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Hermann Kolanoski

Researcher at Humboldt University of Berlin

Publications -  1314
Citations -  102570

Hermann Kolanoski is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Large Hadron Collider & Neutrino. The author has an hindex of 145, co-authored 1279 publications receiving 96152 citations. Previous affiliations of Hermann Kolanoski include Uppsala University & University of California, Davis.

Papers
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Journal ArticleDOI

Simultaneous measurements of the tt¯, W+W−, and Z/γ∗→ττ production cross-sections in pp collisions at s√=7 TeV with the ATLAS detector

Georges Aad, +2885 more
- 06 Mar 2015 - 
TL;DR: Simultaneous measurements of the t (t) over bar, W+W-, and Z/gamma* tau tau production cross-sections using an integrated luminosity of 4.6 fb(-1) of pp collisions at root s = 7 TeV collected as mentioned in this paper.
Journal ArticleDOI

First observation of γγ → ωω

H. Albrecht, +78 more
- 24 Sep 1987 - 
TL;DR: In this article, the ARGUS detector at the e+e− storage ring DORIS II at DESY has been used for the first time to detect ω mesons.
Journal ArticleDOI

Experimental study of hard photon radiation processes at HERA

T. Ahmed, +410 more
- 01 Jan 1995 - 
TL;DR: In this paper, the authors present an experimental study of theep→eγ+p andep→ eγ+X processes using data recorded by the H1 detector in 1993 at the electron-proton collider HERA.

Search for tb Resonances in Proton-Proton Collisions at √s=7 TeV with the ATLAS Detector

Georges Aad, +3013 more
TL;DR: This Letter presents a search for tb resonances in 1.04 fb(-1) of LHC proton-proton collision data collected by the ATLAS detector at a center-of-mass energy of 7 TeV, exploiting the shape of the tb invariant mass distribution compared to the expected standard model backgrounds.
Book ChapterDOI

Application of Artificial Neural Networks in Particle Physics

Hermann Kolanoski
TL;DR: Neural net algorithms like the Hopfield model have been applied for pattern recognition in tracking detectors and self-organizing networks for the recognition of features in large data samples are discussed.