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  1. Book ; Online: Generative Adversarial Networks for LHCb Fast Simulation

    Ratnikov, Fedor

    2020  

    Abstract: LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase ... ...

    Abstract LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.

    Comment: Proceedings for 24th International Conference on Computing in High Energy and Nuclear Physics
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Publishing date 2020-03-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Using Machine Learning to Speed Up and Improve Calorimeter R&D

    Ratnikov, Fedor

    2020  

    Abstract: Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Since the best solution is a trade-off between different kinds of limitations, a quick turn over is necessary to evaluate ... ...

    Abstract Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Since the best solution is a trade-off between different kinds of limitations, a quick turn over is necessary to evaluate physics performance for different techniques in different configurations. Two typical problems which slow down evaluation of physics performance for particular approaches to calorimeter detector technologies and configurations are: - Emulating particular detector properties including raw detector response together with a signal processing chain to adequately simulate a calorimeter response for different signal and background conditions. This includes combining detector properties obtained from the general Geant simulation with properties obtained from different kinds of bench and beam tests of detector and electronics prototypes. - Building an adequate reconstruction algorithm for physics reconstruction of the detector response which is reasonably tuned to extract the most of the performance provided by the given detector configuration. Being approached from the first principles, both problems require significant development efforts. Fortunately, both problems may be addressed by using modern machine learning approaches, that allow a combination of available details of the detector techniques into corresponding higher level physics performance in a semi-automated way. In this paper, we discuss the use of advanced machine learning techniques to speed up and improve the precision of the detector development and optimisation cycle, with an emphasis on the experience and practical results obtained by applying this approach to epitomising the electromagnetic calorimeter design as a part of the upgrade project for the LHCb detector at LHC.

    Comment: "Calorimetry for High Energy Frontier" conference 2019' Fukuoka, Japan
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 621
    Publishing date 2020-03-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks

    Maevskiy, A. / Ratnikov, F. / Zinchenko, A. / Riabov, V.

    2020  

    Abstract: 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 ... ...

    Abstract 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 the NICA accelerator complex. Our method is based on a Generative Adversarial Network - a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.

    Comment: This is a post-peer-review, pre-copyedit version of an article published in Eur. Phys. J. C. The final authenticated version is available online at: http://dx.doi.org/10.1140/epjc/s10052-021-09366-4
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Publishing date 2020-12-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector

    Maevskiy, A. / Ratnikov, F. / Zinchenko, A. / Riabov, V. / Sukhorosov, A. / Evdokimov, D.

    2022  

    Abstract: The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount of computing resources to run. Often this may not be afforded and ... ...

    Abstract The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount of computing resources to run. Often this may not be afforded and less resource-intensive approaches are desired. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as the basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works more than an order of magnitude faster compared to the detailed simulation without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.

    Comment: Submitted for the proceedings of ACAT2021, https://indico.cern.ch/event/855454/contributions/4596732/
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning
    Publishing date 2022-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: What Machine Learning Can Do for Focusing Aerogel Detectors

    Shipilov, Foma / Barnyakov, Alexander / Bobrovnikov, Vladimir / Kononov, Sergey / Ratnikov, Fedor

    2023  

    Abstract: Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient ...

    Abstract Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.

    Comment: 5 pages, 4 figures, to be published in 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP2023) proceedings
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Publishing date 2023-12-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Energy reconstruction for large liquid scintillator detectors with machine learning techniques

    Gavrikov, Arsenii / Malyshkin, Yury / Ratnikov, Fedor

    aggregated features approach

    2022  

    Abstract: Large scale detectors consisting of a liquid scintillator (LS) target surrounded by an array of photo-multiplier tubes (PMT) are widely used in modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and upcoming JUNO with its ... ...

