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October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.
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Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
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Measurement of the W boson mass

Journal of High Energy Physics. 2022. Vol. 2022. No. 01. Article 36.
Derkach D., Maevskiy A., Karpov M., Ratnikov F., Ryzhikov A., Ustyuzhanin A., Boldyrev A., Hushchyn M.

The W boson mass is measured using proton-proton collision data at s√s = 13 TeV corresponding to an integrated luminosity of 1.7 fb−1 recorded during 2016 by the LHCb experiment. With a simultaneous fit of the muon q/pT distribution of a sample of W → μν decays and the ϕ* distribution of a sample of Z → μμ decays the W boson mass is determined to be

(formula)

where uncertainties correspond to contributions from statistical, experimental systematic, theoretical and parton distribution function sources. This is an average of results based on three recent global parton distribution function sets. The measurement agrees well with the prediction of the global electroweak fit and with previous measurements.

Research target: Physics Computer Science
Language: English
DOI
Text on another site
Keywords: QCDForward physicsElectroweak interaction
Publication based on the results of:
Machine learning to check and improve the quality of data collected at the Large Hadron Collider and other particle physics experiments (2022)
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