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Observation of e^(+)e^(-)→pppñπ-+c.c.

Chinese physics C 2023年第4期47卷 16-25页

作者：麦迪娜 M.N.Achasov P.Adlarson M.Albrecht R.Aliberti A.Amoroso 安美儒安琪白旭红白羽 O.Bakina R.Baldini Ferroli I.Balossino 班勇 V.Batozskaya D.Becker K.Begzsuren N.Berger M.Bertani D.Bettoni F.Bianchi J.Bloms A.Bortone I.Boyko R.A.Briere A.Brueggemann 蔡浩蔡啸 A.Calcaterra 曹国富曹宁 S.A.Cetin 常劲帆常万玲 G.Chelkov 陈琛陈超陈刚陈和生陈玛丽陈申见陈少敏 T.Chen 陈旭荣 X.T.Chen 陈元柏陈卓俊成伟帅初晓 G.Cibinetto F.Cossio 崔佳佳代洪亮代建平 A.Dbeyssi R.E.de Boer D.Dedovich 邓子艳 A.Denig I.Denysenko M.Destefanis F.De Mori 丁勇董静董燎原董明义董翔杜书先 P.Egorov 范玉兰方建房双世方文兴方易 R.Farinelli L.Fava F.Feldbauer G.Felici 封常青冯俊华 K Fischer M.Fritsch C.Fritzsch 傅成栋高涵高原宁高扬 S.Garbolino I.Garzia 葛潘婷葛振武耿聪 E.M.Gersabeck A Gilman K.Goetzen 龚丽龚文煊 W.Gradl M.Greco 谷立民顾旻皓顾运厅关春懿郭爱强郭立波郭如盼郭玉萍 A.Guskov 韩婷婷韩文颖郝喜庆 F.A.Harris 何凯凯何康林 F.H.Heinsius C.H.Heinz 衡月昆 C.Herold M.Himmelreich 侯国一侯颖锐侯治龙胡海明 J.F.Hu 胡涛胡誉黄光顺黄凯旋黄麟钦黄麟钦黄性涛黄燕萍黄震 T.Hussain N Hüsken W.Imoehl M.Irshad J.Jackson S.Jaeger S.Janchiv 纪全姬清平季晓斌季筱璐吉钰瑶贾泽坤姜侯兵姜赛赛江晓山 Y.Jiang 焦健斌焦铮金山金毅荆茂强 T.Johansson N.Kalantar-Nayestanaki 康晓珅 R.Kappert 柯百谦 I.K.Keshk A.Khoukaz P.Kiese R.Kiuchi R.Kliemt L.Koch O.B.Kolcu B.Kopf M.Kuemmel M.Kuessner A.Kupsc W.Kühn J.J.Lane J.S.Lange P.Larin A.Lavania L.Lavezzi 雷祚弘 H.Leithoff M.Lellmann T.Lenz 李翠李聪李春花李澄李德民李飞李刚李慧李贺李海波李惠静 H.N.Li J.Q.Li 李静舒李井文李科 L.J.Li 李龙科李蕾李明浩李培荣李素娴栗帅迎李腾李卫东李卫国李旭红李晓玲李晓宇梁昊梁浩梁浩梁勇飞梁羽铁廖广睿廖龙洲 J.Libby A.Limphirat 林创新林德旭 T.Lin 刘北江刘春秀 D.Liu 刘福虎刘芳刘峰 G.M.Liu H.Liu, 刘宏邦刘怀民刘欢欢刘汇慧刘建北刘佳俊刘晶译刘凯刘魁勇刘珂刘亮刘露刘美宏刘佩莲刘倩刘树彬刘桐刘维克刘卫民刘翔刘英刘玉斌刘振安刘智青娄辛丑卢飞翔吕海江吕军光陆小玲卢宇卢云鹏 Z.H.Lu 罗成林罗民兴罗涛罗小兰吕晓睿吕翌丰马凤才马海龙马连良马明明马秋梅马润秋马瑞廷马骁妍马尧 F.E.Maas M.Maggiora S.Maldaner S.Malde Q.A.Malik A.Mangoni 冒亚军毛泽普 S.Marcello 孟召霞 J.G.Messchendorp G.Mezzadri H.Miao 闵天觉 R.E.Mitchell 莫晓虎 N.Yu.Muchnoi Y.Nefedov F.Nerling I.B.Nikolaev 宁哲 S.Nisar 牛艳 S.L.Olsen 欧阳群 S.Pacetti 潘祥潘越 A.Pathak M.Pelizaeus 彭海平 K.Peters 平加伦平荣刚 S.Plura S.Pogodin V.Prasad 齐法制齐航漆红荣祁鸣齐天钰钱森钱文斌钱圳乔从丰秦佳佳秦丽清覃潇平秦小帅秦中华邱进发屈三强 K.H.Rashid C.F.Redmer 任旷洁 A.Rivetti V.Rodin M.Rolo 