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Cristina Galea

Researcher at Radboud University Nijmegen

Publications -  1005
Citations -  82194

Cristina Galea is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 128, co-authored 1000 publications receiving 75663 citations. Previous affiliations of Cristina Galea include Paris Diderot University & West University of Timișoara.

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Studies on the response of a water-Cherenkov detector of the Pierre Auger Observatory to atmospheric muons using an RPC hodoscope

A. Aab, +376 more
TL;DR: In this article, a study of the response of a WCD of the Pierre Auger Observatory to atmospheric muons performed with a hodoscope made of resistive plate chambers (RPCs) is presented.
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Calibration of the underground muon detector of the Pierre Auger Observatory

A. Aab, +371 more
TL;DR: In this paper, the authors present an end-to-end calibration of the muon detector modules: first, the SiPMs are calibrated by means of the binary channel, and then, the ADC channel is calibrated using atmospheric muons, detected in parallel to the shower data acquisition.
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Measurement of τ polarisation in Z/ γ∗→ ττ decays in proton–proton collisions at √s=8 TeV with the ATLAS detector

Morad Aaboud, +2895 more
TL;DR: In this paper, the polarisation of tau leptons produced in Z/gamma* -> tau tau decays was measured with a dataset of proton-proton collisions at root s = 8 TeV.
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Search for particles decaying into a Z boson and a photon in p over(p, ̄) collisions at sqrt(s) = 1.96 TeV

V. M. Abazov, +578 more
- 26 Oct 2006 - 
TL;DR: The first search for a new particle X produced in p{bar p} collisions at {radical}s = 1.96 TeV and subsequently decaying to Z{gamma] was performed in this paper.
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Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

A. Aab, +370 more
TL;DR: In this article, the authors presented a method aimed at extracting the muon component of the time traces registered with each individual detector of the surface detector using recurrent neural networks and derived the performances of the method by training the neural network on simulations, in which the muons and the electromagnetic components of the traces are known.