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Fabian Schneider

Researcher at Heidelberg Institute for Theoretical Studies

Publications -  164
Citations -  8840

Fabian Schneider is an academic researcher from Heidelberg Institute for Theoretical Studies. The author has contributed to research in topics: Stars & Stellar evolution. The author has an hindex of 44, co-authored 164 publications receiving 7437 citations. Previous affiliations of Fabian Schneider include Technische Universität München & University of Oxford.

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Binary Interaction Dominates the Evolution of Massive Stars

TL;DR: More than 70% of all massive stars will exchange mass with a companion, leading to a binary merger in one-third of the cases, greatly exceed previous estimates and imply that binary interaction dominates the evolution of massive stars, with implications for populations ofmassive stars and their supernovae.
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The incidence of stellar mergers and mass gainers among massive stars

TL;DR: In this article, the authors show that the commonly adopted approach to minimize the effects of binaries on an observed sample by excluding systems detected as binaries through radial velocity campaigns can be counterproductive, as it substantially enhances the relative incidence of mergers and binary products in the non-radial velocity variable sample.
Proceedings ArticleDOI

Understanding online social network usage from a network perspective

TL;DR: This paper characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions.
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An excess of massive stars in the local 30 Doradus starburst.

TL;DR: In this article, the authors determined the recent formation history and the initial mass function (IMF) of massive stars in 30 Doradus on the basis of spectroscopic observations of 247 stars more massive than 15 solar masses.
Journal ArticleDOI

NFV and SDN—Key Technology Enablers for 5G Networks

TL;DR: This paper provides an overview of Network Function Virtualization and Software Defined Networking technologies and how the two technologies complement each other and how they are expected to drive the networks of near future.