荣刚 Ch.Ros Institute of High Energy Physics Beijing 100049China Beihang University Beijing 100191China Beijing Institute of Petrochemical Technology Beijing 102617China Bochum Ruhr-University D-44780 BochumGermany Carnegie Mellon University PittsburghPennsylvania 15213USA Central China Normal University Wuhan 430079China Central South University Changsha 410083China China Center of Advanced Science and Technology Beijing 100190China COMSATS University Islamabad Lahore CampusDefence RoadOff Raiwind Road54000 LahorePakistan Fudan University Shanghai 200433China G.I.Budker Institute of Nuclear Physics SB RAS(BINP) Novosibirsk 630090Russia GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291 DarmstadtGermany Guangxi Normal University Guilin 541004China Guangxi University Nanning 530004China Hangzhou Normal University Hangzhou 310036China Hebei University Baoding 071002China Helmholtz Institute Mainz Staudinger Weg 18D-55099 MainzGermany Henan Normal University Xinxiang 453007China Henan University of Science and Technology Luoyang 471003China Henan University of Technology Zhengzhou 450001China Huangshan College Huangshan 245000China Hunan Normal University Changsha 410081China Hunan University Changsha 410082China Indian Institute of Technology Madras Chennai 600036India Indiana University BloomingtonIndiana 47405USA INFN Laboratori Nazionali di Frascati I-00044FrascatiItaly INFN Sezione di Perugia I-06100PerugiaItaly University of Perugia I-06100PerugiaItaly INFN Sezione di Ferrara I-44122FerraraItaly University of Ferrara I-44122FerraraItaly Institute of Modern Physics Lanzhou 730000China B Ulaanbaatar 13330Mongolia Jilin University Changchun 130012China Johannes Gutenberg University of Mainz Johann-Joachim-Becher-Weg 45D-55099 MainzGermany Joint Institute for Nuclear Research 141980 DubnaMoscow regionRussia Justus-Liebig-Universitaet Giessen II.Physikalisches InstitutHeinrich-Buff-Ring 16D-35392 GiessenGermany Lanzhou University Lanzhou 730000China Liaoning Normal Unive

