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arXiv 2024年

作者： Sato, Masato Tominaga, Nozomu Blinnikov, Sergei I. Potashov, Marat Sh. Moriya, Takashi J. Hiramatsu, Daichi Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Tokyo Mitaka181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Department of Physics Faculty of Science and Engineering Konan University 8-9-1 Okamoto Hyogo Kobe658-8501 Japan NRC Kurchatov Institute Moscow123182 Russia Moscow127055 Russia Keldysh Institute of Applied Mathematics RAS 4 Miusskaya Square Moscow125047 Russia School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed ∼ 40 years ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe exploding with energies in circumstellar media (CSM) estimated with theoretically or observationally plausible methods. The plateaus of the ECSNe are shorter, brighter, and bluer than those of the FeCCSNe. To investigate the robustness of their intrinsic differences, we adopted various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. Furthermore, we propose a distance-independent method to identify ECSNe: (g − r)tPT/2 PT − 0.4, i.e., blue g − r at the middle of the plateau (g − r)tPT/2, where tPT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques. © 2024, CC BY.

关键词： Supernovae

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Multimuons in cosmic-ray events as seen in ALICE at the LHC

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Journal of Cosmology and Astroparticle Physics 2025年第4期2025卷 009-009页

作者： Acharya, S. Agarwal, A. Aglieri Rinella, G. Aglietta, L. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Akishina, V. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altamura, A.R. Altsybeev, I. Alvarado, J.R. Alvarez, C.O.R. Anaam, M.N. Andrei, C. Andreou, N. Andronic, A. Andronov, E. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Averyanov, D. Azmi, M.D. Baba, H. Badalà, A. Bae, J. Bae, Y. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Bala, R. Balbino, A. Baldisseri, A. Balis, B. Banoo, Z. Barbasova, V. Barile, F. Barioglio, L. Barlou, M. Barman, B. Barnaföldi, G.G. Barnby, L.S. Barreau, E. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J.L. Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A.D.C. Bellini, F. Bellwied, R. Belokurova, S. Beltran, L.G.E. Beltran, Y.A.V. Bencedi, G. Bensaoula, A. Beole, S. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhattacharjee, B. Bianchi, L. Bielčík, J. Bielčíková, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Bock, F. Bodova, T. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A.G. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buchakchiev, V.D. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Burmasov, N. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cabanillas Noris, J.C. Cabrera, M.F.T. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Cantway, S.L. Carabas, M. Carballo, A.A. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castillo Castel A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation Yerevan Armenia AGH University of Krakow Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Bose Institute Department of Physics Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India California Polytechnic State University San Luis Obispo CA United States Central China Normal University Wuhan China Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) Havana Cuba Centro de Investigación y de Estudios Avanzados (CINVESTAV) Mérida Mexico City Mexico Chicago State University Chicago IL United States China Institute of Atomic Energy Beijing China China University of Geosciences Wuhan China Chungbuk National University Cheongju South Korea Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia Creighton University Omaha NE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan South Korea Department of Physics Sejong University Seoul South Korea Department of Physics University of California Berkeley CA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell’Università Sezione INFN Cagliari Italy Dipartimento di Fisica dell’Università Sezione INFN Trieste Italy Dipartimento di Fisica dell’Università Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell’Università Sezione INFN Padova Italy Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del Politecnico Sezione

ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015–2018) are described. The analysis is limited to multimuon events defined as events with more than four detected muons (N_µ > 4) and in the zenith angle range 0^◦ < θ < 50^◦. The results are compared with Monte Carlo simulations using three of the main hadronic interaction models describing the air shower development in the atmosphere: QGSJET-II-04, EPOS-LHC, and SIBYLL 2.3d. The interval of the primary cosmic-ray energy involved in the measured muon multiplicity distribution is about 4×10¹⁵ < E_prim < 6×10¹⁶ eV. In this interval none of the three models is able to describe precisely the trend of the composition of cosmic rays as the energy increases. However, QGSJET-II-04 is found to be the only model capable of reproducing reasonably well the muon multiplicity distribution, assuming a heavy composition of the primary cosmic rays over the whole energy range, while SIBYLL 2.3d and EPOS-LHC underpredict the number of muons in a large interval of multiplicity by more than 20% and 30%, respectively. The rate of high muon multiplicity events (N_µ > 100) obtained with QGSJET-II-04 and SIBYLL 2.3d is compatible with the data, while EPOS-LHC produces a significantly lower rate (55% of the measured rate). For both QGSJET-II-04 and SIBYLL 2.3d, the rate is close to the data when the composition is assumed to be dominated by heavy elements, an outcome compatible with the average energy E_prim ∼ 10¹⁷ eV of these events. This result places significant constraints on more exotic production mechanisms. © 2025 The Author(s).

