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Benjamin Nachman

Researcher at Lawrence Berkeley National Laboratory

Publications -  147
Citations -  11302

Benjamin Nachman is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 57, co-authored 147 publications receiving 9558 citations. Previous affiliations of Benjamin Nachman include Stanford University & SLAC National Accelerator Laboratory.

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Journal ArticleDOI

Performance of the ATLAS trigger system in 2015

Morad Aaboud, +2848 more
- 18 May 2017 - 
TL;DR: This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton–proton collision data.
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Muon reconstruction performance of the ATLAS detector in proton–proton collision data at √ s =13 TeV

Georges Aad, +2831 more
- 23 May 2016 - 
TL;DR: In this article, the performance of the ATLAS muon identification and reconstruction using the first LHC dataset recorded at s√ = 13 TeV in 2015 was evaluated using the Monte Carlo simulations.
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Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Georges Aad, +2891 more
- 24 Jul 2017 - 
TL;DR: Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS and is exploited to apply a local energy calibration and corrections depending on the nature of the cluster.
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Search for dark matter and other new phenomena in events with an energetic jet and large missing transverse momentum using the ATLAS detector

Morad Aaboud, +2957 more
- 25 Jan 2018 - 
TL;DR: In this paper, a search for new phenomena in final states with an energetic jet and large missing transverse momentum is reported, and the results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.
Journal ArticleDOI

Jet-images — deep learning edition

Luke de Oliveira, +4 more
- 13 Jul 2016 - 
TL;DR: This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.