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Methods for a blind analysis of isobar data collected by the STAR collaboration 被引量:9
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作者 J.Adam L.Adamczyk +366 位作者 J.R.Adams J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderon de la Barca Sanchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanad M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal X.Gou D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He Y.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.Ji J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Md.Nasim K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pinter J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh W.Q.Shen S.S.Shi Y.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip Y.Yu H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak STAR Collaboration Abilene 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2021年第5期43-50,共8页
In 2018,the STAR collaboration collected data from^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr at√^(S)NN=200 Ge V to search for the presence of the chiral magnetic effect in collisions of nuclei.The isobar ... In 2018,the STAR collaboration collected data from^(96)_(44)Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr at√^(S)NN=200 Ge V to search for the presence of the chiral magnetic effect in collisions of nuclei.The isobar collision species alternated frequently between 9644 Ru+^(96)_(44)Ru and^(96)_(40)Zr+^(96)_(40)Zr.In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data,STAR developed a three-step blind analysis procedure.Analysts are initially provided a"reference sample"of data,comprised of a mix of events from the two species,the order of which respects time-dependent changes in run conditions.After tuning analysis codes and performing time-dependent quality assurance on the reference sample,analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual≈30-min data-taking runs.For this sample,species-specific information is disguised,but individual output files contain data from a single isobar species.Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage.Following these modifications,the"frozen"code is passed over the fully un-blind data,completing the blind analysis.As a check of the feasibility of the blind analysis procedure,analysts completed a"mock data challenge,"analyzing data from Au+Au collisions at√^(S)NN=27 Ge V,collected in 2018.The Au+Au data were prepared in the same manner intended for the isobar blind data.The details of the blind analysis procedure and results from the mock data challenge are presented. 展开更多
关键词 Blind analysis Chiral magnetic effect Heavy-ion collisions
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Frequency Gradient with Respect to Temperature for Determination of Classification of the Phase Response Curve
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作者 Yasuomi D.Sato 《Chinese Physics Letters》 SCIE CAS CSCD 2013年第12期170-173,共4页
The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dyn... The interesting task here is to study the frequency-current(f–I)curve and phase response curve(PRC),subject to neural temperature variations,because the f–I curve and PRC are important measurements to understand dynamical mechanisms of generation of neural oscillations,and the neural temperature is widely known to significantly affect the oscillations.Nevertheless,little is discussed about how the temperature affects the f–I curve and PRC.In this study,frequencies of the neural oscillations,modulated with the temperature variations,are quantified with an average of the PRC.The frequency gradient with respect to temperature derived here gives clear classifications of the PRC,regardless of the form.It is also indicated that frequency decreases with an increase in temperature,resulted from bifurcation switching of the saddle-homoclinic to the saddle-node on an invariant circle. 展开更多