关键词： cosmic ray experiments cosmic rays detectors

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First polarisation measurement of coherently photoproduced J/ψ in ultra-peripheral Pb–Pb collisions at s_NN=5.02 TeV

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 865卷

作者： Acharya, S. Adamová, D. Adler, A. Aglieri Rinella, G. Agnello, M. Agrawal, N. Ahammed, Z. Ahmad, S. Ahn, S.U. Ahuja, I. Akindinov, A. Al-Turany, M. Aleksandrov, D. Alessandro, B. Alfanda, H.M. Alfaro Molina, R. Ali, B. Alici, A. Alizadehvandchali, N. Alkin, A. Alme, J. Alocco, G. Alt, T. Altsybeev, I. Alvarado, J.R. Anaam, M.N. Andrei, C. Andronic, A. Anguelov, V. Antinori, F. Antonioli, P. Apadula, N. Aphecetche, L. Appelshäuser, H. Arata, C. Arcelli, S. Aresti, M. Arnaldi, R. Arneiro, J.G.M.C.A. Arsene, I.C. Arslandok, M. Augustinus, A. Averbeck, R. Azmi, M.D. Badalà, A. Bae, J. Baek, Y.W. Bai, X. Bailhache, R. Bailung, Y. Balbino, A. Baldisseri, A. Balis, B. Banerjee, D. Banoo, Z. Barbera, R. Barile, F. Barioglio, L. Barlou, M. Barnaföldi, G.G. Barnby, L.S. Barret, V. Barreto, L. Bartels, C. Barth, K. Bartsch, E. Bastid, N. Basu, S. Batigne, G. Battistini, D. Batyunya, B. Bauri, D. Bazo Alba, J.L. Bearden, I.G. Beattie, C. Becht, P. Behera, D. Belikov, I. Bell Hechavarria, A.D.C. Bellini, F. Bellwied, R. Belokurova, S. Belyaev, V. Bencedi, G. Beole, S. Bercuci, A. Berdnikov, Y. Berdnikova, A. Bergmann, L. Besoiu, M.G. Betev, L. Bhaduri, P.P. Bhasin, A. Bhat, M.A. Bhattacharjee, B. Bianchi, L. Bianchi, N. Bielčík, J. Bielčíková, J. Biernat, J. Bigot, A.P. Bilandzic, A. Biro, G. Biswas, S. Bize, N. Blair, J.T. Blau, D. Blidaru, M.B. Bluhme, N. Blume, C. Boca, G. Bock, F. Bodova, T. Bogdanov, A. Boi, S. Bok, J. Boldizsár, L. Bombara, M. Bond, P.M. Bonomi, G. Borel, H. Borissov, A. Borquez Carcamo, A.G. Bossi, H. Botta, E. Bouziani, Y.E.M. Bratrud, L. Braun-Munzinger, P. Bregant, M. Broz, M. Bruno, G.E. Buckland, M.D. Budnikov, D. Buesching, H. Bufalino, S. Buhler, P. Buthelezi, Z. Bylinkin, A. Bysiak, S.A. Cai, M. Caines, H. Caliva, A. Calvo Villar, E. Camacho, J.M.M. Camerini, P. Canedo, F.D.M. Cantway, S.L. Carabas, M. Carballo, A.A. Carnesecchi, F. Caron, R. Carvalho, L.A.D. Castillo Castellanos, J. Catalano, F. Ceballos Sanchez, C. Chakaberia, I. Chakraborty, P. Chandra, S. Chapeland, S. Chartier, M. Chattopa A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation Yerevan Armenia AGH University of Krakow Cracow Poland Bogolyubov Institute for Theoretical Physics National Academy of Sciences of Ukraine Kiev Ukraine Bose Institute Department of Physics Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India California Polytechnic State University San Luis Obispo CA United States Central China Normal University Wuhan China Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) Havana Cuba Centro de Investigación y de Estudios Avanzados (CINVESTAV) Mexico City and Mérida Mexico Chicago State University Chicago IL United States China Institute of Atomic Energy Beijing China Chungbuk National University Cheongju South Korea Comenius University Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia COMSATS University Islamabad Islamabad Pakistan Creighton University Omaha NE United States Department of Physics Aligarh Muslim University Aligarh India Department of Physics Pusan National University Pusan South Korea Department of Physics Sejong University Seoul South Korea Department of Physics University of California Berkeley CA United States Department of Physics University of Oslo Oslo Norway Department of Physics and Technology University of Bergen Bergen Norway Dipartimento di Fisica Università di Pavia Pavia Italy Dipartimento di Fisica dell'Università Sezione INFN Cagliari Italy Dipartimento di Fisica dell'Università Sezione INFN Trieste Italy Dipartimento di Fisica dell'Università Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Bologna Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Catania Italy Dipartimento di Fisica e Astronomia dell'Università Sezione INFN Padova Italy Dipartimento di Fisica ‘E.R. Caianiello’ dell'Università Gruppo Collegato INFN Salerno Italy Dipartimento DISAT del Politecnico

