Galaxy cluster characterization with machine learning techniques
Maria Sadikov
Julie Hlavacek-larrondo
Laurence Perreault-Levasseur
C. Rhea
Michael McDonald
Michelle Ntampaka
John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Galaxy cluster characterization with machine learning techniques
Maria Sadikov
Julie Hlavacek-larrondo
Laurence Perreault-Levasseur
C. Rhea
Michael McDonald
Michelle Ntampaka
John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Galaxy cluster characterization with machine learning techniques
Maria Sadikov
Julie Hlavacek-larrondo
Laurence Perreault-Levasseur
C. Rhea
Michael McDonald
Michelle Ntampaka
John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
L’appréhension empirique du leadership normatif d’une organisation internationale : l’exemple de l’Organisation mondiale de la Santé
Catherine Régis
Gaelle Foucault
Pierre Larouche
Jean-Louis Denis
Miriam Cohen
En plein essor, la recherche empirique en droit participe à la création de nouvelles connaissances et ouvre aux juristes d’autres voies … (voir plus)pour étudier une question, un phénomène. Oser l’empirisme n’est pas chose aisée, mais les auteurs du présent article ont pris ce virage et proposent d’en exposer le récit. En construisant deux méthodes distinctes (pour deux projets), ils ont pu tester les possibilités qu’offre la recherche empirique pour appréhender l’enjeu du leadership normatif de l’Organisation mondiale de la Santé (OMS). Destiné à aiguiller à partir d’une expérience celles et ceux qui voudraient s’aventurer dans l’empirisme, cet article met en lumière les défis rencontrés, mais surtout les atouts d’une telle recherche. La richesse des informations obtenues a en effet grandement bonifié la compréhension de la trajectoire des normes de l’OMS et de leurs impacts sur les États.
2025-01-07
Lex Electronica (publié)
doi.org
Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Thomas Renne
Worrawat Engchuan
Martineau Jean-Louis
Jakub Kopal
Zohra Saci
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Josephine Mollon
Laura Schultz
Emma E M Knowles
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris
Simon R. Cox
Gunter Schumann … (voir 9 de plus)
Zdenka Pausova
Celia M. T. Greenwood
Tomas Paus
Stephen W Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Guillaume Dumas
Sébastien Jacquemont
Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes
2025-01-07
bioRxiv (prépublication)
doi.org
Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Thomas Renne
Worrawat Engchuan
Martineau Jean-Louis
Jakub Kopal
Zohra Saci
Omar Shanta
Bhooma Thiruvahindrapuram
Jeffrey R. MacDonald
Josephine Mollon
Laura Schultz
Emma E M Knowles
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris
Simon R. Cox
Gunter Schumann … (voir 9 de plus)
Zdenka Pausova
Celia M. T. Greenwood
Tomas Paus
Stephen W Scherer
Laura Almasy
Jonathan Sebat
David C. Glahn
Guillaume Dumas
Sébastien Jacquemont
Regulation of gene expression shapes the interaction between brain networks which in-turn supports psychological processes such as cognitive… (voir plus) ability. How changes in level of gene expression across the cerebral cortex influence cognitive ability remains unknown. Here, we tackle this by leveraging genomic deletions and duplications - copy number variants (CNVs) that fully encompass one or more genes expressed in the human cortex - which lead to large effects on gene-expression levels. We assigned genes to 180 regions of the human cerebral cortex based on their preferential expression across the cortex computed using data from the Allen Human Brain Atlas. We aggregated CNVs in cortical regions, and ran a burden association analysis to compute the mean effect size of genes on general cognitive ability for each of the 180 regions. When affected by CNVs, most of the regional gene-sets were associated with lower cognitive ability. The spatial patterns of effect sizes across the cortex were correlated negatively between deletions and duplications. The largest effect sizes for deletions and duplications were observed for gene-sets with high expression in sensorimotor and association regions, respectively. These two opposing patterns of effect sizes were not influenced by intolerance to loss of function, demonstrating orthogonality to dosage-sensitivity scores. The same mirror patterns were also observed after stratifying genes based on cell types and developmental epochs markers. These results suggest that the effect size of gene dosage on cognitive ability follows a cortical gradient. The same brain region and corresponding gene-set may show different effects on cognition depending on whether variants increase or decrease transcription. The latter has major implications for the association of brain networks with phenotypes
2025-01-07
bioRxiv (prépublication)
doi.org
The empirical apprehension of the normative leadership of an international organization: The example of the World Health Organization
Catherine Régis
Gaelle Foucault
Pierre Larouche
Jean-Louis Denis
Miriam Cohen
2025-01-07
Lex Electronica (publié)
doi.org
A video-based approach to decipher intubation decisions for the critically ill
Guillaume Dumas
Jean-Rémi Lavillegrand
Elie Azoulay
2025-01-07
Intensive Care Medicine (publié)
doi.org
Caffeine induces age-dependent increases in brain complexity and criticality during sleep
Philipp Thölke
Maxine Arcand-Lavigne
Tarek Lajnef
Sonia Frenette
Julie Carrier
Karim Jerbi
Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (voir plus) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during NREM sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) during REM sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.
