Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks

Autor: Nesar Ramachandra, Yuyu Wang, Edgar M. Salazar-Canizales, Richard Watkins, Klaus Dolag, Hume A. Feldman

The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dff24c5657daddd7bf59d9a1c930c341

Easily Interpretable Bulk Flows: Continuing Tension with the Standard Cosmological Model

Autor: Sarah Peery, Richard Watkins, Hume A. Feldman

We present an improved Minimal Variance (MV) method for using a radial peculiar velocity sample to estimate the average of the three-dimensional velocity field over a spherical volume, which leads to an easily interpretable bulk flow measurement. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af9dfba9c082061865530d07ec3679c9

The Aspen-Amsterdam void finder comparison project

Autor: Riccardo Brunino, Lorne Nelson, Michael S. Vogeley, Volker Mueller, J. M. Colberg, Hume A. Feldman, Oliver Hahn, Erwin Platen, Mark C. Neyrinck, Caroline Foster, Sergei F. Shandarin, Rien van de Weygaert, F. Hoyle, Cristiano Porciani, Stefan Gottloeber, Frazer R. Pearce, Manolis Plionis, A. P. Fairall, Spyros Basilakos

Publikováno v: Monthly Notices of the Royal Astronomical Society, 387 (2)
Monthly Notices of the Royal Astronomical Society, 387(2), 933-944. Oxford University Press

Despite a history that dates back at least a quarter of a century studies of voids in the large--scale structure of the Universe are bedevilled by a major problem: there exist a large number of quite different void--finding algorithms, a fact that ha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcf994d8c0c60b44b22e7e7de573eee1
http://doc.rero.ch/record/298665/files/mnras0387-0933.pdf

pkann – II. A non-linear matter power spectrum interpolator developed using artificial neural networks

Autor: Shankar Agarwal, Shaun A. Thomas, Hume A. Feldman, Filipe B. Abdalla, Ofer Lahav

Publikováno v: Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2014, 439 (2), pp.2102-2121. ⟨10.1093/mnras/stu090⟩

In this paper we introduce PkANN, a freely available software package for interpolating the non-linear matter power spectrum, constructed using Artificial Neural Networks (ANNs). Previously, using Halofit to calculate matter power spectrum, we demons

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f3355cd2145a5a8b4c2535f6293b63c
https://doi.org/10.1093/mnras/stu090

Testing the minimum variance method for estimating large-scale velocity moments

Autor: Shankar Agarwal, Richard Watkins, Hume A. Feldman

Publikováno v: Monthly Notices of the Royal Astronomical Society. 424:2667-2675

The estimation and analysis of large-scale bulk flow moments of peculiar velocity surveys is complicated by non-spherical survey geometry, the non-uniform sampling of the matter velocity field by the survey objects and the typically large measurement

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e46b2f69799c43450a83050c42336b2c
https://doi.org/10.1111/j.1365-2966.2012.21345.x