The first measurement of the polarisation of coherently photoproduced J/ψ mesons in ultra-peripheral Pb–Pb collisions, using data at sNN=5.02 TeV, is presented. The J/ψ meson is measured via its dimuon decay channe...

The first measurement of the polarisation of coherently photoproduced J/ψ mesons in ultra-peripheral Pb–Pb collisions, using data at s_NN=5.02 TeV, is presented. The J/ψ meson is measured via its dimuon decay channel in the forward rapidity interval −4.0−1 ± 5% (syst) is analysed. Hadronic activity is highly suppressed since the interaction is mediated by a photon. The polar and azimuthal angle distributions of the decay muons are measured, and the polarisation parameters λ_θ, λ_φ, λ_θφ are extracted. The analysis is carried out in the helicity frame. The results are found to be consistent with a transversely polarised J/ψ. These values are compared with previous measurements by the H1 and ZEUS experiments. The polarisation parameters of coherent J/ψ photoproduction in Pb–Pb collisions are found to be consistent with the s-channel helicity conservation hypothesis. © 2025 CERN for the benefit of the ALICE Collaboration

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Does decoherence violate decoupling?

arXiv

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arXiv 2024年

作者： Burgess, C.P. Colas, Thomas Holman, R. Kaplanek, Greg Department of Physics & Astronomy McMaster University HamiltonONL8S 4M1 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada School of Theoretical Physics Dublin Institute for Advanced Studies 10 Burlington Rd. Dublin Ireland Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Minerva University 14 Mint Plaza San FranciscoCA94103 United States Department of Physics Imperial College LondonSW7 2AZ United Kingdom Department of Electrical Engineering and Computer Science Syracuse University NY13210 United States

Recent calculations in both flat and de Sitter spacetimes have highlighted a tension between the decoupling of high-energy physics from low-energy degrees of freedom and the expectation that quantum systems decohere due to interactions with unknown environments. In effective field theory (EFT), integrating out heavy fields should lead to Hamiltonian time evolution, which preserves the purity of low-energy states. This is consistent with the fact that we never observe isolated quantum states spontaneously decohering in the vacuum due to unknown high-energy physics. However, when a heavy scalar of mass M is traced out, the resulting purity of a light scalar with mass m typically appears to scale as a power of 1/M (when m M), an effect that cannot be captured by a local effective Hamiltonian. We resolve this apparent paradox by showing that the purity depends on the resolution scale of the EFT and how the environment is traced out. We provide a practical method for diagnosing the purity of low-energy states consistent with EFT expectations, and briefly discuss some of the implications these observations have for how ultraviolet divergences can appear in decoherence calculations. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Human systems integration and advanced technology in engineering department workload and manpower reduction

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NAVAL ENGINEERS JOURNAL 2003年第1期115卷 57-65页

