Heinz Andernach, Fritz Zwicky

English and Spanish translations are provided for Fritz Zwicky's seminal article on "The Redshift of Extragalactic Nebulae", published in German in Helvetica Physica Acta in 1933 <https://www.e-periodica.ch/digbib/view?pid=hpa-001:1933:6#112>. This paper is usually cited as the first evidence for dark matter ("dunkle Materie", not "missing matter"). Zwicky's conclusion is based on the velocity dispersion of only seven galaxies in the Coma cluster of galaxies. Now, 84 years later, with 1000+ radial velocities measured for Coma cluster members, Coma's velocity dispersion is very close to that found by Zwicky. The translation is as literal as possible, and annotations on the translation of certain terms are given. Doubts on the meaning of the original phrasing are given in square brackets at a few places where they occur. The pdf version of my English translation was kindly prepared by Cren Frayer at the NASA/IPAC Extragalactic Database (NED), also available at NED's Level5 repository since June 2017 (<this http URL>). A Spanish translation, without figures, prepared by myself with the help of Martha Margarita L\'opez Guti\'errrez is appended within the same pdf file. As translator I take responsibilty for the quality of the translation.

Anatoly Klypin, Francisco Prada, Johan Comparat

Numerical simulations play a key important role in modern cosmology. Examples are plenty including the cosmic web - large scale structure of the distribution of galaxies in space - which was first observed in N-body simulations and later discovered in observations. The cuspy dark matter halo profiles, the overabundance of satellites, the Too-Big-Too-Fail problem are other examples of theoretical predictions that have a dramatic impact on recent developments in cosmology and galaxy formation. Large observational surveys such as e.g. SDSS, Euclid, and LSST are intimately connected with extensive cosmological simulations that provide statistical errors and tests for systematics. Accurate predictions for baryonic acoustic oscillations and redshift space distortions from high-resolution and large-volume cosmological simulations are required for interpretation of these large-scale galaxy/qso surveys. However, most of the results from extensive computer simulations, that would be greatly beneficial if publicly available, are still in hands of few research groups. Even when the simulation data is available, sharing vast amounts of data can be overwhelming. We argue that there is an effective and simple path to expand the data access and dissemination of numerous results from different cosmological models. Here we demonstrate that public access can be effectively provided with relatively modest resources. Among different results, we release for the astronomical community terabytes of raw data of th popular Bolshoi and MultiDark simulations. We also provide numerous results that are focused on mimicking observational data and galaxy surveys for major projects. Skies and Universes is a community effort: data are produced and shared by many research groups. We offer to other cosmologists and astronomers to host their data products in the skiesanduniverses.org space.

Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in three dimensions. Information about galaxy locations is typically summarized in a "single" function of scale, such as the galaxy correlation function or power-spectrum. We show that it is possible to estimate these cosmological parameters directly from the distribution of matter. This paper presents the application of deep 3D convolutional networks to volumetric representation of dark-matter simulations as well as the results obtained using a recently proposed distribution regression framework, showing that machine learning techniques are comparable to, and can sometimes outperform, maximum-likelihood point estimates using "cosmological models". This opens the way to estimating the parameters of our Universe with higher accuracy.

T. M. C. Abbott, F. B. Abdalla, J. Annis, K. Bechtol, B. A. Benson, R. A. Bernstein, G. M. Bernstein, E. Bertin, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. L. Chang, T. M. Crawford, C. E. Cunha, C. B. D'Andrea, L. N. da Costa, C. Davis, S. Desai, H. T. Diehl, J. P. Dietrich, P. Doel, A. Drlica-Wagner, A. E. Evrard, E. Fernandez, B. Flaugher, J. Frieman, J. Garcia-Bellido, E. Gaztanaga, D. W. Gerdes, T. Giannantonio, D. Gruen, R. A. Gruendl, J. Gschwend, G. Gutierrez, W. G. Hartley, J. W. Henning, K. Honscheid, B. Hoyle, B. Jain, D. J. James, M. Jarvis, T. Jeltema, M. D. Johnson, M. W. G. Johnson, E. Krause, K. Kuehn, S. Kuhlmann, N. Kuropatkin, O. Lahav, A. R. Liddle, M. Lima, H. Lin, M. A. G. Maia, A. Manzotti, M. March, et al. (32 additional authors not shown)

We combine Dark Energy Survey Year 1 clustering and weak lensing data with Baryon Acoustic Oscillations (BAO) and Big Bang Nucleosynthesis (BBN) experiments to constrain the Hubble constant. Assuming a flat $\Lambda$CDM model with minimal neutrino mass ($\sum m_\nu = 0.06$ eV) we find $H_0=67.2^{+1.2}_{-1.0}$ km/s/Mpc (68% CL). This result is completely independent of Hubble constant measurements based on the distance ladder, Cosmic Microwave Background (CMB) anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: a) have no shared observational systematics; and b) each constrain the Hubble constant with a few percent level precision. We compare these five independent measurements, and find that, as a set, the differences between them are significant at the $2.1\sigma$ level ($\chi^2/dof=20.1/11$, probability to exceed=4%). This difference is low enough that we consider the data sets statistically consistent with each other. The best fit Hubble constant obtained by combining all five data sets is $H_0 = 69.1^{+0.4}_{-0.6}$ km/s/Mpc.

I. Horvath, B.G. Toth, J. Hakkila, L.V. Toth, I.I. Racz, L.G. Balazs, S. Pinter, Z. Bagoly

GRB170817A associated with the LIGO-Virgo GW170817 neutron-star merger event lacks the short duration and hard spectrum expected of a Short gamma-ray burst (GRB) from long-standing classification models. Classifying this burst correctly requires comparison with other GRBs detected by Fermi GBM. The Fermi GBM catalog provides a large database with many measured variables that can be used to explore gamma-ray burst classification. Our aim is to classify Fermi GRBs and test whether or not GRB170817A belongs - as suggested - to the Short GRB class. We use statistical techniques to look for clustering in a sample of 1298 gamma-ray bursts described by duration and spectral hardness. Classification of the detected bursts shows that GRB170817A belongs to the Intermediate, rather than the Short GRB class. GRB170817A does not appear to fit into a simple phenomenological classification scheme. Recently, it was suggested that GRB170817A/GW170817 may be a member of a new Short GRB sub-class. Here we show that this proposed sub-class is the Intermediate one, as discovered earlier.