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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

引用

arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

引用

arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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High-quality high-order harmonic generation through preplasma truncation

引用

Physical Review E 2019年第5期100卷 053207-053207页

作者： B. Y. Li F. Liu M. Chen Z. Y. Chen X. H. Yuan S. M. Weng T. Jin S. G. Rykovanov J. W. Wang Z. M. Sheng J. Zhang Key Laboratory for Laser Plasmas (MoE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Collaborative Innovation Center of IFSA (CICIFSA) Shanghai Jiao Tong University Shanghai 200240 China National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid Physics China Academy of Engineering Physics Mianyang 621999 China Zhiyuan College Shanghai Jiao Tong University Shanghai 200240 China Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow 121205 Russia Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China SUPA Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom

By introducing preplasma truncation to cases with an initial preplasma scale length larger than 0.2λ, the efficiency of high-order harmonics generated from relativistic laser-solid interactions can be enhanced by more than one order of magnitude and the angular spread can be confined into near-diffraction-limited divergence. Numerical simulations show that density truncation results in more compact oscillation of the surface electron sheet and the curvature of the reflection surface for the driving laser is greatly reduced. This leads to an overall improvement in the harmonic beam quality. More importantly, density truncation makes the harmonic generation weakly dependent on the preplasma scale length, which provides a way to relax the extremely high requirement on the temporal contrast of the driving laser pulse. A feasible scheme to realize the required preplasma truncation is also proposed and demonstrated by numerical simulations.

关键词： High intensity laser-plasma interactions High-order harmonic generation X-ray generation in plasmas

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Recurrence quantification analysis of dynamic brain networks

arXiv

引用

arXiv 2020年

作者： Lopes, Marinho A. Zhang, Jiaxiang Krzemiński, Dominik Hamandi, Khalid Chen, Qi Livi, Lorenzo Masuda, Naoki Department of Engineering Mathematics University of Bristol BS8 1UB United Kingdom Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University CardiffCF24 4HQ United Kingdom Center for Studies of Psychological Application School of Psychology South China Normal University Guangzhou510631 China Departments of Computer Science and Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom Department of Mathematics University at Buffalo State University of New York United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York United States

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in brain function in health and other neurological disorders. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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Classical novae at radio wavelengths

arXiv

引用

arXiv 2021年

作者： Chomiuk, Laura Linford, Justin D. Aydi, Elias Bannister, Keith W. Krauss, Miriam I. Mioduszewski, Amy J. Mukai, Koji Nelson, Thomas J. Rupen, Michael P. Ryder, Stuart D. Sokoloski, Jennifer L. Sokolovsky, Kirill V. Strader, Jay Filipović, Miroslav D. Finzell, Tom Kawash, Adam Kool, Erik C. Metzger, Brian D. Nyamai, Miriam M. Ribeiro, Valério A.R.M. Roy, Nirupam Urquhart, Ryan Weston, Jennifer Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States National Radio Astronomy Observatory P.O. Box O SocorroNM87801 United States CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Pasadena City College 1570 E. Colorado Blvd. PasadenaCA91106 United States Center for Space Science and Technology University of Maryland Baltimore County BaltimoreMD21250 United States CRESST and X-ray Astrophysics Laboratory NASA/GSFC GreenbeltMD20771 United States Department of Physics and Astronomy University of Pittsburgh 3941 OHara St. PittsburghPA15260 United States Herzberg Institute of Astrophysics National Research Council of Canada PentictonBCV2A 6J9 Canada Department of Physics and Astronomy Macquarie University NSW2109 Australia Macquarie University Research Centre for Astronomy Astrophysics & Astrophotonics SydneyNSW2109 Australia Columbia Astrophysics Laboratory Columbia University New YorkNY United States Sternberg Astronomical Institute Moscow State University Universitetskii pr. 13 Moscow119992 Russia Western Sydney University Locked Bag 1797 Penrith South DCNSW2751 Australia Department of Computational Math Science and Engineering Michigan State University East LansingMI48824 United States The Oskar Klein Centre Department of Astronomy Stockholm University AlbaNova StockholmSE-106 91 Sweden Center for Computational Astrophysics Flatiron Institute 162 W. 5th Avenue New YorkNY10011 United States Department of Astronomy University of Cape Town Private Bag X3 Rondebosch7701 South Africa Instituto de Telecomunicações Campus Universitário de Santiago Aveiro3810-193 Portugal Department of Physics Indian Institute of Science Bangalore560012 India Federated IT 1201 Wilson Blvd 27th Floor ArlingtonVA22209 United States

We present radio observations (1–40 GHz) for 36 classical novae, representing data from over five decades compiled from the literature, telescope archives, and our own programs. Our targets display a striking diversity in their optical parameters (e.g., spanning optical fading timescales, t2 = 1–263 days), and we find a similar diversity in the radio light curves. Using a brightness temperature analysis, we find that radio emission from novae is a mixture of thermal and synchrotron emission, with non-thermal emission observed at earlier times. We identify high brightness temperature emission (TB > 5 × 104 K) as an indication of synchrotron emission in at least 9 (25%) of the novae. We find a class of synchrotron-dominated novae with mildly evolved companions, exemplified by V5589 Sgr and V392 Per, that appear to be a bridge between classical novae with dwarf companions and symbiotic binaries with giant companions. Four of the novae in our sample have two distinct radio maxima (the first dominated by synchrotron and the later by thermal emission), and in four cases the early synchrotron peak is temporally coincident with a dramatic dip in the optical light curve, hinting at a common site for particle acceleration and dust formation. We publish the light curves as tables and encourage use of these data by the broader community in multi-wavelength studies and modeling efforts. © 2021, CC BY.

