35 citations · 50 across the 4 of their papers we have counts for
4 papers
A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton parameter maps
Daniel de Andres, Weiguang Cui, Florian Ruppin +8
Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological para…
Mass Estimation of Planck Galaxy Clusters using Deep Learning
Daniel de Andres, Weiguang Cui, Florian Ruppin +6
Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some…
Constructive -module Theory with \textsc{Singular}
Daniel Andres, Michael Brickenstein, Viktor Levandovskyy +2
We overview numerous algorithms in computational -module theory together with the theoretical background as well as the implementation in the computer algebra system \textsc{Sin…
Effective Methods for the Computation of Bernstein-Sato polynomials for Hypersurfaces and Affine Varieties
Daniel Andres, Viktor Levandovskyy, Jorge Martín-Morales
This paper is the widely extended version of the publication, appeared in Proceedings of ISSAC'2009 conference \citep*{ALM09}. We discuss more details on proofs, present new algori…