Institution
Moscow Institute of Physics and Technology
Education•Dolgoprudnyy, 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).
Topics: Laser, Large Hadron Collider, Electron, Plasma, Magnetic field
Papers published on a yearly basis
Papers
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TL;DR: In this article, the process e(+)e(-) -> pi(+)pi(-) J/psi at a center-of-mass energy of 4.260 GeV using a 525 pb(-1) data sample collected with the BESIII detector operating at the Beijing Electron Positron Collider was studied.
Abstract: We study the process e(+)e(-) -> pi(+)pi(-) J/psi at a center-of-mass energy of 4.260 GeV using a 525 pb(-1) data sample collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross section is measured to be (62.9 +/- 1.9 +/- 3.7) pb, consistent with the production of the Y(4260). We observe a structure at around 3.9 GeV/c(2) in the pi(+/-) J/psi mass spectrum, which we refer to as the Z(c)(3900). If interpreted as a new particle, it is unusual in that it carries an electric charge and couples to charmonium. A fit to the pi(+/-) J/psi invariant mass spectrum, neglecting interference, results in a mass of (3899.0 +/- 3.6 +/- 4.9) MeV/c(2) and a width of (46 +/- 10 +/- 20) MeV. Its production ratio is measured to be R = (sigma(e(+)e(-) -> pi(+/-) Z(c)(3900)(-/+) -> pi(+)pi(-) J/psi)/sigma(e(+)e(-) -> pi(+)pi(-) J/psi)) = (21.5 +/- 3.3 +/- 7.5)%. In all measurements the first errors are statistical and the second are systematic.
677 citations
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07 Dec 2015TL;DR: This paper shows that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated and reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features.
Abstract: Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor. In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.
669 citations
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TL;DR: In this paper, the branching fraction ratio R(D)(()*()) of (B) over bar → D-(*())tau(-)(nu)over bar (tau) relative to (B), where l = e or mu, was measured using the full Belle data sample.
Abstract: We report a measurement of the branching fraction ratios R(D)(()*()) of (B) over bar -> D-(*())tau(-)(nu) over bar (tau) relative to (B) over bar -> D-(*())l(-)(nu) over barl (where l = e or mu) using the full Belle data sample of 772 x 10(6)B (B) over bar pairs collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. The measured values are R(D) = 0.375 +/- 0.064(stat) +/- 0.026(syst) and R(D*) = 0.293 +/- 0.038 (stat) +/- 0.015 (syst). The analysis uses hadronic reconstruction of the tag-side B meson and purely leptonic t decays. The results are consistent with earlier measurements and do not show a significant deviation from the standard model prediction.
652 citations
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TL;DR: In a study of Y(4260) → π+ π- J/φ decays, a structure is observed in the M(π(±)J/ψ) mass spectrum with 5.2σ significance that can be interpreted as a new charged charmoniumlike state.
Abstract: The cross section for ee+ e- → π+ π- J/ψ between 3.8 and 5.5 GeV is measured with a 967 fb(-1) data sample collected by the Belle detector at or near the Υ(nS) (n = 1,2,…,5) resonances. The Y(4260) state is observed, and its resonance parameters are determined. In addition, an excess of π+ π- J/ψ production around 4 GeV is observed. This feature can be described by a Breit-Wigner parametrization with properties that are consistent with the Y(4008) state that was previously reported by Belle. In a study of Y(4260) → π+ π- J/ψ decays, a structure is observed in the M(π(±)J/ψ) mass spectrum with 5.2σ significance, with mass M = (3894.5 ± 6.6 ± 4.5) MeV/c2 and width Γ = (63 ± 24 ± 26) MeV/c2, where the errors are statistical and systematic, respectively. This structure can be interpreted as a new charged charmoniumlike state.
622 citations
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TL;DR: In this paper, the spin and parity quantum numbers of the Higgs boson were studied based on the collision data collected by the ATLAS experiment at the LHC, and the results showed that the standard model spin-parity J(...
608 citations
Authors
Showing all 8797 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dominique Pallin | 132 | 1131 | 88668 |
Vladimir N. Uversky | 131 | 959 | 75342 |
Lee Sawyer | 130 | 1340 | 88419 |
Dmitry Novikov | 127 | 348 | 83093 |
Simon Lin | 126 | 754 | 69084 |
Zeno Dixon Greenwood | 126 | 1002 | 77347 |
Christian Ohm | 126 | 873 | 69771 |
Alexey Myagkov | 109 | 586 | 45630 |
Stanislav Babak | 107 | 308 | 66226 |
Alexander Zaitsev | 103 | 453 | 48690 |
Vladimir Popov | 102 | 1030 | 50257 |
Alexander Vinogradov | 96 | 410 | 40879 |
Gueorgui Chelkov | 93 | 321 | 41816 |
Igor Pshenichnov | 83 | 362 | 22699 |
Vladimir Popov | 83 | 370 | 26390 |