Using data taken at 29 center-of-mass energies between 4.16 and 4.70 GeV with the BESⅢdetector at the Beijing Electron Positron Collider corresponding to a total integrated luminosity of approximately 18.8 fb^(-1),the process e^(+)e^(-)→pppñπ+*** observed for the first time with a statistical significance of 11.5σ.The average Born cross sections in the energy ranges of(4.160,4.380)GeV,(4.400,4.600)GeV and(4.610,4.700)GeV are measured to be(21.5±5.7±1.2)fb,(46.3±10.6±2.5)fb and(59.0±9.4±3.2)fb,respectively,where the first uncertainties are statistical and the second are *** line shapes of the pñ and ppπ^(-)invariant mass spectra are consistent with phase space distributions,indicating that no hexaquark or di-baryon state is observed.

关键词： Multi-baryon channel hexaquark di-baryon states cross section measurement

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Search for a light charged Higgs boson in t → H ^± b decays, with H ^± → cb, in the lepton+jets final state in proton-proton collisions at √s = 13 TeV with the ATLAS detector

引用

Journal of High Energy physics 2023年第9期2023卷 1-52页

作者： Aad, G. Abbott, B. Abbott, D.C. Abed Abud, A. Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Achkar, B. Adam, L. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Alconada Verzini, M.J. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alunno Camelia, E. Alvarez Estevez, M. Alviggi, M.G. Amaral Coutinho, Y. Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Amor Dos Santos, S.P. Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Aranzabal, N. Araujo Ferraz, V. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Bakshi Gupta, D. Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Centro de Investigaciónes Unive

A search for a charged Higgs boson, H ±, produced in top-quark decays, t → H ± b, is presented. The search targets H ± decays into a bottom and a charm quark, H ± → cb. The analysis focuses o... 详细信息

A search for a charged Higgs boson, H ^±, produced in top-quark decays, t → H ^± b, is presented. The search targets H ^± decays into a bottom and a charm quark, H ^± → cb. The analysis focuses on a selection enriched in top-quark pair production, where one top quark decays into a leptonically decaying W boson and a bottom quark, and the other top quark decays into a charged Higgs boson and a bottom quark. This topology leads to a lepton-plus-jets final state, characterised by an isolated electron or muon and at least four jets. The search exploits the high multiplicity of jets containing b-hadrons, and deploys a neural network classifier that uses the kinematic differences between the signal and the background. The search uses a dataset of proton-proton collisions collected at a centre-of-mass energy s = 13 TeV between 2015 and 2018 with the ATLAS detector at CERN’s Large Hadron Collider, amounting to an integrated luminosity of 139 fb⁻¹. Observed (expected) 95% confidence-level upper limits between 0.15% (0.09%) and 0.42% (0.25%) are derived for the product of branching fractions B (t → H ^± b) × B(H ^± → cb) for charged Higgs boson masses between 60 and 160 GeV, assuming the SM production of the top-quark pairs. [Figure not available: see fulltext.]. © 2023, The Author(s).

关键词： Hadron-Hadron Scattering

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A first measurement of galaxy merger rate increasing in dynamically colder protoclusters at cosmic noon

arXiv

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arXiv 2024年

作者： Liu, Shuang Zheng, Xian Zhong Gonzalez, Valentino Yang, Xiaohu Huang, Jia-Sheng Shi, Dong Dong Xu, Haiguang Yuan, Qirong Zhang, Yuheng Wen, Run Qiao, Man Yang, Chao Lyu, Zongfei Purple Mountain Observatory Chinese Academy of Sciences 10 Yuanhua Road Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China Tsung-Dao Lee Institute Key Laboratory for Particle Physics Astrophysics and Cosmology Ministry of Education Shanghai Jiao Tong University Shanghai201210 China Departamento de Astronomía Universidad de Chile Camino del Observatorio 1515 Las Condes Santiago7591245 Chile Department of Astronomy School of Physics and Astronomy Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Chinese Academy of Sciences South America centre for Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Center for Fundamental Physics School of Mechanics and Optoelectronic Physics Anhui University of Science and Technology Anhui Huainan232001 China Department of Physics Institute of Theoretical Physics Nanjing Normal University Nanjing210023 China

The process of galaxy cluster formation likely leaves an imprint on the properties of its individual member galaxies. Understanding this process is essential for uncovering the evolutionary connections between galaxies and cosmic structures. Here we study a sample of ten protoclusters at z ∼ 2–3 in different dynamical states that we estimate based on spectroscopic data of their members. We combine the dynamical information with HST imaging to measure galaxy sizes and pair fractions. Our analysis reveals a clear anti-correlation between the velocity dispersion of the protocluster and its galaxy pair fractions (indicative of merger rates). The velocity dispersion also anti-correlates with the dispersion in size among of the member galaxies. These correlations may be explained by protoclusters in colder dynamical states maintaining a velocity dispersion and galaxy number density that boosts galaxy mergers, which in turn contributes to the structural expansion and compaction of galaxies. Our findings offer constraints for cosmological models regarding the evolution of galaxy morphology across different stages in the assembly of protoclusters. Copyright © 2024, The Authors. All rights reserved.