2025-01-05
bioRxiv (prépublication)
doi.org
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
No'e Dia
M. J. Yantovski-Barth
Alexandre Adam
Micah Bowles
Laurence Perreault-Levasseur
Yashar Hezaveh
Anna M. M. Scaife
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
2025-01-05
ArXiv (prépublication)
doi.org
arxiv.org
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
No'e Dia
M. J. Yantovski-Barth
Alexandre Adam
Micah Bowles
Laurence Perreault-Levasseur
Yashar Hezaveh
Anna M. M. Scaife
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
2025-01-05
ArXiv (prépublication)
arxiv.org
arxiv.org
JUNO sensitivity to invisible decay modes of neutrons
Juno Collaboration Angel Abusleme
Angel Abusleme
Thomas Adam
Kai Adamowicz
Shakeel Ahmad
Rizwan Ahmed
Sebastiano Aiello
Fengpeng An
Qi An
Giuseppe Andronico
Nikolay Anfimov
Vito Antonelli
Tatiana Antoshkina
João Pedro Athayde Marcondes de André
Didier Auguste
Weidong Bai
Nikita Balashov
Wander Baldini
Andrea Barresi
Davide Basilico … (voir 670 de plus)
Eric Baussan
Marco Bellato
Marco Beretta
Antonio Bergnoli
Daniel Bick
Lukas Bieger
Svetlana Biktemerova
Thilo Birkenfeld
Iwan Blake
Simon Blyth
Anastasia Bolshakova
Mathieu Bongrand
Dominique Breton
Augusto Brigatti
Riccardo Brugnera
Riccardo Bruno
Antonio Budano
Jose Busto
Anatael Cabrera
Barbara Caccianiga
Hao Cai
Xiao Cai
Yanke Cai
Zhiyan Cai
Z. Cai
Stéphane Callier
Steven Calvez
Antonio Cammi
Agustin Campeny
Chuanya Cao
C. Cao
Guofu Cao
Jun Cao
Rossella Caruso
Cédric Cerna
Vanessa Cerrone
Jinfan Chang
Yun Chang
Auttakit Chatrabhuti
Chao Chen
Guoming Chen
Pingping Chen
Shaomin Chen
Xin Chen
Yiming Chen
Yixue Chen
Yu Chen
Zelin Chen
Zhangming Chen
Zhiyuan Chen
Zikang Chen
Jie Cheng
Yaping Cheng
Yu Chin Cheng
Yuanyuan Zhang
Alexander Chepurnov
Alexey Chetverikov
Davide Chiesa
Pietro Chimenti
Yen-Ting Chin
Po-Lin Chou
Ziliang Chu
Artem Chukanov
Gérard Claverie
Catia Clementi
Barbara Clerbaux
Marta Colomer Molla
Selma Conforti Di Lorenzo
Alberto Coppi
Daniele Corti
Simon Csakli
Chenyang Cui
Flavio Dal Corso
Olivia Dalager
Jaydeep Datta
Christophe De La Taille
C. Taille
Zhi Deng
Ziyan Deng
Xiaoyu Ding
Xuefeng Ding
Yayun Ding
Bayu Dirgantara
Carsten Dittrich
Sergey Dmitrievsky
Tadeas Dohnal
Dmitry Dolzhikov
Georgy Donchenko
Jianmeng Dong
Evgeny Doroshkevich
Wei Dou
Marcos Dracos
Frédéric Druillole
Ran Du
S. X. Du
Shuxian Du
Yujie Duan
Katherine Dugas
K. Dugas
Stefano Dusini
Hongyue Duyang
