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Energy-Scaling Behavior of Intrinsic Transverse-Momentum Parameters in Drell-Yan Simulation
No full textNgadiuba, Jennifer/0000-0002-0055-2935; Dolek, Furkan/0000-0001-7092-5517; Garcia, Francisco/0000-0002-4023-7964; Evdokimov, Olga/0000-0002-1250-8931; Kim, Youngwan/0000-0002-4856-5989; Cardini, Andrea/0000-0003-1803-0999; Benaglia, Andrea Davide/0000-0003-1124-8450; Tytgat, Michael/0000-0002-3990-2074; Stylianou, Nicolas/0000-0002-0113-6829; Ligabue, Franco/0000-0002-1549-7107; Zhang, Licheng/0000-0001-7947-9007; Tornago, Marta/0000-0001-6768-1056; Ruiz, Jose/0000-0002-3306-0363; Brooke, James/0000-0003-2529-0684; Murillo Quijada, Javier Alberto/0000-0003-4933-2092; Portales, Louis/0000-0002-9860-9185; Heath, Helen/0000-0001-6576-9740; Chou, Pin-Chun/0000-0002-5842-8566; Benato, Lisa/0000-0001-5135-7489; Giammanco, Andrea/0000-0001-9640-8294; Chokheli, Davit/0000-0001-7535-4186; Bhowmik, Sandeep/0000-0003-1260-973X; Cussans, David/0000-0001-8192-0826; Tiras, Emrah/0000-0002-5628-7464; Duarte, Javier Mauricio/0000-0002-5076-7096; Safdari, Murtaza/0000-0001-8323-7318; Whalen, Kathleen/0000-0002-9383-8763; Lu, Meng/0000-0002-6999-3931; Ventura, Sandro/0000-0002-8938-2193; Longo, Luigi/0000-0002-2357-7043; Ivanov, Andrew/0000-0002-9270-5643; Csanad, Mate/0000-0002-3154-6925; Vami, Tamas Almos/0000-0002-0959-9211; Mora Herrera, Maria Clemencia/0000-0003-3915-3170; Trocino, Daniele/0000-0002-2830-5872; Grunewald, Martin/0000-0002-5754-0388; Santpur, Sai Neha/0000-0001-6467-9970; Diaz, Daniel/0000-0001-6834-1176; Kreczko, Luke/0000-0003-2341-8330; Smith, Nicholas/0000-0002-0324-3054; Leonidou, Christos/0009-0008-6993-2005An analysis is presented based on models of the intrinsic transverse momentum (intrinsic k(T)) of partons in nucleons by studying the dilepton transverse momentum in Drell-Yan events. Using parameter tuning in event generators and existing data from fixed-target experiments and from hadron colliders, our investigation spans 3 orders of magnitude in center-of-mass energy and 2 orders of magnitude in dilepton invariant mass. The results show an energy-scaling behavior of the intrinsic k(T) parameters, independent of the dilepton invariant mass at a given center-of-mass energy.SC (Armenia); BMBWF (Austria); FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); MES (Bulgaria); BNSF (Bulgaria); CERN; CAS (China); MoST (China); NSFC (China); MINCIENCIAS (Colombia); MSES (Croatia); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, (Estonia); RVTT3 (Estonia); MoER TK202 (Estonia); Academy of Finland (Finland); MEC(Finland); HIP (Finland); CEA (France); CNRS/IN2P3 (France); SRNSF (Georgia); BMBF(Germany); DFG (Germany); HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP (Republic of Korea); NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CINVESTAV (Mexico); CONACYT (Mexico); LNS (Mexico); SEP (Mexico); UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES (Poland); NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI (Spain); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI (Thailand); NSTDA (Thailand); TUBITAK (Turkey); TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE (USA); NSF (USA)We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA)
Enhanced Control of an Inverted Cart-Pendulum System Using Cascade Pd Controller Optimized With Genetic Algorithm
No full textThe inverted pendulum system is a widely studied benchmark problem in control engineering due to its inherent nonlinearity and instability. In this work, a nonlinear multibody model of the inverted pendulum is developed and analyzed to capture the complex dynamics of the system. A cascade Proportional-Derivative (PD) control scheme is implemented, with the dual objectives of controlling the pendulum angle and the cart position through nested control loops. The optimal tuning of PD gains, a critical factor for robust performance, is performed using Genetic Algorithm optimization technique. The proposed approach is implemented and simulated in MATLAB Simulink with Simscape to evaluate its effectiveness. Simulation results demonstrate the successful control of both pendulum angle and the cart position. The study highlights the success of cascade PD control and superior performance of the GA tuning approach over conventional methods, emphasizing its efficacy in handling nonlinear and dynamic control challenges. © 2025 IEEE
Estimation of Solar Radiation and Photovoltaic Power Potential of Türkiye Using Anfis