关键词： cosmology

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Return of 4U~1730-22 after 49 years silence: the spectral properties of the 2021/2022 outbursts observed by NICER and Insight-HXMT and the soft-to-hard state transition caused by the propeller effect

arXiv

引用

arXiv 2022年

作者： Chen, Yu-Peng Zhang, Shu Zhang, Shuang-Nan Ji, Long Wang, Peng-Ju Da Kong, Ling Chang, Zhi Peng, Jing-Qiang Shui, Qing-Cang Li, Jian Tao, Lian Ge, Ming-Yu Qu, Jin-Lu Key Laboratory for Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China School of Physics and Astronomy Sun Yat-Sen University Zhuhai519082 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 TübingenD-72076 Germany CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China

After in quiescence for 49 years, 4U 1730–22 became active and had two outbursts in 2021 & 2022, the onset and tail of the outbursts were observed by NICER, which give us a peerless opportunity to study the state transition and its underlying mechanism. In this work, we take both the NS surface and accretion disk emission as the seed photons of the Comptonization and derive their spectral evolution in a bolometric luminosity range of 1%–15%LEdd. In the high/soft state, the inferred inner disk radius and the NS radius are consistent well, which implies that the accretion disk is close to the NS surface. For the decay stage, we report a steep change of the accretion disk emission within one day, i.e., the soft-to-hard transition, which could be due to the propeller effect and the corresponding neutron star surface magnetic field is 1.8–2.2×108 G. Moreover, the inner disk radius is truncated at the corotation radius, which is similar to the propeller effect detected from 4U 1608–52. The absence of the propeller effect in the hard-to-soft state transition implies that the transition between the magnetospheric accretion and the disk accretion is not the sole cause of the state transitions. © 2022, CC BY.

关键词： Stars

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data analysis and key science results of LHAASO-WCDA 37

Data analysis and key science results of LHAASO-WCDA

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Zha, Min Gao, Chuandong Hu, Shicong Lin, Shujie Wang, Zhen Xiang, Guangman Yao, Zhiguo Zhou, Hao Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Z. Zhou, J.N. Chang, J. Chang, J.F. Chang, X.C. Chen, B.M. Chen, J. Chen, L. Zhou, X.X. Zhu, C.G. Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. D’Ettorre Piazzoli, B. Dong, X.J. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Goa, C.D. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, S. Zheng, Y. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Kuleshov, D. Levochkin, K. Li, B.B. Li, C. Zhou, P. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X. Zhu, H. Li, X.R. Li, Y. Li, Y.Z. Li, Z. Zhou, R. Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qiao, B.Q. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.R. Song, H.C. Stenkin, Yu.V. Stepanov, V. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Key Laboratory of Particle Astrophyics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Sun Yat-sen University Guangdong Zhuhai519082 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China Department of Engineering Physics Tsinghua University Beijing100084 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Physics Peking University Beijing100871 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Sun Yat-sen University Guangdong Zhuhai519082 China School of Physical Science and Technology Guangxi University Guangxi Nanning530004 China Hebei Normal University Hebei Shijiazhuang050024 China School of Physics and Microelectronics Zhengzhou University Henan Zhengzhou450001 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Tsung Dao Lee Institute School of Physics and Astrono

The Water Cherenkov Detector Array (WCDA) is an important component of Large High Altitude Air Shower Observatory (LHAASO). The main scientific target of WCDA is continuously surveying the northern sky at the level of few percent of Crab unit. In this proceeding, based on its first year data of WCDA-1 data, the result about the Crab Nebular observation and its energy spectrum measurment have been presented. And with WCDA full array data taking since Mar. 2021, some preliminary results have been reported. © Copyright owned by the author(s).