作者： Lively, KA Seman, AJ Kirkpatrick, M KENNETH A. LIVELY graduated from the University of Colorado with a BS in applied mathematics and an MS in mathematics in 1976 and from the Massachusetts Institute of Technology with an MS in electrical engineering and the degree ocean engineer in naval architecture and marine engineering in 1984. He retired from the U.S. Navy in 1989 after 23 years of service. Assignments included electrical officer on the USS Constellation (CV 64) project engineer for the DDG 51 machinery control system (NAVSEA) and DDG 51 Technical Director (NAVSEA). He was vice president of the PDI Division of Bird-Johnson Company from July 1989 to November 1998 where he managed various gas turbine and machinery controls related development projects. He joined Anteon Corporation's Systems Engineering Group as senior controls engineer in December 1998 where he provided technical support to the integrated power systems program (NAVSEA PMS 510) and managed the Office of Naval Research Afloat Laboratory. DR. MARK KIRKPATRICK is currently an independent consultant in human factors and work-load/manning analysis and modeling. He holds a Ph.D. degree in experimental psychology from The Ohio State University and has 34 years of experience in applied human factors. From 1982 through 2000 Dr. Kirkpatrick served as the senior vice president of Carlow International. Prior to joining Carlow in 1982 Dr. Kirkpatrick served as a member of the technical staff at North American Rockwell's Missiles Division and as a project director and vice president for Essex Corporation. His areas of expertise include workload simulation task analysis operator-in-the-loop simulation human performance experimentation statistical analysis and human factors T&E. He has directed and/or participated in human factors projects for the U.S. Navy U.S. Army NASA Department of Transportation the U.S. Nuclear Regulatory Commission and private industry. ANTHONY J. SEMAN III is the technical manager for the reduced ship's crew by virtual presence (RSVP) advanced technology d

Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central Control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.

关键词： Naval warfare

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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First Results from the REAL-time Transient Acquisition backend (REALTA) at the Irish LOFAR station

arXiv

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arXiv 2021年

作者： Murphy, Pearse C. Callanan, P. McCauley, J. McKenna, D.J. Fionnagáin, D.Ó. Louis, C.K. Redman, M.P. Cañizares, L.A. Carley, E.P. Maloney, S.A. Coghlan, B. Daly, M. Scully, J. Dooley, J. Gajjar, V. Giese, C. Brennan, A. Keane, E.F. Maguire, C.A. Quinn, J. Mooney, S. Ryan, A.M. Walsh, J. Jackman, C.M. Golden, A. Ray, T.P. Doyle, J.G. Rigney, J. Burton, M. Gallagher, P.T. School of Physics Trinity College Dublin Dublin 2 Ireland Astronomy & Astrophysics Section Dublin Institute for Advanced Studies D02 XF86 Ireland Department of Physics University College Cork Cork Ireland Astrophysics Research Group School of Mathematics Statistics and Applied Mathematics National University of Ireland Galway University Road GalwayH91 TK33 Ireland Centre for Astronomy School of Physics National University of Ireland Galway University Road GalwayH91 TK33 Ireland School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Deptartment of Computer and Software Engineering Athlone Institute of Technology Athlone Ireland Department of Astronomy University of California BerkeleyCA94720 United States School of Physics University College Dublin Belfield Dublin 4 Ireland Armagh Observatory and Planetarium College Hill ArmaghBT61 9DG United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun. © 2021, CC BY.

关键词： Interferometers

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WITHDRAWN: Environmental dimensionality determines species coexistence

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Journal of Theoretical Biology 2021年 526卷

作者： Kalle Parvinen Johan A.J. Metz Ulf Dieckmann Department of Mathematics and Statistics University of Turku FI-20014 Finland Evolution and Ecology Program International Institute for Applied Systems Analysis (IIASA) Laxenburg A-2361 Austria Institute of Biology and Mathematical Institute Leiden University NL-2333BE the Netherlands Netherlands Centre for Biodiversity Naturalis Leiden NL-2333CR the Netherlands Department of Evolutionary Studies of Biosystems The Graduate University for Advanced Studies (Sokendai) Hayama Kanagawa 240-0193 Japan

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( http://***/locate/withdrawalpolicy ). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

关键词： Large datasets

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Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron Microscopy

arXiv

引用

arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

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