×

A deep learning approach to cosmological dark energy models. (English) Zbl 1490.83044


MSC:

83C56 Dark matter and dark energy
85A15 Galactic and stellar structure
85A25 Radiative transfer in astronomy and astrophysics
83D05 Relativistic gravitational theories other than Einstein’s, including asymmetric field theories
68T07 Artificial neural networks and deep learning
82C32 Neural nets applied to problems in time-dependent statistical mechanics
83C50 Electromagnetic fields in general relativity and gravitational theory
78A45 Diffraction, scattering

References:

[1] Supernova Search Team collaboration, 1998 Observational evidence from supernovae for an accelerating universe and a cosmological constant, https://doi.org/10.1086/300499 Astron. J.116 1009 [astro-ph/9805201] · doi:10.1086/300499
[2] Supernova Cosmology Project collaboration, 1999 Measurements of Ω and Λ from 42 high redshift supernovae, https://doi.org/10.1086/307221 Astrophys. J.517 565 [astro-ph/9812133] · Zbl 1368.85002 · doi:10.1086/307221
[3] http://desi.lbl.gov/
[4] https://www.darkenergysurvey.org/
[5] https://www.lsst.org/
[6] https://wfirst.gsfc.nasa.gov/
[7] M. Takada and B. Jain, 2003 The three-point correlation function in cosmology, https://doi.org/10.1046/j.1365-8711.2003.06321.x Mon. Not. Roy. Astron. Soc.340 580 [astro-ph/0209167] · doi:10.1046/j.1365-8711.2003.06321.x
[8] WiggleZ collaboration, 2013 The WiggleZ Dark Energy Survey: constraining galaxy bias and cosmic growth with 3-point correlation functions, https://doi.org/10.1093/mnras/stt520 Mon. Not. Roy. Astron. Soc.432 2654 [1303.6644] · doi:10.1093/mnras/stt520
[9] S. Tsujikawa, Dark energy: investigation and modeling, [1004.1493] · Zbl 1213.83011
[10] D. Huterer and M.S. Turner, 2001 Probing the dark energy: Methods and strategies, https://doi.org/10.1103/PhysRevD.64.123527 Phys. Rev. D 64 123527 [astro-ph/0012510] · doi:10.1103/PhysRevD.64.123527
[11] Y. Wang, 2008 Figure of Merit for Dark Energy Constraints from Current Observational Data, https://doi.org/10.1103/PhysRevD.77.123525 Phys. Rev. D 77 123525 [0803.4295] · doi:10.1103/PhysRevD.77.123525
[12] C. Escamilla-Rivera, R. Lazkoz, V. Salzano and I. Sendra, 2011 Tension between SN and BAO: current status and future forecasts J. Cosmol. Astropart. Phys.2011 09 003 [1103.2386]
[13] P. Bull et al., 2016 Beyond ΛCDM: Problems, solutions and the road ahead, https://doi.org/10.1016/j.dark.2016.02.001 Phys. Dark Univ.12 56 [1512.05356] · doi:10.1016/j.dark.2016.02.001
[14] Planck collaboration, Planck 2018 results. VI. Cosmological parameters, [1807.06209]
[15] L. Verde, T. Treu and A.G. Riess, Tensions between the Early and the Late Universe, in Nature Astronomy 2019, 2019, [1907.10625] · doi:10.1038/s41550-019-0902-0
[16] B. Ratra and P.J.E. Peebles, 1988 Cosmological Consequences of a Rolling Homogeneous Scalar Field, https://doi.org/10.1103/PhysRevD.37.3406 Phys. Rev. D 37 3406 · doi:10.1103/PhysRevD.37.3406
[17] C. Armendariz-Picon, V.F. Mukhanov and P.J. Steinhardt, 2000 A Dynamical solution to the problem of a small cosmological constant and late time cosmic acceleration, https://doi.org/10.1103/PhysRevLett.85.4438 Phys. Rev. Lett.85 4438 [astro-ph/0004134] · doi:10.1103/PhysRevLett.85.4438
[18] I. Sendra and R. Lazkoz, 2012 SN and BAO constraints on (new) polynomial dark energy parametrizations: current results and forecasts, https://doi.org/10.1111/j.1365-2966.2012.20661.x Mon. Not. Roy. Astron. Soc.422 776 [1105.4943] · doi:10.1111/j.1365-2966.2012.20661.x
