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Pierre-Hugues Beauchemin

Researcher at Tufts University

Publications -  881
Citations -  77594

Pierre-Hugues Beauchemin is an academic researcher from Tufts University. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 124, co-authored 766 publications receiving 70602 citations. Previous affiliations of Pierre-Hugues Beauchemin include Université de Montréal & CERN.

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Erratum to: Search for supersymmetry in final states with two same-sign or three leptons and jets using 36 fb−1 of s$ \sqrt{s} $ = 13 TeV pp collision data with the ATLAS detector

Morad Aaboud, +2934 more
TL;DR: In this article, a search for supersymmetry in final states with two same-sign or three leptons and jets using 36 fb-1 of √s = 13 TeV pp collision data with the ATLAS detector is described.
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Corrigendum: A search for an excited muon decaying to a muon and two jets in pp collisions at $\sqrt{s}=8\,\mathrm{TeV}$ with the ATLAS detector

Georges Aad, +2823 more
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Phenomenological Constraints on Extra-Dimensional Scalars

TL;DR: In this article, the authors examined whether the ATLAS detector has sensitivity to extra-dimensional scalars (as opposed to components of higher-dimensional tensors which look like 4D scalars), in scenarios having the extradimensional Planck scale in the TeV range and $n \ge 2$ nonwarped extra dimensions.
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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

Georges Aad, +2782 more
TL;DR: In this paper , a new method to account for these additional interactions in the simulation chain is described, instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the interactions are presampled, independent of the hard scatter, and stored as combined events.
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SEARCHES FOR NEW PHYSICS WITH JETS-PLUS-$E\hspace{-6.2pt}/_T$ SIGNATURES USING DATA-DRIVEN BACKGROUND ESTIMATES

TL;DR: In this article, the results of two searches with such signatures are presented and the benefits of performing these searches in a model-independent way are discussed and data-driven techniques used to estimate Standard Model backgrounds are described in detail.