series of data samples was collected with the Belle Ⅱ detector at the SuperKEKB collider from March 2019 to June 2022.We determine the integrated luminosities of these data samples using three distinct methodologies ...series of data samples was collected with the Belle Ⅱ detector at the SuperKEKB collider from March 2019 to June 2022.We determine the integrated luminosities of these data samples using three distinct methodologies involving Bhabha(e^(+)e^(-)→e^(+)e^(-)(ny),digamma(e^(+)e^(-)→γγ(nγ),and dimuon(e^(+)e^(-)→μ^(+)μ^(-)(nγ)events.The total integrated luminosity obtained with Bhabha,digamma,and dimuon events is(426.88±0.03±2.61)fb^(-1),(429.28±0.03±2.62)fb^(-1),and(423.99±0.04±3.83)fb^(-1),where the first uncertainties are statistical and the second are systematic.The resulting total integrated luminosity obtained from the combination of the three methods is(427.87±2.01)fb^(-1).展开更多
From April to July 2018,a data sample at the peak energy of the T(4 S) resonance was collected with the Belle Ⅱ detector at the SuperKEKB electron-positron collider.This is the first data sample of the Belle Ⅱ exper...From April to July 2018,a data sample at the peak energy of the T(4 S) resonance was collected with the Belle Ⅱ detector at the SuperKEKB electron-positron collider.This is the first data sample of the Belle Ⅱ experiment.Using Bhabha and digamma events,we measure the integrated luminosity of the data sample to be(496.3±0.3±3.0) pb-1,where the first uncertainty is statistical and the second is systematic.This work provides a basis for future luminosity measurements at Belle Ⅱ.展开更多
X-ray absorption near-edge structure(XANES)spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom.However,the quantitative analysis of these spectra is not straightforward....X-ray absorption near-edge structure(XANES)spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom.However,the quantitative analysis of these spectra is not straightforward.Even with the most recent advances in this area,for a given spectrum,it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated.Here,we present an alternative concept for the analysis of XANES spectra,which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra,such as edge position,intensities,positions,and curvatures of minima and maxima on the one hand,and those related to the local atomic and electronic structure which are the coordination numbers,bond distances and angles and oxidation state on the other hand.This approach overcoms the problem of the systematic difference between theoretical and experimental spectra.Furthermore,the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape.The methodology was successfully applied to experimental data for the multicomponent Fe:SiO_(2)system and reference iron compounds,demonstrating the high prediction quality for both the theoretical validation sets and experimental data.展开更多
基金supported by Higher Education and Science Committee of the Republic of Armenia(23LCG-1C011)Australian Research Council and Research(DP200101792,DP210101900,DP210102831,DE220100462,LE210100098,LE230100085)+41 种基金Austrian Federal Ministry of Education,Science and Research,Austrian Science Fund(P 34529,J 4731,J 4625,M 3153)Horizon 2020 ERC Starting(947006)“InterLeptons”Natural Sciences and Engineering Research Council of Canada,Compute Canada and CANARIENational Key R&D Program of China(2022YFA1601903)National Natural Science Foundation of China(11575017,11761141009,11705209,11975076,12135005,12150004,12161141008,12175041)Natural Science Foundation Project of Shandong Province,China(ZR2022JQ02)the Czech Science Foundation(22-18469S)and Charles