An interview with Chas W.Freeman,diplomat,author,and writer对话美国外交家、作家傅立民PIONEER PROFILE Chas W.Freeman Profession:Diplomat,author,and writer Nationality:AmericanThe 1970s were heady days for Sino-US diplo...An interview with Chas W.Freeman,diplomat,author,and writer对话美国外交家、作家傅立民PIONEER PROFILE Chas W.Freeman Profession:Diplomat,author,and writer Nationality:AmericanThe 1970s were heady days for Sino-US diplomacy,with Nixon becoming the first American President to visit China.They were nervous days too,especially for American diplomat Chas W.Freeman who was展开更多
The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture ...The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark–gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs.展开更多
文摘An interview with Chas W.Freeman,diplomat,author,and writer对话美国外交家、作家傅立民PIONEER PROFILE Chas W.Freeman Profession:Diplomat,author,and writer Nationality:AmericanThe 1970s were heady days for Sino-US diplomacy,with Nixon becoming the first American President to visit China.They were nervous days too,especially for American diplomat Chas W.Freeman who was
基金supported by the National Natural Science Foundation of China (Grant Nos.12147101 and 12325507)the National Key Research and Development Program of China (Grant No.2022YFA1604900)+4 种基金the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2020B0301030008 for S.G.and G.M.)the CUHK-Shenzhen university development fund (Grant Nos.UDF01003041 and UDF03003041)Shenzhen Peacock Fund (Grant No.2023TC0179 for K.Z.)the RIKEN TRIP initiative (RIKEN Quantum),JSPS KAKENHI (Grant No.25H01560)JST-BOOST (Grant No.JPMJBY24H9 for L.W.)。
文摘The search for the chiral magnetic effect(CME) in relativistic heavy-ion collisions(HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark–gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs.
