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Ivan Amos Cali

Researcher at Massachusetts Institute of Technology

Publications -  1389
Citations -  95141

Ivan Amos Cali is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Large Hadron Collider & Lepton. The author has an hindex of 131, co-authored 1249 publications receiving 85657 citations. Previous affiliations of Ivan Amos Cali include University of Rochester & University of Florence.

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Decomposing transverse momentum balance contributions for quenched jets in PbPb collisions at √sNN = 2.76 TeV

Vardan Khachatryan, +2324 more
- 09 Nov 2016 - 
TL;DR: In this article, the angular distribution of summed charged-particle transverse momenta (pt) with respect to both the leading and subleading jet axes in high-pt dijet events is studied.
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Search for supersymmetry with photons in pp collisions at √s=8 TeV

Vardan Khachatryan, +2321 more
- 19 Oct 2015 - 
TL;DR: In this article, two searches for physics beyond the standard model in events containing photons are presented, and the results are interpreted in the context of general gauge-mediated supersymmetry, with the next-to-lightest supersymmetric particle either a bino- or wino-like neutralino.
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Commissioning of the CMS High-Level Trigger with Cosmic Rays

S. Chatrchyan, +2464 more
- 19 Mar 2010 - 
TL;DR: An overview of the CMS High-Level Trigger is given and its commissioning using cosmic rays is focused on, with the average time taken for the HLT selection and its dependence on detector and operating conditions presented.
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Search for a W' or Techni-ρ decaying into WZ in pp collisions at √s=7TeV

S. Chatrchyan, +2255 more
- 02 Oct 2012 - 
TL;DR: In this article, a search is performed in pp collisions at 7 TeV for exotic particles decaying via WZ to final states with electrons and muons, and upper bounds at 95% confidence level are set on the production cross section of the W' boson described by the sequential standard model.
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A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.

Albert M. Sirunyan, +2276 more
- 30 Oct 2020 - 
TL;DR: A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet, to improve the sensitivity of analyses that make use of b jets in the final state.