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Tariq Aziz

Researcher at Tata Institute of Fundamental Research

Publications -  1808
Citations -  107296

Tariq Aziz is an academic researcher from Tata Institute of Fundamental Research. The author has contributed to research in topics: Large Hadron Collider & Lepton. The author has an hindex of 138, co-authored 1646 publications receiving 96586 citations. Previous affiliations of Tariq Aziz include Nagoya University & University of Florence.

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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.
Journal ArticleDOI

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.

Search for Higgs Boson Pair Production in Events with Two Bottom Quarks and Two Tau Leptons in Proton–proton Collisions at √s = 13 TeV

Albert M. Sirunyan, +2213 more
TL;DR: Doser et al. as mentioned in this paper presented a survey of the state of the art in the field of cyber-physical cyber-warrior networks and proposed a method to improve it.
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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.