Tom Shanks, Lucy Hogarth, Nigel Metcalfe, Durham University, England)

Riess et al (2018c) have claimed there exist seven problems in the analyses presented by Shanks et al (2018) where we argue that there is enough uncertainty in Cepheid distances and local peculiar velocity fields to explain the current tension in $H_0$. Here, we take each of the Riess et al (2018c) points in turn and suggest that either they do not apply or that the necessary caveats are already made by Shanks et al (2018). We conclude that the main point to be inferred from our analyses still stands which is that previous claims by Riess et al (2018b) that Gaia parallaxes confirm their Cepheid scale are, at best, premature in advance of further improvements in the Gaia astrometric solution.

M. Ntampaka, J. ZuHone, D. Eisenstein, D. Nagai, A. Vikhlinin, L. Hernquist, F. Marinacci, D. Nelson, R. Pakmor, A. Pillepich, P. Torrey, M. Vogelsberger

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7,896 Chandra X-ray mock observations, which are based on 329 massive clusters from the IllustrisTNG simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (-0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15-18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.

Suman Sarkar, Biswajit Pandey, Rishi Khatri

We analyze two volume limited galaxy samples from the SDSS photometric and spectroscopic data to test the isotropy in the local Universe. We use information entropy to quantify the global anisotropy in the galaxy distribution at different length scales and find that the galaxy distribution is highly anisotropic on small scales. The observed anisotropy diminishes with increasing length scales and nearly plateaus out beyond a length scale of 200 Mpc/h in both the datasets. We compare these anisotropies with those predicted by the mock catalogues from the N-body simulations of the Lambda CDM model and find an excellent agreement with the observations. We find a small residual anisotropy on large scales which decays in a way that is consistent with the linear perturbation theory. The slopes of the observed anisotropy converge to the slopes predicted by the linear theory beyond a length scale of ~ 200 Mpc/h indicating a transition to isotropy. We separately compare the anisotropies observed across the different parts of the sky and find no evidence for a preferred direction in the galaxy distribution.