关键词： cosmology

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Nebula-Relay Hypothesis: The Chirality of Biological Molecules in Molecular Clouds

arXiv

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arXiv 2021年

作者： Feng, Lei Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing China School of Astronomy and Space Science University of Science and Technology of China Hefei China Joint Center for Particle Nuclear Physics and Cosmology Nanjing University-Purple Mountain Observatory Nanjing China

The chiral puzzle of biological molecules is thought to be closely related to the origin of life and is still a mystery so far. Previously, we proposed a new model on the origin of life, Nebula-Relay hypothesis, which assumed that the life on Earth originated at the planetary system of the Sun's predecessor star and then filled in the pre-solar nebula after its death. As primitive lives existed in the pre-solar nebula for a long time, did the chiral biomolecules form during this period? We explore such a possibility in this work and find that the ultralow temperature environment of molecular clouds is beneficial to generating the chiral polymer chain of biological molecules. Copyright © 2021, The Authors. All rights reserved.

关键词： Chirality

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cosmology with persistent homology: parameter inference via machine learning

arXiv

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arXiv 2024年

作者： Calles, Juan Yip, Jacky H.T. Contardo, Gabriella Noreña, Jorge Rouhiainen, Adam Shiu, Gary Instituto de Física y Astronomía Universidad de Valparaíso Avda. Gran Bretaña 1111 Valparaíso Chile Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Center for Astrophysics and Cosmology University of Nova Gorica AjdovščinaI-5270 Slovenia Theoretical and Scientific Data Science Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Instituto de Física Pontificia Universidad Católica de Valparaíso Casilla 4950 Valparaíso Chile

Building upon [1], this article investigates the potential constraining power of persistent homology for cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability of persistence images (PIs) to infer parameters, compared to the combined Power Spectrum and Bispectrum (PS/BS), and we compare two types of models: neural-based, and tree-based. PIs consistently lead to better predictions compared to the combined PS/BS when the parameters can be constrained (i.e., for {Ωm, σ8, ns, fNLloc}). PIs perform particularly well for fNLloc, showing the promise of persistent homology in constraining primordial non-Gaussianity. Our results show that combining PIs with PS/BS provides only marginal gains, indicating that the PS/BS contains little extra or complementary information to the PIs. Finally, we provide a visualization of the most important topological features for fNLloc and for Ωm. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for Ωm, while fNLloc uses the filaments (1-cycles) in addition to the other two types of topological features. Copyright © 2024, The Authors. All rights reserved.

关键词： cosmology

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A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks

arXiv

引用

arXiv 2021年

作者： Yiu, Timothy Wing Hei Fluri, Janis Kacprzak, Tomasz Institute for Particle Physics and Astrophysics ETH Zürich Wolfgang-Pauli-Strasse 27 ZürichCH-8093 Switzerland Swiss Data Science Center Paul Scherrer Institute PSI Forschungsstrasse 111 Villigen5232 Switzerland

Large sets of matter density simulations are becoming increasingly important in large-scale structure cosmology. Matter power spectra emulators, such as the Euclid Emulator and CosmicEmu, are trained on simulations to correct the non-linear part of the power spectrum. Map-based analyses retrieve additional non-Gaussian information from the density field, whether through human-designed statistics such as peak counts, or via machine learning methods such as convolutional neural networks. The simulations required for these methods are very resource-intensive, both in terms of computing time and storage. This creates a computational bottleneck for future cosmological analyses, as well as an entry barrier for testing new, innovative ideas in the area of cosmological information retrieval. Map-level density field emulators, based on deep generative models, have recently been proposed to address these challenges. In this work, we present a novel mass map emulator of the KiDS-1000 survey footprint, which generates noise-free spherical maps in a fraction of a second. It takes a set of cosmological parameters (ΩM,σ8) as input and produces a consistent set of 5 maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct the emulator, we use a conditional generative adversarial network architecture and the spherical convolutional neural network DeepSphere, and train it on N-body-simulated mass maps. We compare its performance using an array of quantitative comparison metrics: angular power spectra C, pixel/peaks distributions, C correlation matrices, and Structural Similarity Index. Overall, the average agreement on these summary statistics is Copyright © 2021, The Authors. All rights reserved.