Jessica Eck
J. Eck
Timo Enqvist
Andrea Fabbri
Ulrike Fahrendholz
Lei Fan
Jian Fang
Wenxing Fang
Dmitry Fedoseev
Haiping Peng
Li-Cheng Feng
Qichun Feng
Federico Ferraro
Amélie Fournier
Fritsch Fritsch
Haonan Gan
Feng Gao
Alberto Garfagnini
Arsenii Gavrikov
Marco Giammarchi
Nunzio Giudice
Maxim Gonchar
G. Gong
Hui Gong
Guanghua Gong
Yuri Gornushkin
Marco Grassi
Maxim Gromov
Vasily Gromov
Minghao Gu
X. Gu
Xiaofei Gu
Yunting Gu
M. Guan
Yu Gu
Mengyun Guan
Yuduo Guan
Nunzio Guardone
Rosa Maria Guizzetti
Cong Guo
Wanlei Guo
Caren Hagner
Hechong Han
Ran Han
Yang Han
Jinhong He
Miao He
Wei He
Xinhai He
Tobias Heinz
Patrick Hellmuth
Yuekun Heng
Rafael Herrera
Y. Hor
YuenKeung Hor
Shaojing Hou
Yee Hsiung
Hang Hu
Bei-Zhen Hu
Jun Hu
Peng Hu
Shouyang Hu
Tao Hu
Yuxiang Hu
Zhuojun Hu
Guihong Huang
Hanxiong Huang
Jinhao Huang
Jun-Hao Huang
Junting Huang
Kaixuan Huang
Shengheng Huang
Wenhao Huang
Xingtao Huang
Xin Huang
Yongbo Huang
Jiaqi Hui
Lei Huo
Wenju Huo
Cédric Huss
Safeer Hussain
Leonard Imbert
Ara Ioannisian
Roberto Isocrate
Arshak Jafar
Beatrice Jelmini
Ignacio Jeria
Xiaolu Ji
Huihui Jia
Junji Jia
Siyu Jian
Cailian Jiang
Di Jiang
Guangzheng Jiang
Wei Jiang
Xiaoshan Jiang
Xiaozhao Jiang
Yixuan Jiang
X. Jing
Xiaoping Jing
Cécile Jollet
Li Kang
Rebin Karaparabil
Narine Kazarian
Ali Khan
Amina Khatun
Khanchai Khosonthongkee
Denis Korablev
Konstantin Kouzakov
Alexey Krasnoperov
Sergey Kuleshov
S. Kumaran
Sindhujha Kumaran
Nikolay Kutovskiy
Loïc Labit
Tobias Lachenmaier
Haojing Lai
Cecilia Landini
Sébastien Leblanc
Frederic Lefevre
Rui Li
Rupert Leitner
Jason Leung
Demin Li
Yi Wang
Fule Li
Fei Li
Gaosong Li
Hongjian Li
Huang Li
Jiajun Li
Min Li
Nan Li
Qingjiang Li
Ruhui Li
Ruiting Lei
Shanfeng Li
Shuo Li
Tao Li
Teng Li
Weidong Li
Weiguo Li
Xiaomei Li
Xiaonan Li
Xinglong Li
Yi Li
Yichen Li
Yufeng Li
Zhaohan Li
Zhibing Li
Ziyuan Li
Zonghai Li
An-An Liang
Hao Liang
Jiaming Yan
Yilin Liao
Jiajun Liao
Y. P. Liao
Ayut Limphirat
Yuzhong Liao
Guey-Lin Lin
Shengxin Lin
Tao Lin
Jiajie Ling
Xin Ling
Ivano Lippi
Caimei Liu
Yang Liu
Fengcheng Liu
Haidong Liu
Haotian Liu
Hongbang Liu
Hongjuan Liu
Hongtao Liu
Hongyang Liu
Jianglai Liu
Jiaxi Liu
Jinchang Liu
Min Liu
Qian Liu
Qin Liu
Runxuan Liu
Shenghui Liu
Shubin Liu
Shulin Liu
Xiaowei Liu
Xiwen Liu
Xuewei Liu
Yankai Liu
Zhen Liu
Lorenzo Loi
Alexey Lokhov
Paolo Lombardi
Claudio Lombardo
Kai Loo
Chuan Lu
Haoqi Lu
Jingbin Lu
Junguang Lu
Meishu Lu
Peizhi Lu
Shuxiang Lu