No full textIn this study, we estimated global horizontal irradiance (GHI) and photovoltaic (PV) power potential in Türkiye using fan Adaptive Neuro-Fuzzy Inference System (ANFIS) with inputs from Meteonorm 8.0. We considered latitude and longitude for GHI estimation and added tilt and azimuth angles for PV power potential. Training and testing data yielded Root Mean Square Errors (RMSE) of 10.429 kWh/m2/year and 35.395 kWh/m2/year for GHI, and 3.984 kWh/kWp/year and 39.624 kWh/kWp/year for PV power potential, respectively. The coefficient of determination (R2) was 0.995 for GHI training data, and R2= 0.946 for GHI testing data, and R2= 0.993 for PV power potential training data, and R2= 0.951 for PV power potential testing data. Results showed that the southeast of Türkiye had the highest GHI, around 1900 kWh/m2/year, while the north had the lowest, around 1400 kWh/m2/year. The highest electricity production occurred in the south, approximately 1900 kWh/kWp/year, while the north yielded the least, about 1450 kWh/kWp/year. Tilt angle increase and azimuth angles of -90° or + 90° led to decreased electricity production. This study provides precise GHI and PV power potential estimates for Türkiye, using ANFIS with diverse meteorological inputs. © The Author(s) 2025
Adjacent-Net: Deep Learning Classification of Adjacent Buildings for Assessing Pounding Effects Using Building Facade Images in Earthquake-Prone Regions
No full textIn earthquake-prone areas, it is extremely important to carry out risk analyses of existing buildings and to take proactive measures in advance of potential earthquakes. Despite the availability of Rapid Seismic Assessment Methods (RSAMs), prioritising the seismic risk of buildings is a significant challenge due to the large number of residential buildings in the building stock. In RSAMs, many factors are taken into consideration to determine the earthquake risk priority. While specific construction conditions determine the risk parameters for the considered structures, one of them is the possible pounding effects (collision) of adjacent buildings. The fact that RSAMs have many evaluation parameters makes it difficult in site survey for technical experts to make decisions in some cases. Therefore, it is very important to perform these operations with software support. Based on this motivation, this study aims to perform pre-earthquake risk analysis of residential reinforced concrete buildings by assisting expert engineers (or facilitating the decision-making process in the absence of technical expertise) and to estimate the adjacent building parameter using building facade images for risk prioritisation. To achieve these objectives, a novel deep learning Convolutional Neural Network (CNN) model, named Adjacent-Net, is designed and developed to classify building facade images into adjacent or non-adjacent categories. The performance of Adjacent-Net is compared with various state-of-the-art CNN models such as DarkNet-53, EfficientNet, Inception ResNetV2, NasNet Large, ResNet-101, ShuffleNet, SqueezeNet, VGG-19, and Xception. For evaluation purposes, a dataset comprising 6170 building facade images is collected, and the results indicate that Adjacent-Net can accurately extract building adjacency parameters from images with an accuracy rate of approximately 98 %. This underscores the potential of intelligent systems in detecting collision scenarios, assessing the seismic risk of structures, and determining critical geometric parameters of buildings.This study was supported by Konya Technical University Scientific Research Projects Coordination Unit (Project Number: 211104060) . Authors also would like to thank Konya Technical University for their financial support. This study is derived from part of the authors' registered national Patent No: 2021 021293. The software codes and data in the study could not be shared for this reason and can be sent by email from the authors on request. The authors would also like to thank Selim CELIK (Centre for Structural Engineering and Informatics (CSEI) in the Civil Engineering Department at the University of Nottingham) and Mehmet Fatih AS IK (Turkish State Railways (TCDD) ) , who contributed to data collection via GSVKonya Technical University Scientific Research Projects Coordination Unit; Konya Technical University; [211104060
Portfolio Management Through Algorithmic Trading
