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C. Di Donato

Researcher at University of Naples Federico II

Publications -  637
Citations -  42710

C. Di Donato is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 92, co-authored 503 publications receiving 37972 citations.

Papers
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Journal ArticleDOI

η−η′mixing: From electromagnetic transitions to weak decays of charm and beauty hadrons

C. Di Donato, +2 more
- 20 Jan 2012 - 
TL;DR: In this paper, it has been realized for a long time that knowing the wave functions in terms of quark and gluon components probes our understanding of nonperturbative QCD dynamics.
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Search for direct top squark pair production in final states with two tau leptons in pp collisions at √s = 8 TeV with the ATLAS detector

Georges Aad, +2849 more
- 16 Feb 2016 - 
TL;DR: In this article, a search for direct pair production of the supersymmetric partner of the top quark, decaying via a scalar tau to a nearly massless gravitino, has been performed using 20 fb(-1) of proton-proton collision data at root s = 8 TeV.
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Study of the decay phi -> f(0)(980)gamma -> pi (+) pi(-) gamma with the KLOE detector

F. Ambrosino, +87 more
- 09 Mar 2006 - 
TL;DR: In this article, the spectrum of π + π − invariant mass in a sample of 6.7 × 10 5 e + e − → π+ π− γ events with the photon at large polar angle ( θ γ > 45 ° ) at a centre of mass energy s around the ϕ mass.
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Search for a Charged Higgs Boson Produced in the Vector-boson Fusion Mode with Decay H ± →W ± Z using pp Collisions at √s=8 TeV with the ATLAS Experiment

Georges Aad, +2811 more
- 09 Jun 2015 - 
TL;DR: A search for a charged Higgs boson, H(±), decaying to a W(±) boson and a Z boson is presented and the limits are compared with predictions from the Georgi-Machacek Higgs triplet model.
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A neural network clustering algorithm for the ATLAS silicon pixel detector

Georges Aad, +2911 more
- 01 Sep 2014 - 
TL;DR: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented, reducing the number of clusters shared between tracks in highly energetic jets by up to a factor of three.