Xianguo Lu
Bayarto Lubsandorzhiev
Sultim Lubsandorzhiev
Livia Ludhova
Arslan Lukanov
F. Luo
Guang Luo
Fengjiao Luo
Jianyi Luo
Shu Luo
Wuming Luo
Xiaojie Luo
Vladimir Lyashuk
B. Ma
Bing Ma
Qiumei Ma
Bangzheng Ma
Si Ma
Xiaoyan Ma
Xubo Ma
Jihane Maalmi
Jingyu Mai
Marco Malabarba
Yury Malyshkin
Roberto Carlos Mandujano
Fabio Mantovani
Xin Mao
Yajun Mao
S. Mari
Filippo Marini
Stefano M. Mari
Agnese Martini
Matthias Mayer
Davit Mayilyan
Ints Mednieks
Yu Meng
Anita Meraviglia
Anselmo Meregaglia
Emanuela Meroni
Lino Miramonti
Nikhil Mohan
Michele Montuschi
Cristobal Morales Reveco
M. Nastasi
Dmitry V. Naumov
Massimiliano Nastasi
Elena Naumova
Diana Navas-Nicolas
Igor Nemchenok
Minh Thuan Nguyen Thi
Alexey Nikolaev
F. Ning
Zhe Ning
Feipeng Ning
Hiroshi Nunokawa
Lothar Oberauer
Juan Pedro Ochoa-Ricoux
Alexander Olshevskiy
Domizia Orestano
Fausto Ortica
Rainer Othegraven
A. Paoloni
George Parker
Alessandro Paoloni
Sergio Parmeggiano
Achilleas Patsias
Y. P. Pei
Luca Pelicci
Yatian Pei
Anguo Peng
Yu Peng
Yuefeng Peng
Zhaoyuan Peng
Elisa Percalli
Willy Perrin
Frédéric Perrot
P. Petitjean
Fabrizio Petrucci
Pierre-Alexandre Petitjean
Oliver Pilarczyk
Luis Felipe Piñeres Rico
Artyom Popov
Pascal Poussot
Ezio Previtali
Fazhi Qi
Ming Qi
Xiaohui Qi
Sen Qian
X. Qian
Zhen Qian
Hao Qiao
Xiaohui Qian
Zhonghua Qin
S. Qiu
Manhao Qu
Shoukang Qiu
Z. Qu
Gioacchino Ranucci
Zhenning Qu
A. Re
Abdel Rebii
Alessandra Re
Mariia Redchuk
Gioele Reina
Bin Ren
Jie Ren
Yuhan Ren
Barbara Ricci
Komkrit Rientong
Mariam Rifai
Mathieu Roche
Narongkiat Rodphai
Aldo Romani
Bedřich Roskovec
Xichao Ruan
Arseniy Rybnikov
Andrey Sadovsky
Paolo Saggese
Deshan Sandanayake
Anut Sangka
G. Sava
Utane Sawangwit
Giuseppe Sava
Michaela Schever
Cédric Schwab
Konstantin Schweizer
Alexandr Selyunin
Andrea Serafini
M. Settimo
Junyu Shao
Mariangela Settimo
V. Sharov
Hexi Shi
Vladislav Sharov
Jingyan Shi
Yanan Shi
Vitaly Shutov
Andrey Sidorenkov
Fedor Šimkovic
Apeksha Singhal
Chiara Sirignano
Jaruchit Siripak
Monica Sisti
Mikhail Smirnov
Oleg Smirnov
Sergey Sokolov
Julanan Songwadhana
Boonrucksar Soonthornthum
Albert Sotnikov
Warintorn Sreethawong
Achim Stahl
Luca Stanco
Konstantin Stankevich
Hans Steiger
Jochen Steinmann
Tobias Sterr
M. Stock
Virginia Strati
Matthias Raphael Stock
Michail Strizh
Alexander Studenikin
Aoqi Su
Jun Su
G. X. Sun
Shifeng Sun
Xilei Sun
Yongjie Sun
Guangbao Sun
Yongzhao Sun
Zhengyang Sun
Narumon Suwonjandee
Akira Takenaka