No full textThis chapter stresses how algorithmic trading has transformed portfolio management, underscoring the resultant ability to optimize risk-adjusted returns, enhance decision processes, and sustain efficient asset allocation. Advanced computational methods utilized in this research examine algorithmic strategies as a means to address the complexities of today’s financial markets in their ability to handle risk management, diversification, and periodic rebalancing. The results of the optimization of a portfolio consisting of six Nasdaq 100 stocks—Amazon, Apple, AMD, Tesla, Google, and NVIDIA—for ten years, from 2014 to 2024, are shown here. These assets have been selected based on their historical performance and variable risk-return profile as a sample to evaluate algorithmic trading strategies. In this paper, SLSQP is used to optimize the weights of each portfolio according to the Sharpe ratio, with efficient capital allocation considering the realistic constraint of no short-selling on the historical price data. Annual rebalancing was adopted to dampen the drifting of weights and to make the weights given at any period closer to the target weights. The performance of the portfolio is measured concerning the Nasdaq 100 through a set of key metrics: the cumulative return, the annualized return, volatility, and the Sharpe ratio. Hereby, the optimized portfolio gains an annualized return of 46.89% with a cumulative return of 4576.56% throughout the period under review. Although the portfolio demonstrated higher volatility (40.89%) in comparison to the Nasdaq 100, its Sharpe ratio of 1.12 surpassed that of the benchmark (0.90), thereby illustrating superior risk-adjusted performance. The rebalancing process effectively maintained the efficiency of the portfolio, although the concentration of risk in high-growth assets, such as NVIDIA, was brought to light. The findings highlight the inherent trade-offs between return maximization and risk management, offering valuable insights for investors, practitioners, and policymakers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Nano Silisyum Dioksit Kolloidal Süspansiyonunun Tribolojik Performans Ajanı Olarak Kullanılabilirliğinin İncelenmesi
No full textCooling and lubricating fluids are used in many sectors such as metalworking, automotive and aerospace. These fluids minimise wear by reducing friction between materials, effectively dissipating heat and protecting surfaces. With the addition of nanoparticles to cooling and lubricating fluids, the tribological performance of the material is significantly affected. In this study, the colloidal suspension formed by combining nano silicon dioxide with boron oil and pure water separately will be used as cooling and lubricating fluid for the wear experiment. The effect of the cooling and lubricating fluid to be used in the experiment on the wear behaviour of 1040 manufacturing steel will be observed. The tribological performance of the coolant will be examined by temperature change, friction coefficient, volume loss and surface roughness parameters.Soğutma ve yağlama sıvıları metal işleme, otomotiv, havacılık gibi birçok sektörde kullanılmaktadır. Bu sıvılar; malzemeler arasındaki sürtünmeyi azaltarak, ısıyı etkili bir şekilde dağıtarak ve yüzeylerin korunmasını sağlayarak aşınmayı minimum seviyeye düşürmektedir. Soğutma ve yağlama sıvılarına nano partiküllerin ilavesiyle, malzemenin tribolojik performansı önemli ölçüde etkilenmektedir. Bu çalışmada aşınma deneyi için, nano silisyum dioksitin ayrı ayrı bor yağı ve saf su ile birleştirilmesiyle oluşan kolloidal süspansiyon, soğutma ve yağlama sıvısı olarak kullanılacaktır. Deneyde kullanılacak soğutma ve yağlama sıvısının, 1040 imalat çeliğinin aşınma davranışına etkisi gözlemlenecektir. Soğutma sıvısının tribolojik performansı sıcaklık değişimi, sürtünme katsayısı, hacim kaybı ve yüzey pürüzlülüğü parametreleri ile incelenecektir
Metacognitive Awareness of Teachers of English as a Foreign Language
No full textThe purpose of this study was to investigate in-service English language teachers' (ISELTs) metacognitive awareness levels and how they integrate metacognitive strategies into their teaching practices and to identify the factors influencing their judgements. Using a sequential explanatory research design, the Metacognitive Awareness Inventory for Teachers (MAIT) was administered to 54 ISELTs in Turkey. Quantitative data were analyzed using Kruskal-Wallis H test and Mann Whitney U test, while qualitative data were examined through content analysis. The results showed that there were significant demographic differences in metacognitive awareness levels and sub- dimensions. Qualitative findings revealed both positive and negative aspects of the metacognitive awareness of ISELTs and its impact on their teaching approaches. This study highlights the complex relationship between metacognitive awareness, teaching techniques and contextual factors, emphasizing the need for holistic teacher training and support to overcome emotional and practical challenges. This study highlights the need for a holistic approach to teacher education and support that aims to develop teachers' metacognitive skills and equip them with the necessary tools to enhance student-centered teaching, while also providing a comprehensive discussion on the implications for pre-service teacher education and practice