关键词 FORM SADDLE INVARIANT
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An Optimal Power-Law for Synchrony and Lognormally Synaptic Weighted Hub Networks
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作者 Yasuomi D.Sato 《Chinese Physics Letters》 SCIE CAS CSCD 2013年第9期190-193,共4页
Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network m... Details about the structure of a network model are revealed at the spontaneous spike activity level,in which the power-law of synchrony is optimized to that observed in the CA3 hippocampal slice cultures.The network model is subject to spike noise with exponentially distributed interspike intervals.The excitatory(E)and/or inhibitory(I)neurons interact through synapses whose weights show a log-normal distribution.The spike behavior observed in the network model with the appropriate log-normal distributed synaptic weights fits best to that observed in the experiment.The best-fit is then achieved with high activities of I neurons having a hub-like structure,in which the I neurons,subject to optimized spike noise,are intensively projected from low active E neurons. 展开更多
关键词 app WEIGHTS NETWORK
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Changes in Repetitive Firing Rate Related to Phase Response Curves for Andronov-Hopf Bifurcations
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作者 Yasuomi D. Sato 《Chinese Physics Letters》 SCIE CAS CSCD 2014年第5期13-16,共4页
We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose (2dHR) oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf (All) type to the subcr... We study specific changes in repetitive firing in the two-dimensional Hindmarsh-Rose (2dHR) oscillatory sys- tem that undergoes a bifurcation transition from the supercritical Andronov-Hopf (All) type to the subcritical Andronov-Hopf (SAH) type. We identify dynamical mechanisms which are responsible for changes of the repeti- tive firing rate during the AH to SAH bifurcation transitions. These include frequency-shift functions in response to small perturbations of a timescale parameter, its multiplicative parameter, and an external input current in the 2dHR oscillatory system. The frequency-shift functions are explicitly represented as functions relating to the phase response curves (PRCs). Then, we demonstrate that when the timescale is normal and relatively fast, the repetitive firing rate slightly increases and decreases respectively during the AH to SAH bifurcation transition with a change of the intrinsic parameter, whereas it decreases during the SAH to AH bifurcation transition with an increase in the timescale. By analyzing the three different frequency-shift functions, we show that such changes of the repetitive firing rate depend largely on changes of the PRC size. The PRC size for the SAH bifurcation shrinks to the PRC size for the AH bifurcation. 展开更多
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Phase Transition Study Meets Machine Learning 被引量:14
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作者 马余刚 庞龙刚 +1 位作者 王睿 周凯 《Chinese Physics Letters》 SCIE EI CAS CSCD 2023年第12期34-39,共6页
In recent years, machine learning(ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a con... In recent years, machine learning(ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies. 展开更多
关键词 TRANSITIONS TRANSITION APPLYING
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Why Deep Neural Nets Cannot Ever Match Biological Intelligence and What To Do About It?
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作者 Danko Nikolic 《International Journal of Automation and computing》 EI CSCD 2017年第5期532-541,共10页
The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure... The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artifidal intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks. 展开更多
