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Cherenkov detectors fast simulation using neural networks
Derkach D., Kazeev N., Ratnikov F. et al., Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2020 Vol. 952 No. 0168-9002 P. 161804
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation ...
Added: February 11, 2019
Hushchyn M., Chekalina V., , in : Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. Frontier Detectors for Frontier Physics: 14th Pisa Meeting on Advanced Detectors. : Elsevier, 2019. P. 568-569.
One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the ...
Added: February 19, 2020
Derkach D., Hushchyn M., Kazeev N. et al., Journal of High Energy Physics 2019 Vol. 2019 No. 2 P. 1-33
A search for CP violation in the Cabibbo-suppressed D0 → K+K−π+π− decay mode is performed using an amplitude analysis. The measurement uses a sample of pp collisions recorded by the LHCb experiment during 2011 and 2012, corresponding to an integrated luminosity of 3.0 fb−1. The D0 mesons are reconstructed from semileptonic b-hadron decays into D0μ−X final states. The selected sample contains more than 160 000 signal decays, allowing ...
Added: March 17, 2019
M. Borisyak, N. Kazeev, Journal of Instrumentation 2019 Vol. 14 No. 08 P. 1-8
Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some ...
Added: August 20, 2019
Boldyrev A., Derkach D., Ratnikov F. et al., Journal of Physics: Conference Series 2021 Vol. 1740 No. 012047 Article 012047
The optimization of big industrial setups and the accompanying detailed simulations of internal physical processes require complex and time-consuming simulation calculations. We propose a versatile approach that can alleviate difficulties in solving this problem and show this using an example of electromagnetic calorimeter optimization at a Large Hadron Collider experiment. Our approach consists of a ...
Added: October 29, 2021
Borisyak M., Kazeev N., Journal of Physics: Conference Series 2020 Vol. 1525 Article 012088
Data analysis in high energy physics has to deal with data samples produced from different sources. One of the most widely used ways to unfold their contributions is the sPlot technique. It uses the results of a maximum likelihood fit to assign weights to events. Some weights produced by sPlot are by design negative. Negative ...
Added: October 5, 2021
Derkach D., Ratnikov F., Ustyuzhanin A., World Scientific Publishing Co., 2022
The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality ...
Added: September 21, 2022
Derkach D., Hushchyn M., Likhomanenko T. et al., Journal of Physics: Conference Series 2018 Vol. 1085 No. 4 P. 1-5
One of the most important aspects of data analysis at the LHC experiments is the particle identification (PID). In LHCb, several different sub-detectors provide PID information: two Ring Imaging Cherenkov (RICH) detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, we have developed models based on deep learning and ...
Added: October 18, 2018
A. Maevskiy, F. Ratnikov, Zinchenko A. et al., The European Physical Journal C - Particles and Fields 2021 Vol. 81 Article 599
High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at ...
Added: July 12, 2021
Soboleva Natalia, Yakovlev K., , in : Proceedings of the 42nd German Conference on Artificial Intelligence (KI 2019), Kassel, Germany, September 23-26, 2019. : Springer, 2019. P. 316-324.
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes ...
Added: February 3, 2020
Derkach D., Hushchyn M., Kazeev N. et al., Physical Review Letters 2019 Vol. 122 No. 1 P. 1-10
A measurement of the charm-mixing parameter yCP using D0 → KþK−, D0 → πþπ−, and D0 → K−πþ decays is reported. The D0 mesons are required to originate from semimuonic decays of B− and B0 mesons. These decays are partially reconstructed in a data set of proton-proton collisions at center-of-mass energies of 7 and 8 ...
Added: March 17, 2019
A. Boldyrev, D. Derkach, F. Ratnikov et al., Journal of Instrumentation 2020 Vol. 15 P. 1-7
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy ...
Added: September 22, 2020
Ratnikov F., Panin A., Chekalina V., Journal of Physics: Conference Series 2017
Reconstruction and identification in calorimeters of modern High Energy Physics experiments is a complicated task. Solutions are usually driven by a priori knowledge about expected properties of reconstructed objects. Such an approach is also used to distinguish single photons in the electromagnetic calorimeter of the LHCb detector on LHC from overlapping photons produced from high ...
Added: February 26, 2018
Lukashina N., Kartysheva E., Spjuth O. et al., Journal of Cheminformatics 2022 Vol. 14 Article 49
Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for new drugs. This paper shows that the ...
Added: October 11, 2022
Prague : CEUR Workshop Proceedings, 2014
The first and the second edition of the FCA4AI Workshop showed that many researchers working in Artificial Intelligence are indeed interested by a well-founded method for classi- fication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). The first edition of FCA4AI was co-located with ECAI 2012 in Montpellier and published as http://ceur-ws.org/Vol-939/ while the ...
Added: September 12, 2014
Kuznetsov V. O., Логистика и управление цепями поставок 2018 № 4 (87) С. 27-33
One of the options for a more flexible approach to analyzing the reliability of supply chains is the principal component analysis (PCA). With a large number of variables describing supply chain, it is a difficult task to analyze the structure of variables in two-dimensional space. Within the analysis of the variables dependencies PCA allows to ...
Added: November 29, 2018
Anton Khritankov, , in : Software Quality: Future Perspectives on Software Engineering Quality: 13th International Conference, SWQD 2021, Vienna, Austria, January 19–21, 2021, Proceedings. : Springer, 2021. P. 54-65.
In this concept paper, we explore some of the aspects of quality of continuous learning artificial intelligence systems as they interact with and influence their environment. We study an important problem of implicit feedback loops that occurs in recommendation systems, web bulletins and price estimation systems. We demonstrate how feedback loops intervene with user behavior ...
Added: September 23, 2021
Kitzmann H., Strimovskaya A., Serova E., , in : Transfer, Diffusion and Adoption of Next-Generation Digital Technologies. IFIP WG 8.6 International Working Conference on Transfer and Diffusion of IT, TDIT 2023 Nagpur, India, December 15–16, 2023 Proceedings, Part II. Vol. 698.: Springer, 2024. P. 132-143.
Highly evolving economic environment requires from logistics companies
fast response and agile solutions. Recently development of digital technologies
gives significant advantages to logistics business. Hence many optimized
processes belong to operational management level. At the same time the importance
of digital technologies adoption to strategic management level should not be
underestimated, as it allows gaining competitive advantages alongside the supply
chain. ...
Added: January 12, 2024
Springer, 2014
This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective ...
Added: September 15, 2014
Dudyrev E., Semenkov Ilia, Kuznetsov S. et al., Plos One 2022 Vol. 17 No. 10 Article e0275814
Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of ...
Added: October 29, 2022
[б.и.], 2018
Proceedings of the international conference on Uncertainty in Artificial Intelligence (UAI 2018) ...
Added: October 29, 2018
Gothenburg : Association for Computational Linguistics, 2023
Current deep learning systems require large amounts of data in order to yield optimal results. Despite ever-increasing model and data size, these systems have achieved remarkable success across a wide range of tasks in NLP, and AI in general. However, these systems possess a number of limitations. Firstly, the models require a significant amount of ...
Added: December 5, 2023
PMLR, 2022
Added: July 27, 2022