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The increasingly large amount of cosmological data coming from ground-based and space-borne telescopes requires highly efficient and fast enough data analysis techniques to maximise the scientific exploitation.
A hybrid deep learning approach to cosmological constraints from galaxy redshift surveys
Ntampaka, M., Eisenstein, D.J., Yuan, S., Garrison, L.H., 2019 · 1909
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Multivariate uncertainty in deep learning
Russell, R.L., Reale, C., 2019 · 1910
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Some studies in machine learning using the game of checkers
Samuel, A.L., 1959 · 1959
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The correlation function for the distribution of galaxies
Totsuji, H., Kihara, T., 1969 · 1969
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The gravitational-instability picture and the nature of the distribution of galaxies
Peebles, P.J., 1974 · 1974
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Non-gaussian statistics and the microwave background radiation
Coles, P., Barrow, J.D., 1987 · 1987
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Clustering in real space and in redshift space
Kaiser, N., 1987 · 1987
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A lognormal model for the cosmological mass distribution
Coles, P., Jones, B., 1991 · 1991
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Bias and variance of angular correlation functions
Landy, S.D., Szalay, A.S., 1993 · 1993
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Principles of physical cosmology
Peebles, P.J.E., 1993 · 1993
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Uncorrelated modes of the non-linear power spectrum
Hamilton, A.J.S., 2000 · 2000
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Efficient Computation of Cosmic Microwave Background Anisotropies in Closed Friedmann-Robertson-Walker Models
Lewis, A., Challinor, A., Lasenby, A., 2000 · 2000
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The 2df galaxy redshift survey: correlation functions, peculiar velocities and the matter density of the universe
Hawkins, E., Maddox, S., Cole, S., Lahav, O., Madgwick, D.S., Norberg, P., Peacock, J.A., Baldry, I.K., Baugh, C.M., Bland-Hawthorn, J., et al., 2003 · 2003
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The 2df galaxy redshift survey: power-spectrum analysis of the final data set and cosmological implications
Cole, S., Percival, W.J., Peacock, J.A., Norberg, P., Baugh, C.M., Frenk, C.S., Baldry, I., Bland-Hawthorn, J., Bridges, T., Cannon, R., et al., 2005 · 2005
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The 2.5 m telescope of the sloan digital sky survey
Gunn, J.E., Siegmund, W.A., Mannery, E.J., Owen, R.E., Hull, C.L., Leger, R.F., Carey, L.N., Knapp, G.R., York, D.G., Boroski, W.N., et al., 2006 · 2006
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Matplotlib: A 2D Graphics Environment
Hunter, J.D., 2007 · 2007
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Quantifying uncertainty: modern computational representation of probability and applications, in: Extreme man-made and natural hazards in dynamics of structures. Springer, pp. 105–135
Matthies, H.G., 2007 · 2007
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Aleatory or epistemic? does it matter?
Der Kiureghian, A., Ditlevsen, O., 2009 · 2009
Cited alongside, same era.
The 6dF Galaxy Survey: baryon acoustic oscillations and the local Hubble constant
Beutler, F., Blake, C., Colless, M., Jones, D.H., Staveley-Smith, L., Campbell, L., Parker, Q., Saunders, W., Watson, F., 2011 · 2011
Cited alongside, same era.
Euclid definition study report
Laureijs, R., Amiaux, J., Arduini, S., Augueres, J.L., Brinchmann, J., Cole, R., Cropper, M., Dabin, C., Duvet, L., Ealet, A., et al., 2011 · 2011
Cited alongside, same era.
Large synoptic survey telescope: Dark energy science collaboration
LSST Dark Energy Science Collaboration, 2012 · 2012
Cited alongside, same era.
The wigglez dark energy survey: final data release and cosmological results
Parkinson, D., Riemer-Sørensen, S., Blake, C., Poole, G.B., Davis, T.M., Brough, S., Colless, M., Contreras, C., Couch, W., Croom, S., et al., 2012 · 2012
What uncertainties do we need in bayesian deep learning for computer vision?, in: Advances in neural information processing systems, pp. 5574–5584
Kendall, A., Gal, Y., 2017 · 2017
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Emulating simulations of cosmic dawn for 21 cm power spectrum constraints on cosmology, reionization, and x-ray heating
Kern, N.S., Liu, A., Parsons, A.R., Mesinger, A., Greig, B., 2017 · 2017
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The vimos public extragalactic redshift survey (vipers)
Pezzotta, A., de la Torre, S., Bel, J., Granett, B.R., Guzzo, L., Peacock, J.A., Garilli, B., Scodeggio, M., Bolzonella, M., Abbas, U., et al., 2017 · 2017
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Planck 2018 results. vi. cosmological parameters
Aghanim, N., Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A., Barreiro, R., Bartolo, N., Basak, S., et al., 2018 · 2018
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The vimos public extragalactic redshift survey (vipers)-unbiased clustering estimate with vipers slit assignment
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Cited alongside, same era.