    Abstract Large scale detectors consisting of a liquid scintillator (LS) target surrounded by an array of photo-multiplier tubes (PMT) are widely used in modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and upcoming JUNO with its satellite detector TAO. Such apparatuses are able to measure neutrino energy, which can be derived from the amount of light and its spatial and temporal distribution over PMT-channels. However, achieving a fine energy resolution in large scale detectors is challenging. In this work, we present machine learning methods for energy reconstruction in JUNO, the most advanced detector of its type. We focus on positron events in the energy range of 0-10 MeV which corresponds to the main signal in JUNO $-$ neutrinos originated from nuclear reactor cores and detected via an inverse beta-decay channel. We consider Boosted Decision Trees and Fully Connected Deep Neural Network trained on aggregated features, calculated using information collected by PMTs. We describe the details of our feature engineering procedure and show that machine learning models can provide energy resolution $\sigma = 3\%$ at 1 MeV using subsets of engineered features. The dataset for model training and testing is generated by the Monte Carlo method with the official JUNO software. Consideration of calibration sources for evaluation of the reconstruction algorithms performance on real data is also presented.
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Using machine learning to speed up new and upgrade detector studies

    Ratnikov, F. / Derkach, D. / Boldyrev, A. / Shevelev, A. / Fakanov, P. / Matyushin, L.

    a calorimeter case

    2020  

    Abstract: In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design ... ...

    Abstract In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R\&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The spatial reconstruction and time of arrival properties for the electromagnetic calorimeter were demonstrated.

    Comment: Talk presented on CHEP 2019 conference
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Computational Physics
    Subject code 670
    Publishing date 2020-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Generative Models for Fast Calorimeter Simulation.LHCb case

    Chekalina, Viktoria / Orlova, Elena / Ratnikov, Fedor / Ulyanov, Dmitry / Ustyuzhanin, Andrey / Zakharov, Egor

    2018  

    Abstract: Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large ... ...

    Abstract Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.

    Comment: Proceedings of the presentation at CHEP 2018 Conference
    Keywords Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning
    Publishing date 2018-12-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: K voprosu o profilaktike i lechenii kokliushnykh zabolevanii u detei putem podsaki tkanei po metodu V. P. Filatova.

    RATNIKOV, F I

    Voprosy pediatrii

    1953  Volume 21, Issue 3, Page(s) 42–45

    Title translation Prevention and therapy of whooping cough in children by tissue implantation in children according to V. P. Filatov's method.
    MeSH term(s) Biomedical Research ; Cell- and Tissue-Based Therapy ; Embryo Implantation ; Whooping Cough/therapy
    Language Undetermined
    Publishing date 1953-05
    Publishing country Russia (Federation)
    Document type Journal Article
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Measurement of CP Violation in B^{0}→ψ(→ℓ^{+}ℓ^{-})K_{S}^{0}(→π^{+}π^{-}) Decays.

    Aaij, R / Abdelmotteleb, A S W / Abellan Beteta, C / Abudinén, F / Ackernley, T / Adeva, B / Adinolfi, M / Adlarson, P / Afsharnia, H / Agapopoulou, C / Aidala, C A / Ajaltouni, Z / Akar, S / Akiba, K / Albicocco, P / Albrecht, J / Alessio, F / Alexander, M / Alfonso Albero, A /
    Aliouche, Z / Alvarez Cartelle, P / Amalric, R / Amato, S / Amey, J L / Amhis, Y / An, L / Anderlini, L / Andersson, M / Andreianov, A / Andreola, P / Andreotti, M / Andreou, D / Ao, D / Archilli, F / Artamonov, A / Artuso, M / Aslanides, E / Atzeni, M / Audurier, B / Bacher, D / Bachiller Perea, I / Bachmann, S / Bachmayer, M / Back, J J / Bailly-Reyre, A / Baladron Rodriguez, P / Balagura, V / Baldini, W / Baptista de Souza Leite, J / Barbetti, M / Barbosa, I R / Barlow, R J / Barsuk, S / Barter, W / Bartolini, M / Baryshnikov, F / Basels, J M / Bassi, G / Batsukh, B / Battig, A / Bay, A / Beck, A / Becker, M / Bedeschi, F / Bediaga, I B / Beiter, A / Belin, S / Bellee, V / Belous, K / Belov, I / Belyaev, I / Benane, G / Bencivenni, G / Ben-Haim, E / Berezhnoy, A / Bernet, R / Bernet Andres, S / Berninghoff, D / Bernstein, H C / Bertella, C / Bertolin, A / Betancourt, C / Betti, F / Bex, J / Bezshyiko, Ia / Bhom, J / Bian, L / Bieker, M S / Biesuz, N V / Billoir, P / Biolchini, A / Birch, M / Bishop, F C R / Bitadze, A / Bizzeti, A / Blago, M P / Blake, T / Blanc, F / Blank, J E / Blusk, S / Bobulska, D / Bocharnikov, V / Boelhauve, J A / Boente Garcia, O / Boettcher, T / Bohare, A / Boldyrev, A / Bolognani, C S / Bolzonella, R / Bondar, N / Borgato, F / Borghi, S / Borsato, M / Borsuk, J T / Bouchiba, S A / Bowcock, T J V / Boyer, A / Bozzi, C / Bradley, M J / Braun, S / Brea Rodriguez, A / Breer, N / Brodzicka, J / Brossa Gonzalo, A / Brown, J / Brundu, D / Buonaura, A / Buonincontri, L / Burke, A T / Burr, C / Bursche, A / Butkevich, A / Butter, J S / Buytaert, J / Byczynski, W / Cadeddu, S / Cai, H / Calabrese, R / Calefice, L / Cali, S / Calvi, M / Calvo Gomez, M / Cambon Bouzas, J / Campana, P / Campora Perez, D H / Campoverde Quezada, A F / Capelli, S / Capriotti, L / Carbone, A / Carcedo Salgado, L / Cardinale, R / Cardini, A / Carniti, P / Carus, L / Casais Vidal, A / Caspary, R / Casse, G / Cattaneo, M / Cavallero, G / Cavallini, V / Celani, S / Cerasoli, J / Cervenkov, D / Chadwick, A J / Chahrour, I / Chapman, M G / Charles, M / Charpentier, Ph / Chavez Barajas, C A / Chefdeville, M / Chen, C / Chen, S / Chernov, A / Chernyshenko, S / Chobanova, V / Cholak, S / Chrzaszcz, M / Chubykin, A / Chulikov, V / Ciambrone, P / Cicala, M F / Cid Vidal, X / Ciezarek, G / Cifra, P / Clarke, P E L / Clemencic, M / Cliff, H V / Closier, J / Cobbledick, J L / Cocha Toapaxi, C / Coco, V / Cogan, J / Cogneras, E / Cojocariu, L / Collins, P / Colombo, T / Comerma-Montells, A / Congedo, L / Contu, A / Cooke, N / Corredoira, I / Correia, A / Corti, G / Cottee Meldrum, J J / Couturier, B / Craik, D C / Cruz Torres, M / Currie, R / Da Silva, C L / Dadabaev, S / Dai, L / Dai, X / Dall'Occo, E / Dalseno, J / D'Ambrosio, C / Daniel, J / Danilina, A / d'Argent, P / Davidson, A / Davies, J E / Davis, A / De Aguiar Francisco, O / de Boer, J / De Bruyn, K / De Capua, S / De Cian, M / De Freitas Carneiro Da Graca, U / De Lucia, E / De Miranda, J M / De Paula, L / De Serio, M / De Simone, D / De Simone, P / De Vellis, F / de Vries, J A / Dean, C T / Debernardis, F / Decamp, D / Dedu, V / Del Buono, L / Delaney, B / Dembinski, H-P / Denysenko, V / Deschamps, O / Dettori, F / Dey, B / Di Nezza, P / Diachkov, I / Didenko, S / Ding, S / Dobishuk, V / Docheva, A D / Dolmatov, A / Dong, C / Donohoe, A M / Dordei, F / Dos Reis, A C / Douglas, L / Downes, A G / Duan, W / Duda, P / Dudek, M W / Dufour, L / Duk, V / Durante, P / Duras, M M / Durham, J M / Dutta, D / Dziurda, A / Dzyuba, A / Easo, S / Eckstein, E / Egede, U / Egorychev, A / Egorychev, V / Eirea Orro, C / Eisenhardt, S / Ejopu, E / Ek-In, S / Eklund, L / Elashri, M / Ellbracht, J / Ely, S / Ene, A / Epple, E / Escher, S / Eschle, J / Esen, S / Evans, T / Fabiano, F / Falcao, L N / Fan, Y / Fang, B / Fantini, L / Faria, M / Farmer, K / Farry, S / Fazzini, D / Felkowski, L / Feng, M / Feo, M / Fernandez Gomez, M / Fernez, A D / Ferrari, F / Ferreira Lopes, L / Ferreira Rodrigues, F / Ferreres Sole, S / Ferrillo, M / Ferro-Luzzi, M / Filippov, S / Fini, R A / Fiorini, M / Firlej, M / Fischer, K M / Fitzgerald, D S / Fitzpatrick, C / Fiutowski, T / Fleuret, F / Fontana, M / Fontanelli, F / Foreman, L F / Forty, R / Foulds-Holt, D / Franco Sevilla, M / Frank, M / Franzoso, E / Frau, G / Frei, C / Friday, D A / Frontini, L / Fu, J / Fuehring, Q / Fujii, Y / Fulghesu, T / Gabriel, E / Galati, G / Galati, M D / Gallas Torreira, A / Galli, D / Gambetta, S / Gandelman, M / Gandini, P / Gao, H / Gao, R / Gao, Y / Garau, M / Garcia Martin, L M / Garcia Moreno, P / García Pardiñas, J / Garcia Plana, B / Garcia Rosales, F A / Garrido, L / Gaspar, C / Geertsema, R E / Gerken, L L / Gersabeck, E / Gersabeck, M / Gershon, T / Giambastiani, L / Giasemis, F I / Gibson, V / Giemza, H K / Gilman, A L / Giovannetti, M / Gioventù, A / Gironella Gironell, P / Giugliano, C / Giza, M A / Gizdov, K / Gkougkousis, E L / Glaser, F C / Gligorov, V V / Göbel, C / Golobardes, E / Golubkov, D / Golutvin, A / Gomes, A / Gomez Fernandez, S / Goncalves Abrantes, F / Goncerz, M / Gong, G / Gooding, J A / Gorelov, I V / Gotti, C / Grabowski, J P / Granado Cardoso, L A / Graugés, E / Graverini, E / Grazette, L / Graziani, G / Grecu, A T / Greeven, L M / Grieser, N A / Grillo, L / Gromov, S / Gu, C / Guarise, M / Guittiere, M / Guliaeva, V / Günther, P A / Guseinov, A-K / Gushchin, E / Guz, Y / Gys, T / Hadavizadeh, T / Hadjivasiliou, C / Haefeli, G / Haen, C / Haimberger, J / Haines, S C / Hajheidari, M / Halewood-Leagas, T / Halvorsen, M M / Hamilton, P M / Hammerich, J / Han, Q / Han, X / Hansmann-Menzemer, S / Hao, L / Harnew, N / Harrison, T / Hartmann, M / Hasse, C / Hatch, M / He, J / Heijhoff, K / Hemmer, F / Henderson, C / Henderson, R D L / Hennequin, A M / Hennessy, K / Henry, L / Herd, J / Heuel, J / Hicheur, A / Hill, D / Hilton, M / Hollitt, S E / Horswill, J / Hou, R / Hou, Y / Howarth, N / Hu, J / Hu, W / Hu, X / Huang, W / Huang, X / Hulsbergen, W / Hunter, R J / Hushchyn, M / Hutchcroft, D / Ibis, P / Idzik, M / Ilin, D / Ilten, P / Inglessi, A / Iniukhin, A / Ishteev, A / Ivshin, K / Jacobsson, R / Jage, H / Jaimes Elles, S J / Jakobsen, S / Jans, E / Jashal, B K / Jawahery, A / Jevtic, V / Jiang, E / Jiang, X / Jiang, Y / Jiang, Y J / John, M / Johnson, D / Jones, C R / Jones, T P / Joshi, S / Jost, B / Jurik, N / Juszczak, I / Kaminaris, D / Kandybei, S / Kang, Y / Karacson, M / Karpenkov, D / Karpov, M / Kauniskangas, A M / Kautz, J W / Keizer, F / Keller, D M / Kenzie, M / Ketel, T / Khanji, B / Kharisova, A / Kholodenko, S / Khreich, G / Kirn, T / Kirsebom, V S / Kitouni, O / Klaver, S / Kleijne, N / Klimaszewski, K / Kmiec, M R / Koliiev, S / Kolk, L / Kondybayeva, A / Konoplyannikov, A / Kopciewicz, P / Kopecna, R / Koppenburg, P / Korolev, M / Kostiuk, I / Kot, O / Kotriakhova, S / Kozachuk, A / Kravchenko, P / Kravchuk, L / Kreps, M / Kretzschmar, S / Krokovny, P / Krupa, W / Krzemien, W / Kubat, J / Kubis, S / Kucewicz, W / Kucharczyk, M / Kudryavtsev, V / Kulikova, E / Kupsc, A / Kutsenko, B K / Lacarrere, D / Lafferty, G / Lai, A / Lampis, A / Lancierini, D / Landesa Gomez, C / Lane, J J / Lane, R / Langenbruch, C / Langer, J / Lantwin, O / Latham, T / Lazzari, F / Lazzeroni, C / Le Gac, R / Lee, S H / Lefèvre, R / Leflat, A / Legotin, S / Leroy, O / Lesiak, T / Leverington, B / Li, A / Li, H / Li, K / Li, L / Li, P / Li, P-R / Li, S / Li, T / Li, Y / Li, Z / Lian, Z / Liang, X / Lin, C / Lin, T / Lindner, R / Lisovskyi, V / Litvinov, R / Liu, G / Liu, H / Liu, K / Liu, Q / Liu, S / Liu, Y / Lobo Salvia, A / Loi, A / Lomba Castro, J / Long, T / Longstaff, I / Lopes, J H / Lopez Huertas, A / López Soliño, S / Lovell, G H / Lu, Y / Lucarelli, C / Lucchesi, D / Luchuk, S / Lucio Martinez, M / Lukashenko, V / Luo, Y / Lupato, A / Luppi, E / Lynch, K / Lyu, X-R / Ma, R / Maccolini, S / Machefert, F / Maciuc, F / Mackay, I / Macko, V / Madhan Mohan, L R / Madurai, M M / Maevskiy, A / Magdalinski, D / 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    Physical review letters

    2024  Volume 132, Issue 2, Page(s) 21801

    Abstract: A measurement of time-dependent CP violation in the decays of B^{0} and B[over ¯]^{0} mesons to the final states J/ψ(→μ^{+}μ^{-})K_{S}^{0}, ψ(2S)(→μ^{+}μ^{-})K_{S}^{0} and J/ψ(→e^{+}e^{-})K_{S}^{0} with K_{S}^{0}→π^{+}π^{-} is presented. The data ... ...

    Abstract A measurement of time-dependent CP violation in the decays of B^{0} and B[over ¯]^{0} mesons to the final states J/ψ(→μ^{+}μ^{-})K_{S}^{0}, ψ(2S)(→μ^{+}μ^{-})K_{S}^{0} and J/ψ(→e^{+}e^{-})K_{S}^{0} with K_{S}^{0}→π^{+}π^{-} is presented. The data correspond to an integrated luminosity of 6  fb^{-1} collected at a center-of-mass energy of sqrt[s]=13  TeV with the LHCb detector. The CP-violation parameters are measured to be S_{ψK_{S}^{0}}=0.717±0.013(stat)±0.008(syst) and C_{ψK_{S}^{0}}=0.008±0.012(stat)±0.003(syst). This measurement of S_{ψK_{S}^{0}} represents the most precise single measurement of the CKM angle β to date and is more precise than the current world average. In addition, measurements of the CP-violation parameters of the individual channels are reported and a combination with the LHCb Run 1 measurements is performed.
    Language English
    Publishing date 2024-01-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.132.021801
    Database MEDical Literature Analysis and Retrieval System OnLINE

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