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Institution

Moscow Institute of Physics and Technology

EducationDolgoprudnyy, Russia
About: Moscow Institute of Physics and Technology is a education organization based out in Dolgoprudnyy, Russia. It is known for research contribution in the topics: Laser & Large Hadron Collider. The organization has 8594 authors who have published 16968 publications receiving 246551 citations. The organization is also known as: MIPT & Moscow Institute of Physics and Technology (State University).


Papers
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Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Peter Davison2, Samuel Webb3  +2918 moreInstitutions (81)
TL;DR: In this paper, the ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to dielectron or dimuon final states.
Abstract: The ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to dielectron or dimuon final states. Results are presented from an analysis of proton-proton (pp) collisions at a center-of-mass energy of 8 TeV corresponding to an integrated luminosity of 20.3 fb(-1) in the dimuon channel. A narrow resonance with Standard Model Z couplings to fermions is excluded at 95% confidence level for masses less than 2.79 TeV in the dielectron channel, 2.53 TeV in the dimuon channel, and 2.90 TeV in the two channels combined. Limits on other model interpretations are also presented, including a grand-unification model based on the E-6 gauge group, Z* bosons, minimal Z' models, a spin-2 graviton excitation from Randall-Sundrum models, quantum black holes, and a minimal walking technicolor model with a composite Higgs boson.

364 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, Ovsat Abdinov4  +2812 moreInstitutions (207)
TL;DR: In this paper, an independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b tagging algorithm used in the online trigger are also presented.
Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.

362 citations

Journal ArticleDOI
TL;DR: In this article, the evolution of large atomic clusters exposed to a super-intense ultrashort laser pulse is considered, and the optimal conditions are analyzed for efficient absorption of laser radiation by large clusters.

360 citations

Journal ArticleDOI
Morad Aaboud1, Georges Aad2, Brad Abbott3, Ovsat Abdinov4  +2954 moreInstitutions (225)
TL;DR: In this paper, a search for new phenomena in final states with an energetic jet and large missing transverse momentum is reported, and the results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.
Abstract: Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 36.1 fb−1 at a centre-of-mass energy of 13 TeV collected in 2015 and 2016 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons (e or μ). Several signal regions are considered with increasing requirements on the missing transverse momentum above 250 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.

358 citations

Proceedings ArticleDOI
12 Dec 2009
TL;DR: This paper presents ThreadSanitizer -- a dynamic detector of data races, and introduces what is called dynamic annotations -- a sort of race detection API that allows a user to inform the detector about any tricky synchronization in the user program.
Abstract: Data races are a particularly unpleasant kind of threading bugs. They are hard to find and reproduce -- you may not observe a bug during the entire testing cycle and will only see it in production as rare unexplainable failures. This paper presents ThreadSanitizer -- a dynamic detector of data races. We describe the hybrid algorithm (based on happens-before and locksets) used in the detector. We introduce what we call dynamic annotations -- a sort of race detection API that allows a user to inform the detector about any tricky synchronization in the user program. Various practical aspects of using ThreadSanitizer for testing multithreaded C++ code at Google are also discussed.

352 citations


Authors

Showing all 8797 results

NameH-indexPapersCitations
Dominique Pallin132113188668
Vladimir N. Uversky13195975342
Lee Sawyer130134088419
Dmitry Novikov12734883093
Simon Lin12675469084
Zeno Dixon Greenwood126100277347
Christian Ohm12687369771
Alexey Myagkov10958645630
Stanislav Babak10730866226
Alexander Zaitsev10345348690
Vladimir Popov102103050257
Alexander Vinogradov9641040879
Gueorgui Chelkov9332141816
Igor Pshenichnov8336222699
Vladimir Popov8337026390
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202368
2022238
20211,774
20202,246
20192,112
20181,902