Reiprich, T., Johnston-Hollitt, M, D. J. Saikia, Saikia, D. J., T. H. Reiprich, Slee, O. B., Ciliegi, P, G. Giovannini, Hopkins, A.M., Padovani, Paolo, Norris, Ray P., Edge, A., Reiprich, TH, L. Dunne, Sharp, RG, Condon, J. J., Urquhart, JS, J. S. Urquhart, Kennicutt, R., Giovannini, G., Alexander, D. M., Koekemoer, A.M., B. M. Gaensler, Randall, K., Condon, JJ, Edge, AC, Seymour, Nick, Pimbblet, K.A., Ivison, R.J., Rttgering, H., Sharp, R., Randall, K.E., Brown, M., Roseboom, IG, Koribalski, B. S., I. G. Roseboom H. Röttgering, Kennicutt, R, Smail, I, K. E. Randall, I. Pr, Randall, KE, Jarvis, M, I. Prandoni, Prandoni, I, Mao, M.Y., Appleton, PN, Alexander, D., Rudnick, L, Roseboom, I.G., Feain, I., Jackson, Carole, S. Brown, Bacon, D, Feain, Ilana J., C. A. Hales, Roseboom, I. G., Eales, S., Reiprich, T. H., Brown, M. J. I., Roseboom, Isaac G., Ray P. Norris, Mao, MY, Condon, James J., H. Messias, Hollow, Robert P., Jackson, C, Koribalski, BS, C. Jackson, Hales, C. A., Prandoni, I., Seymour, N, Edge, A.C., Banfiled, J., Á. R. López-Sánchez, M. Y. Mao, Appleton, Philip N., Mao, M., Wall, J. V., E. Middelberg, J. Banfield, Kimball, Amy E., Amy E, Andernach, Heinz, Koribalski, Baerbel S., Amy E. Kimball, Afonso, Jose, W. Becker, Zhao, G., Eales, Stephen Anthony, Wall, J.V., Zhao, G-B, López-Sá&nchez, Á.R., Roettgering, H., Lpez-Snchez, R., Huynh, M. T., G.-B. Zhao, Lenc, E, Lopez-Sanchez, AR, Raccanelli, A., Alexander, DM, R. Kennicutt, Appleton, P., A. M. Hopkins, Edge, Alastair C., Gaensler, B.M., J. Afonso, Thompson, M. A., Smail, Ian, M. Y. Huynh, Thompson, M.A., R. G. Sharp, Lopez-Sanchez, A., Ibar, E, Sharp, Robert G., Hales, C.A., Norris, RP, Slee, O., Wall, J., Johnston-Hollitt, Melanie, Raccanelli, Alvise, A. M. Koekemoer, Hancock, P, T. Murphy, Ivison, R. J., A. Raccanelli, G. -b. Zhao, P. N. Appleton, Andernach, H, Gaensler, B. M., Huynh, M.T., Norris, R., Seymour, N., Kimball, A.E., R. J. Ivison, Huynh, MT, Wall, JV, Slee, OB, M. J. I. Brown, Dunne, Loretta, Gaensler, B., Hancock, P., R. Hollow, Hales, CA, Messia, H., P. Padovani, G. Umana, Saikia, DJ, Middelberg, E., Becker, W., Ivison, RJ, Appleton, P.N., Hales, C., Koekemoer, AM, K. A. Pimbblet, Banfield, J., Banfield, J, Pimbblet, KA, L. Rudnick, Lenc, E., Condon, J.J., D. M. Alexander, Gaensler, BM, Brown, Shea D., Koribalski, B., Huynh, M., Y. Mao, Saikia, D., Slee, O.B., Pimbblet, K., Koekemoer, A., Koekemoer, A. M., Giovannini, G   +279 more
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Analyzing astronomical data with machine learning techniques

Astronomical and Astrophysical Transactions, 2022
Classification is a popular task in the field of machine learning (ML) and artificial intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempt to draw some conclusions from observed values, so classification algorithms predict categorical class labels for use in classifying new data ...
openaire   +2 more sources

Lucky exposures: diffraction limited astronomical imaging through the atmosphere [PDF]

, 2003
The resolution of astronomical imaging from large optical telescopes is usually limited by the blurring effects of refractive index fluctuations in the Earth’s atmosphere. By taking a large number of short exposure images through the atmosphere, and then
Tubbs, Robert Nigel
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Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory [PDF]

, 2023
In the present article, we study the possibilities of machine learning for the estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24″ N, 66∘53′47″ E) using only Era-5 reanalysis data.
Alexander V. Kiselev, Kirill E. Kirichenko, Shuhrat A. Ehgamberdiev, Pavel G. Kovadlo, Evgeniy A. Kopylov, Yusufjon A. Tillayev, Artem Y. Shikhovtsev   +6 more
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Comparison of density estimation methods for astronomical datasets [PDF]

, 2011
Context. Galaxies are strongly influenced by their environment. Quantifying the galaxy density is a difficult but critical step in studying the properties of galaxies. Aims.
Buddelmeijer, H.,, M. H. F. Wilkinson, Buddelmeijer, H.; id_orcid, Trager, S.C.,, Ferdosi, B.J.,, Trager, S.C.; id_orcid, Wilkinson, M. H. F., Ferdosi, B. J., Roerdink, J. B. T. M.; id_orcid, Wilkinson, M.H.F.,, Trager, S.C., S. C. Trager, Roerdink, J.B.T.M.,, Buddelmeijer, H., B. J. Ferdosi, Roerdink, J. B. T. M., Wilkinson, M. H. F.; id_orcid, H. Buddelmeijer, J. B. T. M. Roerdink   +18 more
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Astronomical Data Features Extraction and Citation Prediction [PDF]

, 2023
Natural Language Processing methods present promising opportunities for analyzing astronomical data, enabling the extraction of essential information from vast amounts of observations.
Kutsuruk, Vladyslav
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Infrared fibres in astronomical instrumentation [PDF]

, 1995
For several years multi-object spectroscopy systems have been available for carrying out survey work in the visible region, but until very recently there has not been a system for the near infrared region.
Haynes, Roger
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Discovering the unexpected in astronomical survey data [PDF]

, 2017
Most major discoveries in astronomy are unplanned, and result from surveying the Universe in a new way, rather than by testing a hypothesis or conducting an investigation with planned outcomes.
Norris, Ray P. (R17893)
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