Xiaohan Tan
Jian Tang
Jing-Yu Tang
Qiang Tang
Quan Tang
Jingzhe Tang
Xiao Tang
Vidhya Thara Hariharan
Igor Tkachev
Tomas Tmej
M. Torri
Andrea Triossi
Wladyslaw Trzaska
Marco Danilo Claudio Torri
Y. Tung
Cristina Tuve
Nikita Ushakov
Vadim Vedin
Yu-Chen Tung
Carlo Venettacci
Giuseppe Verde
Maxim Vialkov
Benoit Viaud
Cornelius Moritz Vollbrecht
Katharina von Sturm
Vit Vorobel
Dmitriy Voronin
Lucia Votano
Pablo Walker
Caishen Wang
Chung-Hsiang Wang
En Wang
Guoli Wang
Han-Yang Wang
Jian Wang
Jun Wang
Li Wang
Lucinda W. Wang
Meng Wang
Mingyuan Wang
Qianchuan Wang
Lu Wang
Ruiguang Wang
Sibo Wang
Siguang Wang
Wei Wang
Wenshuai Wang
Xi Wang
Xiangyue Wang
Yangfu Wang
Yaoguang Wang
Yifang Wang
Yuanqing Wang
Yuyi Wang
Zhe Wang
Zheng Wang
Zhimin Wang
Apimook Watcharangkool
Wei Wei
Wenlu Wei
Yadong Wei
Yuehuan Wei
Liangjian Wen
Jun Weng
Christopher Wiebusch
Rosmarie Wirth
Chengxin Wu
Diru Wu
Qun Wu
Yinhui Wu
Yiyang Wu
Zhi Wu
Michael Wurm
Jacques Wurtz
Christian Wysotzki
Yufei Xi
Dongmei Xia
Shishen Xian
Ziqian Xiang
Fei Xiao
Xiang Xiao
Xiaochuan Xie
Yijun Xie
Yuguang Xie
Zhao Xin
Zhizhong Xing
Benda Xu
Cheng Xu
Donglian Xu
Fanrong Xu
Hangkun Xu
Jiayang Xu
Jilei Xu
Jing Xu
Jinghuan Xu
Meihang Xu
Xunjie Xu
Yin Xu
Yu Xu
Baojun Yan
Qiyu Yan
Taylor Yan
Xiongbo Yan
Yupeng Yan
Changgen Yang
Chengfeng Yang
Fengfan Yang
Jie Yang
Lei Yang
Pengfei Yang
Xiaoyu Yang
Yifan Yang
Yixiang Yang
Zekun Yang
Haifeng Yao
Jiaxuan Ye
Mei Ye
Ziping Ye
Frédéric Yermia
Zhengyun You
Boxiang Yu
Chiye Yu
Chunxu Yu
Guojun Yu
Hongzhao Yu
Miao Yu
Xianghui Yu
Zeyuan Yu
Zezhong Yu
Cenxi Yuan
Chengzhuo Yuan
Zhenxiong Yuan
Baobiao Yue
Noman Zafar
Kirill Zamogilnyi
Vitalii Zavadskyi
Fanrui Zeng
Shan Zeng
Tingxuan Zeng
Yuda Zeng
Liang Zhan
Aiqiang Zhang
Bin Zhang
Binting Zhang
Feiyang Zhang
Hangchang Zhang
Haosen Zhang
Honghao Zhang
Jiawen Zhang
Jie Zhang
Jingbo Zhang
Jinnan Zhang
Junwei Zhang
Lei Zhang
Peng Zhang
Ping Zhang
Qingmin Zhang
Shiqi Zhang
Shu Zhang
Shuihan Zhang
Siyuan Zhang
Tao Zhang
Xiaomei Zhang
Xin Zhang
Xuantong Zhang
Yibing Zhang
Yinhong Zhang
Yiyu Zhang
Yongpeng Zhang
Yu Zhang
Yumei Zhang
Zhenyu Zhang
Zhijian Zhang
Jie Zhao
Rong Zhao
Runze Zhao
Shujun Zhao
Tianhao Zhao
Hua Zheng
Yangheng Zheng
Jing Zhou
Li Zhou
Nan Zhou
Shun Zhou
Tong Zhou
Xiang Zhou
Xing Zhou
Jingsen Zhu
Kangfu Zhu
Kejun Zhu
Zhihang Zhu
Bo Zhuang
Honglin Zhuang
Liang Zong
Jiaheng Zou
2025-01-04
The European Physical Journal C (publié)
doi.org
arxiv.org