Optimal Site Selection for Green Hydrogen Production Plants Based on Solar Energy in Konya/Türkiye
No full textThe combination of solar energy and hydrogen technologies has a critical role in accelerating the energy transition and reducing carbon emissions. This study identifies optimal sites for green hydrogen production in Konya, a region with high solar energy potential in T ; uuml;rkiye. Within the scope of the research, a multi-criteria approach covering technical, economic and environmental sensitivities has been adopted. These criteria were prioritised according to the expert opinions by using Best-Worst Method (BWM), a Multi criteria Decision-Making (MCDM) method. By using Geographic Information System (GIS) spatial analysis tools, the prioritised criteria were combined according to their weights and suitable and unsuitable areas were revealed. The results showed that 7.10% (275858 ha) of the study area is very suitable for constructing green hydrogen production plant based on solar energy, especially in Konya city centre, Karapinar, Eregli, Beys,ehir and Seydis,ehir districts. Konya province's large solar energy potential, existing energy infrastructure and proximity to industrial zones make this region a strategic centre for green hydrogen production. This study provides important findings to strengthen T ; uuml;rkiye's strategic position in renewable energy-based hydrogen production and contribute to regional sustainable development
Measurement of the Higgs Boson Mass and Width Using the Four-Lepton Final State in Proton-Proton Collisions at (Formula Presented)
No full textWhalen, Kathleen/0000-0002-9383-8763; Trevisani, Nicolo/0000-0002-5223-9342; Al Kadhim, Ali/0000-0003-3490-8407; Hall, Geoffrey/0000-0002-6299-8385; Csanad, Mate/0000-0002-3154-6925; Ventura, Sandro/0000-0002-8938-2193; Sahasransu, Abanti Ranadhir/0000-0003-1505-1743; Singh, Jasbir/0000-0001-9029-2462; Cussans, David/0000-0001-8192-0826; Khvedelidze, Arsen/0000-0002-5953-0140; D'Enterria, David/0000-0002-5754-4303; Moon, Chang-Seong/0000-0001-8229-7829; Vannerom, David/0000-0002-2747-5095; Tiras, Emrah/0000-0002-5628-7464; Hussain, Priya Sajid/0000-0002-4825-5278; Wittich, Peter/0000-0002-7401-2181; Jin, Weijie/0009-0009-8976-7702; Vischia, Pietro/0000-0002-7088-8557; Garcia, Francisco/0000-0002-4023-7964; Lai, Yihui/0000-0002-7795-8693; Gerosa, Raffaele/0000-0001-8359-3734; Zhang, Licheng/0000-0001-7947-9007; Vami, Tamas Almos/0000-0002-0959-9211;A measurement of the Higgs boson mass and width via its decay to two Z bosons is presented. Proton-proton collision data collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb(-1) at a center-of-mass energy of 13 TeV, is used. The invariant mass distribution of four leptons in the on-shell Higgs boson decay is used to measure its mass and constrain its width. This yields the most precise single measurement of the Higgs boson mass to date, 125.04 +/- 0.12 GeV, and an upper limit on the width Gamma(H) 330 MeV at 95% confidence level. A combination of the on- and off-shell Higgs boson production decaying to four leptons is used to determine the Higgs boson width, assuming that no new virtual particles affect the production, a premise that is tested by adding new heavy particles in the gluon fusion loop model. This result is combined with a previous CMS analysis of the off-shell Higgs boson production with decay to two leptons and two neutrinos, giving a measured Higgs boson width of 3.0(-1.5)(+2.0) MeV, in agreement with the standard model prediction of 4.1 MeV. The strength of the off-shell Higgs boson production is also reported. The scenario of no off-shell Higgs boson production is excluded at a confidence level corresponding to 3.8 standard deviations.FWF; FNRS; FWO (Belgium) [30820817]; CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF (USA); Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation `a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); F. R. S.