Peter Sheldon
· ProfessorUniversity of Illinois Urbana-Champaign · Advertising
Active 1983–2024
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About
Peter Sheldon is a Senior Lecturer of Advertising at the College of Media, University of Illinois at Urbana-Champaign. He holds a BA in Advertising from Michigan State University and an MS in Advertising from the University of Texas at Austin. Sheldon began his creative career as a copywriter in Chicago and advanced to become a creative director in Richmond, Virginia. His creative work has been recognized by prestigious awards such as the Clio Awards, The One Show, Communication Arts magazine, and the Chicago and Richmond Addys. Currently, Sheldon teaches both introductory and advanced courses in creativity in advertising. His expertise includes content creation and creative concepts. He has served as a consultant for the AAF creative team in the National Scholastic Advertising Competition. Sheldon has been honored with several teaching awards, including the Association for Education in Journalism and Mass Communications Advertising Division’s Distinguished Teaching Award in 2014, the University of Illinois Campus Award for Excellence in Undergraduate Education in 2003 and 2010, and multiple Department of Advertising Excellence in Teaching awards between 2005 and 2011.
Research topics
- Physics
- Particle physics
- Nuclear physics
Selected publications
The European Physical Journal C · 2020 · 92 citations
Abstract Normalised multi-differential cross sections for top quark pair ( $$\hbox {t}{\bar{\hbox {t}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mtext>t</mml:mtext><mml:mover><mml:mrow><mml:mtext>t</mml:mtext></mml:mrow><mml:mrow><mml:mo>¯</mml:mo></mml:mrow></mml:mover></mml:mrow></mml:math> ) production are measured in proton-proton collisions at a centre-of-mass energy of 13 $$\,{\text {TeV}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow…
Journal of High Energy Physics · 2020 · 81 citations
A bstract A search is presented for additional scalar (H) or pseudoscalar (A) Higgs bosons decaying to a top quark pair in proton-proton collisions at a center-of-mass energy of 13 TeV. The data set analyzed corresponds to an integrated luminosity of 35.9 fb − 1 collected by the CMS experiment at the LHC. Final states with one or two charged leptons are considered. The invariant mass of the reconstructed top quark pair system and variables that are sensitive to the spin of the particles decaying…
Journal of High Energy Physics · 2023 · 69 citations
A bstract Three searches are presented for signatures of physics beyond the standard model (SM) in ττ final states in proton-proton collisions at the LHC, using a data sample collected with the CMS detector at $$ \sqrt{s} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:math> = 13 TeV, corresponding to an integrated luminosity of 138 fb − 1 . Upper limits at 95% confidence level (CL) are set on the products of the branching fraction f…
Journal of High Energy Physics · 2020 · 68 citations
A bstract A search for direct top squark pair production is presented. The search is based on proton-proton collision data at a center-of-mass energy of 13 TeV recorded by the CMS experiment at the LHC during 2016, 2017, and 2018, corresponding to an integrated luminosity of 137 fb −1 . The search is carried out using events with a single isolated electron or muon, multiple jets, and large transverse momentum imbalance. The observed data are consistent with the expectations from standard model p…
Identification of hadronic tau lepton decays using a deep neural network
Journal of Instrumentation · 2022 · 51 citations
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance co…
Recent grants
NSF · $980k · 2012–2016
NSF · $990k · 2015–2018
NSF · $1.5M · 2018–2021
Frequent coauthors
- 1326 shared
M. Lethuillier
Institute of Nuclear Physics of Lyon
- 1287 shared
J. Andreä
Institut Pluridisciplinaire Hubert Curien
- 1286 shared
D. Blöch
Institut Pluridisciplinaire Hubert Curien
- 1275 shared
C. Collard
Institut Pluridisciplinaire Hubert Curien
- 1246 shared
M. Titov
Institut de Recherche sur les Lois Fondamentales de l'Univers
- 1237 shared
G. Hamel de Monchenault
Université Paris-Saclay
- 1225 shared
E. Conte
Institut Pluridisciplinaire Hubert Curien
- 1215 shared
S. Perriès
Institute of Nuclear Physics of Lyon
Labs
College of MediaPI
Education
B.A., Advertising
Michigan State University
M.S., Advertising
University of Texas at Austin
Awards & honors
- Association for Education in Journalism and Mass Communicati…
- University of Illinois Campus Award for Excellence in Underg…
- Department of Advertising Excellence in Teaching award (2005…
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