The clustering of galaxies in the sdss-iii baryon oscillation spectroscopic survey: baryon acoustic oscillations in the data releases 10 and 11 galaxy samples
Anderson, L., Aubourg, E., Bailey, S., Beutler, F., Bhardwaj, V., Blanton, M., Bolton, A.S., Brinkmann, J., Brownstein, J.R., Burden, A., et al., 2014 · 2014
Cited alongside, same era.
The vimos public extragalactic redshift survey (vipers)- ω \omega m0 from the galaxy clustering ratio measured at z˜ 1
Bel, J., Marinoni, C., Granett, B., Guzzo, L., Peacock, J., Branchini, E., Cucciati, O., De La Torre, S., Iovino, A., Percival, W., et al., 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015 · 2015
Cited alongside, same era.
The eleventh and twelfth data releases of the sloan digital sky survey: final data from sdss-iii
Alam, S., Albareti, F.D., Prieto, C.A., Anders, F., Anderson, S.F., Anderton, T., Andrews, B.H., Armengaud, E., Aubourg, É., Bailey, S., et al., 2015 · 2015
Cited alongside, same era.
Testing deviations from λ \lambda cdm with growth rate measurements from six large-scale structure surveys at z= 0.06–1
Alam, S., Ho, S., Silvestri, A., 2016 · 2016
Cited alongside, same era.
Deep learning
Goodfellow, I., Bengio, Y., Courville, A., 2016 · 2016
Cited alongside, same era.
Mohammad, F., Bianchi, D., Percival, W., De La Torre, S., Guzzo, L., Granett, B.R., Branchini, E., Bolzonella, M., Garilli, B., Scodeggio, M., et al., 2018 · 2018
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Classifying the large-scale structure of the universe with deep neural networks
Aragon-Calvo, M.A., 2019 · 2019
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Machine learning and the future of supernova cosmology
Ishida, E.E., 2019 · 2019
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Comparing approximate methods for mock catalogues and covariance matrices–i. correlation function
Lippich, M., Sánchez, A.G., Colavincenzo, M., Sefusatti, E., Monaco, P., Blot, L., Crocce, M., Alvarez, M.A., Agrawal, A., Avila, S., et al., 2019 · 2019
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Euclid preparation-vii. forecast validation for euclid cosmological probes
Blanchard, A., Camera, S., Carbone, C., Cardone, V., Casas, S., Clesse, S., Ilić, S., Kilbinger, M., Kitching, T., Kunz, M., et al., 2020 · 2020
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The completed sdss-iv extended baryon oscillation spectroscopic survey: measurement of the bao and growth rate of structure of the luminous red galaxy sample from the anisotropic power spectrum between redshifts 0.6 and 1.0
Gil-Marín, H., Bautista, J.E., Paviot, R., Vargas-Magaña, M., de la Torre, S., Fromenteau, S., Alam, S., Ávila, S., Burtin, E., Chuang, C.H., et al., 2020 · 2020
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Array programming with NumPy
Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N.J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M.H., Brett, M., Haldane, A., del Río, J.F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., Oliphant, T.E., 2020 · 2020
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Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the ska
Hassan, S., Andrianomena, S., Doughty, C., 2020 · 2020
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Cosmological parameter estimation from large-scale structure deep learning
Pan, S., Liu, M., Forero-Romero, J., Sabiu, C.G., Li, Z., Miao, H., Li, X.D., 2020 · 2020
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Large-scale structures in the λ \lambda cdm universe: network analysis and machine learning
Tsizh, M., Novosyadlyj, B., Holovatch, Y., Libeskind, N.I., 2020 · 2020
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Scipy 1.0: fundamental algorithms for scientific computing in python
Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al., 2020 · 2020
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Cosmology in the machine learning era
Villaescusa-Navarro, F., 2021 · 2021
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