[19] G.-B. Zhao, D. Bacon, R. Maartens, M. Santos and A. Raccanelli, Model-independent constraints on dark energy and modified gravity with the SKA, [1501.03840]
[20] C. Escamilla-Rivera, 2016 Status on bidimensional dark energy parameterizations using SNe Ia JLA and BAO datasets, https://doi.org/10.3390/galaxies4030008 Galaxies4 8 [1605.02702] · doi:10.3390/galaxies4030008
[21] M. Rezaei, M. Malekjani, S. Basilakos, A. Mehrabi and D.F. Mota, 2017 Constraints to Dark Energy Using PADE Parameterizations, https://doi.org/10.3847/1538-4357/aa7898 Astrophys. J.843 65 [1706.02537] · doi:10.3847/1538-4357/aa7898
[22] C. Escamilla-Rivera and S. Capozziello, 2019 Unveiling cosmography from the dark energy equation of state, https://doi.org/10.1142/S0218271819501542 Int. J. Mod. Phys. D 28 1950154 [1905.04602] · doi:10.1142/S0218271819501542
[23] L.G. Jaime, L. Patiño and M. Salgado, 2014 Note on the equation of state of geometric dark-energy in f(R) gravity, https://doi.org/10.1103/PhysRevD.89.084010 Phys. Rev. D 89 084010 [1312.5428] · doi:10.1103/PhysRevD.89.084010
[24] R. Lazkoz, M. Ortiz-Baños and V. Salzano, 2018 f(R) gravity modifications: from the action to the data, https://doi.org/10.1140/epjc/s10052-018-5711-6 Eur. Phys. J. C 78 213 [1803.05638] · doi:10.1140/epjc/s10052-018-5711-6
[25] S. Capozziello, R. D’Agostino and O. Luongo, 2019 Extended Gravity Cosmography, https://doi.org/10.1142/S0218271819300167 Int. J. Mod. Phys. D 28 1930016 [1904.01427] · Zbl 1425.83084 · doi:10.1142/S0218271819300167
[26] J. Alberto Vazquez, M. Bridges, M.P. Hobson and A.N. Lasenby, 2012 Reconstruction of the Dark Energy equation of state J. Cosmol. Astropart. Phys.2012 09 020 [1205.0847]
[27] M. Seikel, C. Clarkson and M. Smith, 2012 Reconstruction of dark energy and expansion dynamics using Gaussian processes J. Cosmol. Astropart. Phys.2012 06 036 [1204.2832]
[28] A. Montiel, R. Lazkoz, I. Sendra, C. Escamilla-Rivera and V. Salzano, 2014 Nonparametric reconstruction of the cosmic expansion with local regression smoothing and simulation extrapolation, https://doi.org/10.1103/PhysRevD.89.043007 Phys. Rev. D 89 043007 [1401.4188] · doi:10.1103/PhysRevD.89.043007
[29] G.-B. Zhao et al., 2017 Dynamical dark energy in light of the latest observations, https://doi.org/10.1038/s41550-017-0216-z Nat. Astron.1 627 [1701.08165] · doi:10.1038/s41550-017-0216-z
[30] S. Capozziello, 2002 Curvature quintessence, https://doi.org/10.1142/S0218271802002025 Int. J. Mod. Phys. D 11 483 [gr-qc/0201033] · Zbl 1062.83565 · doi:10.1142/S0218271802002025
[31] L.G. Jaime, M. Jaber and C. Escamilla-Rivera, 2018 New parametrized equation of state for dark energy surveys, https://doi.org/10.1103/PhysRevD.98.083530 Phys. Rev. D 98 083530 [1804.04284] · doi:10.1103/PhysRevD.98.083530
[32] https://www.tensorflow.org
[33] G. Aurelien, 2017 Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly Media [ISBN-10:1491962291]
[34] T. Charnock and A. Moss, 2017 Deep Recurrent Neural Networks for Supernovae Classification, https://doi.org/10.3847/2041-8213/aa603d Astrophys. J.837 L28 [1606.07442] · doi:10.3847/2041-8213/aa603d
[35] A. Mathuriya et al., CosmoFlow: Using Deep Learning to Learn the Universe at Scale, [1808.04728]
[36] R. Kessler, A. Conley, S. Jha and S. Kuhlmann, Supernova Photometric Classification Challenge, [1001.5210]
[37] A. Moss, Improved Photometric Classification of Supernovae using Deep Learning, [1810.06441]
[38] A. Moss, Accelerated Bayesian inference using deep learning, [1903.10860]
