[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] |