关键词 Artificial intelligence neural networks strong artificial intelligence practopoiesis machine learning
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On the Perturbation Correction Factor <i>p<sub>cav</sub></i>of the Markus Parallel-Plate Ion Chamber in Clinical Electron Beams
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作者 Philip von Voigts-Rhetz Hilke Vorwerk Klemens Zink 《International Journal of Medical Physics, Clinical Engineering and Radiation Oncology》 2017年第2期150-161,共12页
Purpose: All present dosimetry protocols recommend well-guarded parallelplate ion chambers for electron dosimetry. For the guard-less Markus chamber, an energy dependent fluence perturbation correction pcav is given. ... Purpose: All present dosimetry protocols recommend well-guarded parallelplate ion chambers for electron dosimetry. For the guard-less Markus chamber, an energy dependent fluence perturbation correction pcav is given. This perturbation correction was experimentally determined by van der Plaetsen by comparison of the read-out of a Markus and a NACP chamber, which was assumed to be “perturbation-free”. Aim of the present study is a Monte Carlo based reiteration of this experiment. Methods: Detailed models of four parallel-plate chambers (Roos, Markus, NACP and Advanced Markus) were designed using the Monte Carlo code EGSnrc and placed in a water phantom. For all chambers, the dose to the active volume filled with low density water was calculated for 13 clinical electron spectra (E0 = 6 - 21 MeV) and three energies of an Electra linear accelerator at the depth of maximum and at the reference depth under reference conditions. In all cases, the chamber’s reference point was positioned at the depth of measurement. Moreover, the dose to water DW was calculated in a small water voxel positioned at the same depth. Results: The calculated dose ratio DNACP/DMarkus, which according to van der Plaetsen reflects the fluence perturbation correction of the Markus chamber, deviates less from unity than the values given by van der Plaetsen, but exhibits similar energy dependence. The same holds for the dose ratios of the other well-guarded chambers. But, in comparison to water, the Markus chamber reveals the smallest overall perturbation correction which is nearly energy independent at both investigated depths. Conclusion: The simulations principally confirm the energy dependence of the dose ratio DNACP/DMarkus as published by van der Plaetsen. But, as shown by our simulations of the ratio DW/DMarkus, the conclusion drawn in all dosimetry protocols is questionable: in contrast to all well-guarded chambers, the guard-less Markus chamber reveals the smallest overall perturbation correction and also the smallest energy dependence. 展开更多
关键词 EGSNRC Monte Carlo Cavity PERTURBATION Ionization CHAMBER
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Measurement of away-side broadening with self-subtraction of flow in Au+Au collisions at √sNN=200 GeV 被引量:2
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作者 L.Adamczyk J.R.Adams +359 位作者 J.K.Adkins G.Agakishiev M.M.Aggarwal Z.Ahammed I.Alekseev D.M.Anderson A.Aparin E.C.Aschenauer M.U.Ashraf F.G.Atetalla A.Attri G.S.Averichev V.Bairathi K.Barish A.Behera R.Bellwied A.Bhasin J.Bielcik J.Bielcikova L.C.Bland I.G.Bordyuzhin J.D.Brandenburg A.V.Brandin J.Butterworth H.Caines M.Calderón de la Barca Sánchez D.Cebra I.Chakaberia P.Chaloupka B.K.Chan F-H.Chang Z.Chang N.Chankova-Bunzarova A.Chatterjee D.Chen J.H.Chen X.Chen Z.Chen J.Cheng M.Cherney M.Chevalier S.Choudhury W.Christie X.Chu H.J.Crawford M.Csanád M.Daugherity T.G.Dedovich I.M.Deppner A.A.Derevschikov L.Didenko X.Dong J.L.Drachenberg J.C.Dunlop T.Edmonds N.Elsey J.Engelage G.Eppley S.Esumi O.Evdokimov A.Ewigleben O.Eyser R.Fatemi S.Fazio P.Federic J.Fedorisin