University Grant Agency(246122)European Research Council,Seventh Framework(PIEF-GA-2013-622527)Horizon 2020 ERC-Advanced(Grant Nos.267104 and 884719)Horizon 2020 ERC-Consolidator(819127)Horizon 2020 Marie Sklodowska-Curie Grant Agreement(700525)“NIOBE”and(101026516)Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 Grant Agreement(822070)(European grants)L'Institut National de Physique Nucléaire et de Physique des Particules(IN2P3)du CNRS and L'Agence Nationale de la Recherche(ANR)(ANR-21-CE31-0009)(France)BMBF,DFG,HGF,MPG,and AvH Foundation(Germany)Department of Atomic Energy under Project Identification(RTI 4002)Department of Science and Technology,and UPES SEED funding programs(UPES/R&D-SEED-INFRA/17052023/01,UPES/R&D-SOE/20062022/06)(India)Israel Science Foundation(2476/17)U.S.-Israel Binational Science Foundation(2016113)Israel Ministry of Science(3-16543)Istituto Nazionale di Fisica Nucleare and the Research Grants BELLE2Japan Society for the Promotion of Science,Grant-in-Aid for Scientific Research(16H03968,16H03993,16H06492,16K05323,17H01133,17H05405,18K03621,18H03710,18H05226,19H00682,20H05850,20H05858,22H00144,22K14056,22K21347,23H05433,26220706,26400255)the Ministry of Education,Culture,Sports,Science,and Technology(MEXT)of JapanNational Research Foundation(NRF)of Korea(2016R1D1A1B02012900,2018R1A2B3003643,2018R1A6A1A06024970,2019R1I1A3A01058933,2021R1A6A1A03043957,2021R1F1A1060423,2021R1F1A1064008,2022R1A2C1003993,RS-2022-00197659)Radiation Science Research Institute,Foreign Large-Size Research Facility Application Supporting project,the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIADUniversiti Malaya RU grant,Akademi Sains Malaysia,and Ministry of Education MalaysiaFrontiers of Science Program(FOINS-296,CB-221329,CB-236394,CB-254409,CB-180023)SEP-CINVESTAV Research(237)(Mexico)the Polish Ministry of Science and Higher Education and the National Science Centerthe Ministry of Science and Higher Education of the Russian Federation and the HSE University Basic Research Program,MoscowUniversity of Tabuk Research(S-0256-1438,S-0280-1439)(Saudi Arabia)Slovenian Research Agency and Research(J1-9124,P1-0135)Agencia Estatal de Investigacion,Spain(RYC2020-029875-I)Generalitat Valenciana,Spain(CIDEGENT/2018/020)The Knut and Alice Wallenberg Foundation(Sweden),(2021.0174,2021.0299)National Science and Technology Council,and Ministry of EducationThailand Center of Excellence in PhysicsTUBITAK ULAKBIM(Turkey)National Research Foundation of Ukraine,(2020.02/0257)Ministry of Education and Science of Ukrainethe U.S.National Science Foundation and Research(PHY-1913789,PHY-2111604)the U.S.Department of Energy and Research Awards(DE-AC06-76RLO1830,DE-SC0007983,DESC0009824,DE-SC0009973,DE-SC0010007,DE-SC0010073,DE-SC0010118,DE-SC0010504,DE-SC0011784,DE-SC0012704,DE-SC0019230,DESC0021274,DE-SC0021616,DE-SC0022350,DE-SC0023470)the Vietnam Academy of Science and Technology(VAST)(NVCC.05.12/22-23,DL0000.02/24-25)。
文摘series of data samples was collected with the Belle Ⅱ detector at the SuperKEKB collider from March 2019 to June 2022.We determine the integrated luminosities of these data samples using three distinct methodologies involving Bhabha(e^(+)e^(-)→e^(+)e^(-)(ny),digamma(e^(+)e^(-)→γγ(nγ),and dimuon(e^(+)e^(-)→μ^(+)μ^(-)(nγ)events.The total integrated luminosity obtained with Bhabha,digamma,and dimuon events is(426.88±0.03±2.61)fb^(-1),(429.28±0.03±2.62)fb^(-1),and(423.99±0.04±3.83)fb^(-1),where the first uncertainties are statistical and the second are systematic.The resulting total integrated luminosity obtained from the combination of the three methods is(427.87±2.01)fb^(-1).