关键词： cosmology

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Small-scale signatures of primordial non-Gaussianity in k-Nearest Neighbour cumulative distribution functions

arXiv

引用

arXiv 2023年

作者： Coulton, William R. Abel, Tom Banerjee, Arka Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics Indian Institute of Science Education and Research Homi Bhabha Road Pashan Pune411008 India

Searches for primordial non-Gaussianity in cosmological perturbations are a key means of revealing novel primordial physics. However, robustly extracting signatures of primordial non-Gaussianity from non-linear scales of the late-time Universe is an open problem. In this paper, we apply k-Nearest Neighbor cumulative distribution functions, kNN-CDFs, to the quijote-png simulations to explore the sensitivity of kNN-CDFs to primordial non-Gaussianity. An interesting result is that for halo samples with Mh 14 M☉/h, the kNN-CDFs respond to equilateral PNG in a manner distinct from the other parameters. This persists in the galaxy catalogs in redshift space and can be differentiated from the impact of galaxy modelling, at least within the halo occupation distribution (HOD) framework considered here. kNN-CDFs are related to counts-in-cells and, through mapping a subset of the kNN-CDF measurements into the count-in-cells picture, we show that our results can be modeled analytically. A caveat of the analysis is that we only consider the HOD framework, including assembly bias. It will be interesting to validate these results with other techniques for modeling the galaxy–halo connection, e.g., (hybrid) effective field theory or semi-analytical methods. © 2023, CC BY.

关键词： Galaxies

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DarkAI: Reconstructing the density, velocity and tidal field of dark matter from DESI-like bright galaxy sample

arXiv

引用

arXiv 2025年

作者： Shi, Feng Wang, Zitong Yang, Xiaohu Gu, Yizhou Wei, Chengliang Li, Ming Han, Jiaxin Wang, Huiyuan Zhang, Youcai Hong, Wensheng Wang, Yirong Li, Xiao-Dong School of Aerospace Science and Technology Xidian University Xi’an710126 China Shaanxi Key Laboratory of Space Extreme Detection Xidian University Xi’an710126 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai Jiao Tong University Shanghai200240 China Purple Mountain Observatory Nanjing210008 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Astronomy Shanghai Jiao Tong University Shanghai200240 China Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China Shanghai Astronomical Observatory Nandan Road 80 Shanghai200030 China School of Physics and Astronomy Sun Yat-Sen University Zhuhai519082 China CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area SYSU Zhuhai519082 China

Reconstructing the dark matter density, velocity, and tidal (MTV) fields from galaxy surveys is essential for advancing our understanding of the large-scale structure of the Universe. In this work, we present a machine learning-based framework using a UNet convolutional neural network to reconstruct the MTV fields from mock samples of the DESI bright galaxy survey within the redshift range 0.1 −1. The velocity field reconstruction accurately captures large-scale coherent flows and small-scale turbulent features, exhibiting slopes of the grid-to-grid relationship close to unity and a scatter below ∼100 km s−1. Additionally, the tidal field is reconstructed without bias, successfully recovering the features of the large-scale cosmic web, including clusters, filaments, sheets, and voids. Our results confirm that the proposed framework effectively captures the large-scale distribution and dynamics of dark matter while addressing key systematic challenges. These advancements provide a reliable and robust tool for analyzing current and future galaxy surveys, paving the way for new insights into cosmic structure formation and evolution. Copyright © 2025, The Authors. All rights reserved.

关键词： Dark Matter

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