C.J.Feng Y.Feng P.Filip E.Finch Y.Fisyak A.Francisco L.Fulek C.A.Gagliardi T.Galatyuk F.Geurts A.Gibson K.Gopal D.Grosnick W.Guryn A.I.Hamad A.Hamed S.Harabasz J.W.Harris S.He W.He X.H.He S.Heppelmann S.Heppelmann N.Herrmann E.Hoffman L.Holub Y.Hong S.Horvat Y.Hu H.Z.Huang S.L.Huang T.Huang X.Huang T.J.Humanic P.Huo G.Igo D.Isenhower W.W.Jacobs C.Jena A.Jentsch Y.JI J.Jia K.Jiang S.Jowzaee X.Ju E.G.Judd S.Kabana M.L.Kabir S.Kagamaster D.Kalinkin K.Kang D.Kapukchyan K.Kauder H.W.Ke D.Keane A.Kechechyan M.Kelsey Y.V.Khyzhniak D.P.Kikoła C.Kim B.Kimelman D.Kincses T.A.Kinghorn I.Kisel A.Kiselev M.Kocan L.Kochenda L.K.Kosarzewski L.Kramarik P.Kravtsov K.Krueger N.Kulathunga Mudiyanselage L.Kumar S.Kumar R.Kunnawalkam Elayavalli J.H.Kwasizur R.Lacey S.Lan J.M.Landgraf J.Lauret A.Lebedev R.Lednicky J.H.Lee Y.H.Leung C.Li W.Li W.Li X.Li Y.Li Y.Liang R.Licenik T.Lin Y.Lin M.A.Lisa F.Liu H.Liu P.Liu P.Liu T.Liu X.Liu Y.Liu Z.Liu T.Ljubicic W.J.Llope R.S.Longacre N.S.Lukow S.Luo X.Luo G.L.Ma L.Ma R.Ma Y.G.Ma N.Magdy R.Majka D.Mallick S.Margetis C.Markert H.S.Matis J.A.Mazer N.G.Minaev S.Mioduszewski B.Mohanty I.Mooney Z.Moravcova D.A.Morozov M.Nagy J.D.Nam Nasim Md K.Nayak D.Neff J.M.Nelson D.B.Nemes M.Nie G.Nigmatkulov T.Niida L.V.Nogach T.Nonaka A.S.Nunes G.Odyniec A.Ogawa S.Oh V.A.Okorokov B.S.Page R.Pak A.Pandav Y.Panebratsev B.Pawlik D.Pawlowska H.Pei C.Perkins L.Pinsky R.L.Pintér J.Pluta J.Porter M.Posik N.K.Pruthi M.Przybycien J.Putschke H.Qiu A.Quintero S.K.Radhakrishnan S.Ramachandran R.L.Ray R.Reed H.G.Ritter O.V.Rogachevskiy J.L.Romero L.Ruan J.Rusnak N.R.Sahoo H.Sako S.Salur J.Sandweiss S.Sato W.B.Schmidke N.Schmitz B.R.Schweid F.Seck J.Seger M.Sergeeva R.Seto P.Seyboth N.Shah E.Shahaliev P.V.Shanmuganathan M.Shao A.I.Sheikh F.Shen W.Q.Shen S.S.Shi Q.Y.Shou E.P.Sichtermann R.Sikora M.Simko J.Singh S.Singha N.Smirnov W.Solyst P.Sorensen H.M.Spinka B.Srivastava T.D.S.Stanislaus M.Stefaniak D.J.Stewart M.Strikhanov B.Stringfellow A.A.P.Suaide M.Sumbera B.Summa X.M.Sun X.Sun Y.Sun Y.Sun B.Surrow D.N.Svirida P.Szymanski A.H.Tang Z.Tang A.Taranenko T.Tarnowsky J.H.Thomas A.R.Timmins D.Tlusty M.Tokarev C.A.Tomkiel S.Trentalange R.E.Tribble P.Tribedy S.K.Tripathy O.D.Tsai Z.Tu T.Ullrich D.G.Underwood I.Upsal G.Van Buren J.Vanek A.N.Vasiliev I.Vassiliev F.Videbæk S.Vokal S.A.Voloshin F.Wang G.Wang J.S.Wang P.Wang Y.Wang Y.Wang Z.Wang J.C.Webb P.C.Weidenkaff L.Wen G.D.Westfall H.Wieman S.W.Wissink R.Witt Y.Wu Z.G.Xiao G.Xie W.Xie H.Xu N.Xu Q.H.Xu Y.F.Xu Y.Xu Z.Xu Z.Xu C.Yang Q.Yang S.Yang Y.Yang Z.Yang Z.Ye Z.Ye L.Yi K.Yip H.Zbroszczyk W.Zha C.Zhang D.Zhang S.Zhang S.Zhang X.P.Zhang Y.Zhang Y.Zhang Z.J.Zhang Z.Zhang Z.Zhang J.Zhao C.Zhong C.Zhou X.Zhu Z.Zhu M.Zurek M.Zyzak 《Chinese Physics C》 SCIE CAS CSCD 2020年第10期59-67,共9页
High transverse momentum(pT)particle production is suppressed owing to the parton(jet)energy loss in the hot dense medium created in relativistic heavy-ion collisions.Redistribution of energy at low-to-modest pT has b... High transverse momentum(pT)particle production is suppressed owing to the parton(jet)energy loss in the hot dense medium created in relativistic heavy-ion collisions.Redistribution of energy at low-to-modest pT has been difficult to measure,owing to large anisotropic backgrounds.We report a data-driven method for background evaluation and subtraction,exploiting the away-side pseudorapidity gaps,to measure the jetlike correlation shape in Au+Au collisions at √sNN=200 GeV in the STAR experiment.The correlation shapes,for trigger particles pT>3GeV/c and various associated particle pT ranges within 0.5<pT<10GeV/c,are consistent with Gaussians,and their widths increase with centrality.The results indicate jet broadening in the medium created in central heavy-ion collisions. 展开更多
关键词 di-hadron correlations jet HEAVY-ION
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