基金supported by the following funding sources:Science Committee of the Republic of Armenia Grant No.18T-1C180Australian Research Council and research grant Nos.DP180102629,DP170102389,DP170102204,DP150103061,FT130100303,and FT130100018+37 种基金Austrian Federal Ministry of Education,Science and Research,and Austrian Science Fund No.P 31361-N36Natural Sciences and Engineering Research Council of Canada,Compute Canada and CANARIEChinese Academy of Sciences and research grant No.QYZDJ-SSW-SLH011National Natural Science Foundation of China and research grant Nos.11521505,11575017,11675166,11761141009,11705209,and 11975076LiaoNing Revitalization Talents Program under contract No.XLYC1807135Shanghai Municipal Science and Technology Committee under contract No.19ZR1403000Shanghai Pujiang Program under Grant No.18PJ1401000the CAS Center for Excellence in Particle Physics(CCEPP)the Ministry of Education,Youth and Sports of the Czech Republic under Contract No.LTT17020Charles University grants SVV260448 and GAUK 404316European Research Council,7th Framework PIEF-GA-2013-622527Horizon 2020 Marie Sklodowska-Curie grant agreement No.700525’NIOBE,’Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER grant agreement No.644294Horizon 2020 ERC-Advanced Grant No.267104NewAve No.638528(European grants)L’Institut National de Physique Nucléaire et de Physique des Particules(IN2P3)du CNRS(France),BMBF,DFG,HGF,MPG and AvH Foundation(Germany)Department of Atomic Energy and Department of Science and Technology(India)Israel Science Foundation grant No.2476/17United States-Israel Binational Science Foundation grant No.2016113Istituto Nazionale di Fisica Nucleare and the research grants BELLE2Japan Society for the Promotion of Science,Grant-in-Aid for Scientific Research grant Nos.16H03968,16H03993,16H06492,16K05323,17H01133,17H05405,18K03621,18H03710,18H05226,19H00682,26220706,and 26400255the National Institute of Informatics,and Science Information NETwork 5(SINET5)the Ministry of Education,Culture,Sports,Science,and Technology(MEXT)of JapanNational Research Foundation(NRF)of Korea Grant Nos.2016R1D1A1B01010135,2016R1D1A1B02012900,2018R1A2B3003643,2018R1A6A1A06024970,2018R1D1A1B07047294,2019K1A3A7A09033840,and 2019R1I1A3A01058933Radiation Science Research Institute,Foreign Large-size Research Facility Application Supporting project,the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIADUniversiti Malaya RU grant,Akademi Sains Malaysia and Ministry of Education MalaysiaFrontiers of Science Program contracts FOINS-296,CB-221329,CB-236394,CB-254409,and CB-180023,and the Thematic Networks program(Mexico)the Polish Ministry of Science and Higher Education and the National Science Centerthe Ministry of Science and Higher Education of the Russian Federation,Agreement14.W03.31.0026Slovenian Research Agency and research grant Nos.J1-9124 and P1-0135Agencia Estatal de Investigacion,Spain grant Nos.FPA2014-55613-P and FPA2017-84445-P,and CIDEGENT/2018/020 of Generalitat ValencianaMinistry of Science and Technology and research grant Nos.MOST106-2112-M-002-005-MY3 and MOST107-2119-M-002-035-MY3,and the Ministry of Education(Taiwan)Thailand Center of Excellence in PhysicsTUBITAK ULAKBIM(Turkey)Ministry of Education and Science of Ukrainethe US National Science Foundation and research grant Nos.PHY-1807007 and PHY-1913789the US Department of Energy and research grant Nos.DE-AC06-76RLO1830,DE-SC0007983,DE-SC0009824,DE-SC0009973,DE-SC0010073,DE-SC0010118,DE-SC0010504,DESC0011784,DE-SC0012704the National Foundation for Science and Technology Development(NAFOSTED)of Vietnam under grant No 103.99-2018.45
文摘From April to July 2018,a data sample at the peak energy of the T(4 S) resonance was collected with the Belle Ⅱ detector at the SuperKEKB electron-positron collider.This is the first data sample of the Belle Ⅱ experiment.Using Bhabha and digamma events,we measure the integrated luminosity of the data sample to be(496.3±0.3±3.0) pb-1,where the first uncertainty is statistical and the second is systematic.This work provides a basis for future luminosity measurements at Belle Ⅱ.
基金A.Guda acknowledges the financial support from the Russian Foundation for Basic Research(project number 20-32-70227)for the work on the multicomponent mixtures.A.Bugaev and A.V.Soldatov acknowledge the Russian Science Foundation grant#20-43-01015 for the financial support for the work on the spectral descriptors.Authors acknowledge D.D.Badyukov from Vernadsky Institute of Geochemistry and Analytical Chemistry of Russian Academy of Sciences for providing samples for analysis.P.Šot acknowledges the Shell Global Solutions International,B.V.for funding the work on the synthesis of Fe-containing catalyst,and European Synchrotron Research Facility for awarded beamtimes at beamlines ID26,BM25,and Swiss Light Source for the beamtime at SuperXAS beamline.
文摘X-ray absorption near-edge structure(XANES)spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom.However,the quantitative analysis of these spectra is not straightforward.Even with the most recent advances in this area,for a given spectrum,it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated.Here,we present an alternative concept for the analysis of XANES spectra,which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra,such as edge position,intensities,positions,and curvatures of minima and maxima on the one hand,and those related to the local atomic and electronic structure which are the coordination numbers,bond distances and angles and oxidation state on the other hand.This approach overcoms the problem of the systematic difference between theoretical and experimental spectra.Furthermore,the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape.The methodology was successfully applied to experimental data for the multicomponent Fe:SiO_(2)system and reference iron compounds,demonstrating the high prediction quality for both the theoretical validation sets and experimental data.