-FNRS; Beijing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [Strategy-EXC 2121, 400140256-GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences; NKFIH [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund [MCIN/AEI/10.13039/501100011033]; ERDF "a way of making Europe; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project; National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA)We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, Contracts No. 675440, No. 724704, No. 752730, No. 758316, No. 765710, No. 824093, No. 101115353, No. 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, Project No. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation `a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the F. R. S.-FNRS and FWO (Belgium) under the "Excellence of Science-EOS"-be.h Project No. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, Grant No. FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany's Excellence Strategy-EXC 2121 "Quantum Universe"-390833306, and under Project No. 400140256-GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project No. 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program-UNKP, the NKFIH research Grants No. K 131991, No. K 133046, No. K 138136, No. K 143460, No. K 143477, No. K 146913, No. K 146914, No. K 147048, No. 2020-2.2.1-ED-2021-00181, and No. TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC-National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR-Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, Project No. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, Grant No. CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe," and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant No. MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, Grant No. B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, Contract C-1845; and the Weston Havens Foundation (USA)
Study of Wh Production Through Vector Boson Scattering and Extraction of the Relative Sign of the W and Z Couplings To the Higgs Boson in Proton-Proton Collisions at √s=13 Te
No full textA search for the production of a W boson and a Higgs boson through vector boson scattering (VBS) is presented, using CMS data from proton-proton collisions at root s = 13 TeV collected from 2016 to 2018. The integrated luminosity of the data sample is 138 fb(-1). Selected events must be consistent with the presence of two jets originating from VBS, the leptonic decay of the W boson to an electron or muon, possibly also through an intermediate tau lepton, and a Higgs boson decaying into a pair of b quarks, reconstructed as either a single merged jet or two resolved jets. A measurement of the process as predicted by the standard model (SM) is performed alongside a study of beyond-the-SM (BSM) scenarios. The SM analysis sets an observed (expected) 95% confidence level upper limit of 14.3 (9.9) on the ratio of the measured VBS WH cross section to that expected by the SM. The BSM analysis, conducted within the so-called kappa framework, excludes all scenarios with lambda(WZ) 0 that are consistent with current measurements, where lambda(WZ) = kappa(W)/kappa(Z) and kappa W and kappa(Z) are the HWW and HZZ coupling modfiers, respectively. The significance of the exclusion is beyond 5 standard deviations, and it is consistent with the SM expectation of lambda(WZ) = 1.We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MOST, and NSFC (China); Minciencias (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, Conahcyt, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MoSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; The Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science - OS'' -be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); The Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR22985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 "Quantum Universe''-390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program -UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC - National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR - Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe'', and the Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the Super-Micro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).FWF; FNRS; FWO (Belgium) [30820817]; CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MOST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; NASU (Ukraine); NSF (USA); Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); Beijing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 400140256 -GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF "a way of making Europe [MDM-2017-0765]; National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B37G660013]; Kavli Foundation; Nvidia Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA