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arXiv 2024年

作者： Iyer, Kartheik G. Yunus, Mikaeel O’Neill, Charles Ye, Christine Hyk, Alina McCormick, Kiera Ciucă, Ioana Wu, John F. Accomazzi, Alberto Astarita, Simone Chakrabarty, Rishabh Cranney, Jesse Field, Anjalie Ghosal, Tirthankar Ginolfi, Michele Huertas-Company, Marc Jablońska, Maja Kruk, Sandor Liu, Huiling Marchidan, Gabriel Mistry, Rohit Naiman, J.P. Peek, J.E.G. Polimera, Mugdha Rodríguez Méndez, Sergio J. Schawinski, Kevin Sharma, Sanjib Smith, Michael J. Ting, Yuan-Sen Walmsley, Mike Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics and Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States School of Computing TheAustralian National University 108 North Rd ActonACT2601 Australia Stanford University 450 Jane Stanford Way StanfordCA94305 United States School of Electrical Engineering and Computer Science Oregon State University 1500 SW Jefferson Way CorvallisOR97331 United States Department of Engineering Loyola University Maryland 4501 North Charles Street BaltimoreMD21210 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Paris France Astralis-AITC - Stromlo RSAA Australian National University Cotter Road WestonACT2600 Australia Department of Computer Science Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Dipartimento di Fisica e Astronomia Universita di Firenze Via G. Sansone 1 Sesto Fiorentino Firenze50019 Italy INAF - Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FirenzeI-50125 Italy Instituto de Astrofisica de Canarias C. Via Lactea 1 Tenerife La LagunaE-38205 Spain Universidad de la Laguna dept. Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Cite LERMA - Observatoire de Paris PSL Paris France SCIPP University of

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with key-words. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use key-words or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool’s versatility through case studies, showcasing its application in various research scenarios. The system’s performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature. © 2024, CC BY.

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Quantum Simulation for High-Energy Physics

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PRX Quantum 2023年第2期4卷 027001-027001页

作者： Christian W. Bauer Zohreh Davoudi A. Baha Balantekin Tanmoy Bhattacharya Marcela Carena Wibe A. de Jong Patrick Draper Aida El-Khadra Nate Gemelke Masanori Hanada Dmitri Kharzeev Henry Lamm Ying-Ying Li Junyu Liu Mikhail Lukin Yannick Meurice Christopher Monroe Benjamin Nachman Guido Pagano John Preskill Enrico Rinaldi Alessandro Roggero David I. Santiago Martin J. Savage Irfan Siddiqi George Siopsis David Van Zanten Nathan Wiebe Yukari Yamauchi Kübra Yeter-Aydeniz Silvia Zorzetti Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics Maryland Center for Fundamental Physics and the NSF Institute for Robust Quantum Simulation University of Maryland College Park Maryland 20742 USA Department of Physics University of Wisconsin Madison Wisconsin 53706 USA T-2 Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Enrico Fermi Institute University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois Urbana Illinois 61801 USA QuEra Computing Inc Boston Massachusetts 02135 USA Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University New York 11794-3800 USA Department of Physics Brookhaven National Laboratory Upton New York 11973 USA Peng Huanwu Center for Fundamental Theory Hefei Anhui 230026 China Interdisciplinary Center for Theoretical Study University of Science and Technology of China Hefei Anhui 230026 China Pritzker School of Molecular Engineering Chicago Quantum Exchange and Kadanoff Center for Theoretical Physics University of Chicago Illinois 60637 USA qBraid Co. Harper Court 5235 Chicago Illinois 60615 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics and Astronomy University of Iowa Iowa City Iowa 52242 USA Duke Quantum Center Duke University Durham North Carolina 27701 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics Duke University

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This Roadmap is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

关键词： Quantum simulation

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The parkes pulsar timing array project: Second data release

arXiv

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arXiv 2020年

作者： Kerr, M. Reardon, D.J. Hobbs, G. Shannon, R.M. Manchester, R.N. Dai, S. Russell, C.J. Zhang, S.-B. van Straten, W. Oslowski, S. Parthasarathy, A. Spiewak, R. Bailes, M. Bhat, N.D.R. Cameron, A.D. Coles, W.A. Dempsey, J. Deng, X. Goncharov, B. Kaczmarek, J.F. Keith, M.J. Lasky, P.D. Lower, M.E. Preisig, B. Sarkissian, J.M. Toomey, L. Wang, H. Wang, J. Zhang, L. Zhu, X. Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia CSIRO Astronomy and Space Science Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia University of Chinese Academy of Sciences Beijing100049 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China International Centre for Radio Astronomy Research University of Western Australia CrawleyWA6009 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Department of Electrical and Computer Engineering University of California San DiegoCA92093 United States CSIRO Information Management & Technology GPO Box 1700 CanberraACT2601 Australia School of Physics and Astronomy Monash University VIC3800 Australia CSIRO Astronomy and Space Science Parkes Observatory 473 Telescope road ParkesNSW2870 Australia Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Alan Turing Building Oxford Road ManchesterM13 9PL United Kingdom School of Physics and Electronic Engineering Guangzhou University Guangzhou510006 China Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang830011 China National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China

We describe 14 years of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately three weeks in three observing bands. A comprehensive description of the pulsar observing systems employed at the telescope since 2004 is provided, including the calibration methodology and an analysis of the stability of system components. We attempt to provide full accounting of the reduction from the raw measured Stokes parameters to pulse times of arrival to aid third parties in reproducing our results. This conversion is encapsulated in a processing pipeline designed to track provenance. Our data products include pulse times of arrival for each of the pulsars along with an initial set of pulsar parameters and noise models. The calibrated pulse profiles and timing template profiles are also available. These data represent almost 21,000 hrs of recorded data spanning over 14 years. After accounting for processes that induce time-correlated noise, 22 of the pulsars have weighted root-mean-square timing residuals of Copyright © 2020, The Authors. All rights reserved.

关键词： Pulsars

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Search for axion-like dark matter using solid-state nuclear magnetic resonance

arXiv

引用

arXiv 2021年

作者： Aybas, Deniz Adam, Janos Blumenthal, Emmy Gramolin, Alexander V. Johnson, Dorian Kleyheeg, Annalies Afach, Samer Blanchard, John W. Centers, Gary P. Garcon, Antoine Engler, Martin Figueroa, Nataniel L. Sendra, Marina Gil Wickenbrock, Arne Lawson, Matthew Wang, Tao Wu, Teng Luo, Haosu Mani, Hamdi Mauskopf, Philip Graham, Peter W. Rajendran, Surjeet Jackson Kimball, Derek F. Budker, Dmitry Sushkov, Alexander O. Department of Physics Boston University BostonMA02215 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Helmholtz-Institut GSI Helmholtzzentrum für Schwerionenforschung Mainz55128 Germany Johannes Gutenberg-Universität Mainz Mainz55128 Germany The Oskar Klein Centre for Cosmoparticle Physics Department of Physics Stockholm University AlbaNova Stockholm10691 Sweden Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 Stockholm10691 Sweden Department of Physics Princeton University PrincetonNJ08544 United States State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Shanghai Institute of Ceramics Chinese Academy of Sciences China School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States Department of Physics & Astronomy The Johns Hopkins University BaltimoreMD21218 United States Department of Physics California State University East Bay HaywardCA94542-3084 United States Department of Physics University of California BerkeleyCA94720-7300 United States Photonics Center Boston University BostonMA02215 United States

We report the results of an experimental search for ultralight axion-like dark matter in the mass range 162 neV to 166 neV. The detection scheme of our Cosmic Axion Spin Precession Experiment (CASPEr) is based on a precision measurement of 207Pb solid-state nuclear magnetic resonance in a polarized ferroelectric crystal. Axion-like dark matter can exert an oscillating torque on 207Pb nuclear spins via the electric-dipole moment coupling gd, or via the gradient coupling gaNN. We calibrated the detector and characterized the excitation spectrum and relaxation parameters of the nuclear spin ensemble with pulsed magnetic resonance measurements in a 4.4 T magnetic field. We swept the magnetic field near this value and searched for axion-like dark matter with Compton frequency within a 1 MHz band centered at 39.65 MHz. Our measurements place the upper bounds |gd| −4 GeV−2 and |gaNN| −1 GeV−1 (95% confidence level) in this frequency range. The constraint on gd corresponds to an upper bound of 1.0 × 10−21 e · cm on the amplitude of oscillations of the neutron electric dipole moment, and 4.3×10−6 on the amplitude of oscillations of CP-violating θ parameter of quantum chromodynamics. Our results demonstrate the feasibility of using solid-state nuclear magnetic resonance to search for axion-like dark matter in the nanoelectronvolt mass range. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum theory

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

引用

arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

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arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe Terrien, Ryan Holcomb, Rae Ford, Eric Zawadzki, Brianna Bowler, Brendan P. Bender, Chad Cochran, William Diddams, Scott Endl, Michael Fredrick, Connor Halverson, Samuel Hearty, Fred Hill, Gary J. Lin, Andrea Metcalf, Andrew Monson, Andrew Ramsey, Lawrence Roy, Arpita Schwab, Christian Wright, Jason Zeimann, Gregory Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

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A two-kind-boson mixture honeycomb Hamiltonian of bloch exciton-polaritons

arXiv

引用

arXiv 2021年

作者： Pan, Haining Winkler, K. Powlowski, Mats Xie, Ming Schade, A. Emmerling, M. Kamp, M. Klembt, S. Schneider, C. Byrnes, Tim Höfling, S. Kim, Na Young Institute for Quantum Computing University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada Condensed Matter Theory Center Joint Quantum Institute Department of Physics University of Maryland College ParkMD20742 United States Technische Physik Physikalisches Institut Universität Würzbug WürzburgD-97074 Germany Department of Electrical and Computer Engineering University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada Department of Physics The University of Texas at Austin AustinTX78712 United States State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai200062 China New York University Shanghai 1555 Century Ave. Pudong Shanghai200122 China NYU-ECNU Institute of Physics NYU Shanghai 366 Zhongshan Road North Shanghai200062 China Department of Physics New York University New YorkNY10003 United States School of Physics and Astronomy University of St. Andrews St. AndrewsKY16 9SS United Kingdom Department of Physics and Astronomy University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada

The electronic bandstructure of a solid is a collection of allowed bands separated by forbidden bands, revealing the geometric symmetry of the crystal structures. Comprehensive knowledge of the bandstructure with band parameters explains intrinsic physical, chemical and mechanical properties of the solid. Here we report the artificial polaritonic bandstructures of two-dimensional honeycomb lattices for microcavity exciton-polaritons using GaAs semiconductors in the wide-range detuning values, from cavity-photon-like (red-detuned) to exciton-like (blue-detuned) regimes. In order to understand the experimental bandstructures and their band parameters, such as gap energies, bandwidths, hopping integrals and density of states, we originally establish a polariton band theory within an augmented plane wave method with two-kind-bosons, cavity photons trapped at the lattice sites and freely moving excitons. In particular, this two-kind-band theory is absolutely essential to elucidate the exciton effect in the bandstructures of blue-detuned exciton-polaritons, where the flattened exciton-like dispersion appears at larger in-plane momentum values captured in our experimental access window. We reach an excellent agreement between theory and experiments in all detuning values. © 2021, CC BY-NC-SA.

关键词： Honeycomb structures

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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On the role of theory and modeling in neuroscience

arXiv

引用

arXiv 2020年

作者： Levenstein, Daniel Alvarez, Veronica A. Amarasingham, Asohan Azab, Habiba Chen, Zhe S. Gerkin, Richard C. Hasenstaub, Andrea Iyer, Ramakrishnan Jolivet, Renaud B. Marzen, Sarah Monaco, Joseph D. Prinz, Astrid A. Quraishi, Salma Santamaria, Fidel Shivkumar, Sabyasachi Singh, Matthew F. Traub, Roger Rotstein, Horacio G. Nadim, Farzan Redish, A. David Montreal Neurological Institute McGill University MontrealQC Canada Laboratory on Neurobiology of Compulsive Behaviors National Institute on Alcohol Abuse and Alcoholism NIH BethesdaMD United States Department of Neuroscience Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States School of Life Sciences Arizona State University TempeAZ United States Department of Otolaryngology - Head and Neck Surgery University of California San Francisco San FranciscoCA United States Allen Institute for Brain Science SeattleWA United States Maastricht University Maastricht Netherlands Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Department of Biology University of Texas at San Antonio San AntonioTX United States Department of Psychological & Brain Sciences Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO United States IBM T.J. Watson Research Center AI Foundations Yorktown HeightsNY United States W. M. Keck Science Department Pitzer Scripps and Claremont McKenna Colleges ClaremontCA United States Department of Biology Emory University AtlantaGA United States Department of Mathematics City College of New York United States Departments of Biology Computer Science and Psychology The Graduate Center City University of New York United States Brain and Cognitive Sciences University of Rochester RochesterNY United States Federated Department of Biological Sciences New Jersey Institute of Technology Rutgers University Institute for Brain and Neuroscience Research New Jersey Institute of Technology NewarkNJ United States Department of Neuroscience University of Minnesota MinneapolisMN United States Department of Psychiatry Neuroscience & Physiology New York University School of Medicine United States Mila - Quebec AI Institute MontrealQC Canada

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. © 2020, CC BY.

关键词： Computation theory

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