关键词： Radio astronomy

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Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

arXiv

引用

arXiv 2020年

作者： Ezaki, Takahiro Himeno, Yu Watanabe, Takamitsu Masuda, Naoki Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Cognition Circuit Dynamics RIKEN Centre for Brain Science Saitama351-0198 Japan International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Advances of machine learning in molecular modeling and simulation

arXiv

引用

arXiv 2019年

作者： Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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11 New Transiting Brown Dwarfs and Very Low Mass Stars from TESS

arXiv

引用

arXiv 2025年

作者： Vowell, Noah Rodriguez, Joseph E. Latham, David W. Quinn, Samuel N. Schulte, Jack Eastman, Jason D. Bieryla, Allyson Barkaoui, Khalid Ciardi, David R. Collins, Karen A. Girardin, Eric Heldridge, Ellie Kotten, Brooke Mancini, Luigi Murgas, Felipe Narita, Norio Radford, D.J. Relles, Howard M. Shporer, Avi Soares-Furtado, Melinda Strakhov, Ivan A. Ziegler, Carl Briceño, César Calkins, Michael L. Clark, Catherine A. Collins, Kevin I. Esquerdo, Gilbert A. Fajardo-Acosta, Sergio B. Fukui, Akihiko Watkins, Cristilyn N. He, Ruixuan Horne, Keith Jenkins, Jon M. Mann, Andrew W. Naponiello, Luca Palle, Enric Schwarz, Richard P. Seager, S. Southworth, John Srdoc, Gregor Swift, Jonathan J. Winn, Joshua N. Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Center for Astrophysics | Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States Astrobiology Research Unit Université de Liège 19C Allée du 6 Août Liège4000 Belgium Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain NASA Exoplanet Science Institute Caltech/IPAC PasadenaCA91125 United States Grand Pra Observatory Les Hauderes1984 Switzerland The Thacher School 5025 Thacher Rd. OjaiCA93023 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Department of Physics University of Rome Tor Vergata Via della Ricerca Scientifica 1 RomeI-00133 Italy Max Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany INAF Osservatorio Astrofisico di Torino Via Osservatorio 20 Pino TorineseI-10025 Italy Tenerife La LagunaE-38205 Spain Tenerife La LagunaE-38206 Spain Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Tokyo Meguro153-8902 Japan Astrobiology Center 2-21-1 Osawa Tokyo Mitaka181 8588 Japan Brierfield Observatory BowralNSW Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53706 United States Sternberg Astronomical Institute Lomonosov Moscow State University Universitetsky Pr. 13 Moscow119234 Russia Department of Physics Engineering and Astronomy Stephen F. Austin State University 1936 North St NacogdochesTX75962 United States SOAR Telescope NSF NOIRLab Casilla

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA’s TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from 25 MJ to 128 MJ. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at ∼42 MJ. We also contribute a first look into the metallicity distribution of transiting companions in the range 7 − 150 MJ, showing that this too does not support a ∼42 MJ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment. Copyright © 2025, The Authors. All rights reserved.

关键词： NASA

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Numerical studies of solutions for kinetic equations with many-particle collisions

引用

Journal of Physics: Conference Series 2019年第1期1163卷 012008-012008页

作者： Matveev, S A Stefonishin, D A Smirnov, A P Sorokin, A A Tyrtyshnikov, E E Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute for Science and Technology Moscow 121205 Russia Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute for Science and Technology Moscow 121205 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow 119992 Russia Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow 119992 Russia Nuclear Safety Institute Russian Academy of Sciences Moscow 115191 Russia Marchuk Institute of Numerical Mathematics Russian Academy of Sciences Moscow 119333 Russia

In this work we present a method of low-rank tensor decompositions to accelerate evaluation of the right-hand side of systems of kinetic equations with many-particle collisional terms. These equations can be interpreted as a generalization of classical Smoluchowski aggregation equations allowing one to consider not only binary collisions of particles but also triple collisions. Straight-forward evaluation of the right-hand side for such system of N equations with k = 1, 2,…, N requires O(N 3) numerical operations and we find such complexity too high for practical investigations. However, under assumptions that kinetic coefficients can be represented with either canonical polyadic (CP) or tensor train decomposition (TT) with the rank R ≪ N we can propose algorithms evaluating the right-hand side with much lower complexities: O(NRlog N) and O(NR 2 log N) for CP and TT respectively. We compare numerical solutions with different triple-collision rates and obtain a significant influence of accounting triple collisional effects.

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