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Sebastian Steinlechner

Researcher at Maastricht University

Publications -  303
Citations -  77646

Sebastian Steinlechner is an academic researcher from Maastricht University. The author has contributed to research in topics: Gravitational wave & LIGO. The author has an hindex of 93, co-authored 299 publications receiving 62949 citations. Previous affiliations of Sebastian Steinlechner include Glasgow Caledonian University & Max Planck Society.

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Improved upper limits on the stochastic gravitational-wave background from 2009-2010 LIGO and Virgo data

J. Aasi, +865 more
TL;DR: A search for the stochastic background with the latest data from the LIGO and Virgo detectors shows no evidence of a stochastically gravitational-wave signal, and the limits in these four bands are the lowest direct measurements to date on the stoChastic background.
Journal ArticleDOI

Search for gravitational waves from binary black hole inspiral, merger and ringdown in LIGO-Virgo Data from 2009-2010

J. Aasi, +913 more
- 23 Jan 2013 - 
TL;DR: In this article, the authors reported a search for gravitational waves from the inspiral, merger and ringdown of binary black holes with total mass between 25 and 100 solar masses, in data taken at the LIGO and Virgo observatories between July 7, 2009 and October 20, 2010.

Search for gravitational waves from binary black hole inspiral, merger, and ringdown

J. Abadie, +720 more
Journal ArticleDOI

Implementation and testing of the first prompt search for gravitational wave transients with electromagnetic counterparts

J. Abadie, +815 more
TL;DR: In this paper, a low-latency analysis pipeline was used to identify and localize GW event candidates and to request images of targeted sky locations, where a catalog of nearby galaxies and Milky Way globular clusters were used to select the most promising sky positions to be imaged.
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Model Comparison from LIGO-Virgo Data on GW170817's Binary Components and Consequences for the Merger Remnant

B. P. Abbott, +1280 more
TL;DR: In this paper, the authors perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state, and find that all scenarios from prompt collapse to long-lived or even stable remnants are possible.