[39] P.J.E. Peebles and B. Ratra, 2003 The Cosmological Constant and Dark Energy, https://doi.org/10.1103/RevModPhys.75.559 Rev. Mod. Phys.75 559 [astro-ph/0207347] · Zbl 1205.83082 · doi:10.1103/RevModPhys.75.559
[40] M. Chevallier and D. Polarski, 2001 Accelerating universes with scaling dark matter, https://doi.org/10.1142/S0218271801000822 Int. J. Mod. Phys. D 10 213 [gr-qc/0009008] · doi:10.1142/S0218271801000822
[41] E.V. Linder, 2008 The Dynamics of Quintessence, The Quintessence of Dynamics, https://doi.org/10.1007/s10714-007-0550-z Gen. Rel. Grav.40 329 [0704.2064] · Zbl 1137.83381 · doi:10.1007/s10714-007-0550-z
[42] B.C. Paul and P. Thakur, 2013 Observational constraints on modified Chaplygin gas from cosmic growth J. Cosmol. Astropart. Phys.2013 11 052 [1306.4808]
[43] D.M. Scolnic et al., 2018 The Complete Light-curve Sample of Spectroscopically Confirmed SNe Ia from Pan-STARRS1 and Cosmological Constraints from the Combined Pantheon Sample, https://doi.org/10.3847/1538-4357/aab9bb Astrophys. J.859 101 [1710.00845] · doi:10.3847/1538-4357/aab9bb
[44] M. Ntampaka et al., The Role of Machine Learning in the Next Decade of Cosmology, [1902.10159]
[45] J. Schmelzle et al., Cosmological model discrimination with Deep Learning, [1707.05167]
[46] S. Ruder, An overview of gradient descent optimization algorithms, [1609.04747]
[47] A. Möller and T. de Boissière SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification, https://doi.org/10.1093/mnras/stz3312 Mon. Not. Roy. Astron. Soc.491 2020 4277 [1901.06384] · doi:10.1093/mnras/stz3312
[48] I. Goodfellow, Y. Bengio and A. Courville, 2016 Deep Learning, MIT Press [http://www.deeplearningbook.org] · Zbl 1373.68009
[49] W. Zaremba and I. Sutskever, Reinforcement Learning Neural Turing Machines — Revised, [1505.00521]
[50] D. Pedamonti, Comparison of non-linear activation functions for deep neural networks on MNIST classification task, [1804.02763]
[51] Y. Gal and Z. Ghahramani, A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, [1512.05287]
[52] C. Louizos and M. Welling, Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors, [1603.04733]
[53] R. Bayes, An essay toward solving a problem in the doctrine of chances, https://doi.org/10.1098/rstl.1763.0053 Phil. Trans. Roy. Soc. Lond.53 (1764) 370 · Zbl 1250.60007 · doi:10.1098/rstl.1763.0053
[54] P. Gregory Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, New York, U.S.A. 2005 · Zbl 1069.62109 · doi:10.1017/CBO9780511791277
[55] R. Trotta, 2007 Applications of Bayesian model selection to cosmological parameters, https://doi.org/10.1111/j.1365-2966.2007.11738.x Mon. Not. Roy. Astron. Soc.378 72 [astro-ph/0504022] · doi:10.1111/j.1365-2966.2007.11738.x
[56] J. Skilling, 2006 Nested Sampling for General Bayesian Computation Bayesian Annal.1 833 [http://www.mrao.cam.ac.uk/ steve/maxent2009/images/skilling.pdf] · Zbl 1332.62374 · doi:10.1214/06-BA127
[57] A.R. Liddle, P. Mukherjee, D. Parkinson and Y. Wang, 2006 Present and future evidence for evolving dark energy, https://doi.org/10.1103/PhysRevD.74.123506 Phys. Rev. D 74 123506 [astro-ph/0610126] · doi:10.1103/PhysRevD.74.123506
[58] H. Jeffreys, 1998 Theory of Probability, 3rd edition, Oxford University Press, Oxford, U.K. · JFM 65.0546.04
[59] A.R. Liddle, 2004 How many cosmological parameters?, https://doi.org/10.1111/j.1365-2966.2004.08033.x Mon. Not. Roy. Astron. Soc.351 L49 [astro-ph/0401198] · doi:10.1111/j.1365-2966.2004.08033.x
[60] Y. Gal and Z. Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, [1506.02142]
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.