Machine learning cosmological structure formation

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matt ...

[1]  R. Sheth,et al.  Peaks theory and the excursion set approach , 2012, 1206.3506.

[2]  Alan L. Yuille,et al.  Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief , 2011, NeuroImage.

[3]  C. A. Oxborrow,et al.  Planck2015 results , 2015, Astronomy & Astrophysics.

[4]  J. Hilden.  The Area under the ROC Curve and Its Competitors , 1991, Medical decision making : an international journal of the Society for Medical Decision Making.

[5]  H. M. P. Couchman,et al.  The mass function of dark matter haloes , 2000, astro-ph/0005260.

[6]  G. Lake,et al.  Evolution of the mass function of dark matter haloes , 2003, astro-ph/0301270.

[7]  G. Chapman,et al.  [Medical decision making]. , 1976, Lakartidningen.

[8]  Robert J. Brunner,et al.  Machine Learning and Cosmological Simulations I: Semi-Analytical Models , 2015, 1510.06402.

[9]  O. Lahav,et al.  PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING , 2016, 1603.00882.

[10]  Marc Toussaint,et al.  Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.

[11]  A. Farahi,et al.  Excursion set theory for correlated random walks , 2013, 1303.0337.

[12]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[13]  A. Knebe,et al.  Ahf: AMIGA'S HALO FINDER , 2009, 0904.3662.

[14]  Ravi K. Sheth Giuseppe Tormen.  Large scale bias and the peak background split , 1999 .

[15]  A. Ludlow,et al.  The formation of cold dark matter haloes – II. Collapse time and tides , 2014, 1405.7367.

[16]  A. Szalay,et al.  The statistics of peaks of Gaussian random fields , 1986 .

[17]  R. Sheth,et al.  Ellipsoidal collapse and an improved model for the number and spatial distribution of dark matter haloes , 1999, astro-ph/9907024.

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  V. Springel,et al.  GADGET: a code for collisionless and gasdynamical cosmological simulations , 2000, astro-ph/0003162.

[20]  J. Bond,et al.  The Peak-Patch Picture of Cosmic Catalogs. I. Algorithms , 1996 .

[21]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[22]  William H. Press,et al.  Formation of Galaxies and Clusters of Galaxies by Self-Similar Gravitational Condensation , 1974 .

[23]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[24]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[25]  Canada.,et al.  Data Mining and Machine Learning in Astronomy , 2009, 0906.2173.

[26]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[27]  Risa H. Wechsler,et al.  Modeling the Impact of Baryons on Subhalo Populations with Machine Learning , 2017, The Astrophysical Journal.

[28]  Bruce A. Bassett,et al.  Painting galaxies into dark matter haloes using machine learning , 2017, 1712.03255.

[29]  M. Halpern,et al.  FIVE-YEAR WILKINSON MICROWAVE ANISOTROPY PROBE OBSERVATIONS: LIKELIHOODS AND PARAMETERS FROM THE WMAP DATA , 2008, 0803.0586.

[30]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[31]  Michael S. Warren,et al.  Toward a Halo Mass Function for Precision Cosmology: The Limits of Universality , 2008, 0803.2706.

[32]  M. Maggiore,et al.  THE HALO MASS FUNCTION FROM EXCURSION SET THEORY. I. GAUSSIAN FLUCTUATIONS WITH NON-MARKOVIAN DEPENDENCE ON THE SMOOTHING SCALE , 2009, 0903.1249.

[33]  Mark Vogelsberger,et al.  Numerical simulations of the dark universe: State of the art and the next decade , 2012, 1209.5745.

[34]  Stuart P. D. Gill,et al.  The evolution of substructure - I. A new identification method , 2004, astro-ph/0404258.

[35]  V. Springel.  The Cosmological simulation code GADGET-2 , 2005, astro-ph/0505010.

[36]  J. R. Bond,et al.  Excursion set mass functions for hierarchical Gaussian fluctuations , 1991 .