检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhu, Zi-Pei Xu, Dong Fynbo, Johan P.U. Fu, Shao-Yu Liu, Xing Jiang, Shuai-Qing Xiao, Shuo Xie, Wei Zou, Yuan-Chuan Gao, He Hartmann, Dieter de Ugarte Postigo, Antonio Kann, David Alexander Valle, Massimo Della Jakobsson, Pall Zafar, Tayabba D’Elia, Valerio Xin, Li-Ping Wei, Jian-Yan Gao, Xing Liu, Jin-Zhong Lu, Tian-Hua Lei, Wei-Hua Department of Astronomy School of Physics Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Niels Bohr Institute University of Copenhagen Lyngbyvej 2 Copenhagen2100 Denmark School of Astronomy and Space Science University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Key Laboratory of Cosmic Rays Ministry of Education Tibet University Tibet Lhasa850000 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Department of Astronomy Beijing Normal University Beijing100875 China Department of Physics and Astronomy Clemson University ClemsonSC29634 United States Université Côte d’Azur Observatoire de la Côte d’Azur Artemis CNRS Nice06304 France Hessian Research Cluster ELEMENTS Giersch Science Center Goethe University Frankfurt Campus Riedberg Max-von-Laue-Strasse 12 Frankfurt am Main60438 Germany Capodimonte Astronomical Observatory INAF-Napoli Salita Moiariello 16 Italy Department of Physics Ariel University Ariel Israel ICRANet Piazza della Repubblica 10 PescaraI-65122 Italy Centre for Astrophysics and Cosmology Science Institute University of Iceland Dunhagi 5 Reykjavik107 Iceland Australian Astronomical Optics Macquarie University 105 Delhi Road North RydeNSW2113 Australia Macquarie University Research Centre for Astronomy Astrophysics & Astrophotonics SydneyNSW2109 Australia ASI-Space Science Data Centre Via del Politecnico snc RomeI-00133 Italy INAF Osservatorio Astronomico di Roma Via di Frascati 33 Monteporzio Catone RomeI-00040 Italy Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi Xinjiang830011 China

Early optical observations of gamma-ray bursts can significantly contribute to the study of the central engine and physical processes therein. However, of the thousands observed so far, still only a few have data at optical wavelengths in the first minutes after the onset of the prompt emission. Here we report on GRB 190106A, whose afterglow was observed in optical bands just 36 s after the Swift/BAT trigger, i.e., during the prompt emission phase. The early optical afterglow exhibits a bimodal structure followed by a normal decay, with a faster decay after ∼ T0+1 day. We present optical photometric and spectroscopic observations of GRB 190106A. We derive the redshift via metal absorption lines from Xinglong 2.16-m/BFOSC spectroscopic observations. From the BFOSC spectrum, we measure z = 1.861 ± 0.002. The double-peak optical light curve is a significant feature predicted by the reverse-forward external shock model. The shallow decay followed by a normal decay in both the X-ray and optical light curves is well explained with the standard forward-shock model with late-time energy injection. Therefore, GRB 190106A offers a case study for GRBs emission from both reverse and forward shocks. Copyright © 2023, The Authors. All rights reserved.

关键词： Absorption spectroscopy

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Constructing Impactful Machine Learning research for Astronomy: Best Practices for researchers and Reviewers

arXiv

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arXiv 2023年

作者： Huppenkothen, Daniela Ntampaka, Michelle Ho, Matthew Fouesneau, Morgan Nord, Brian Peek, J.E.G. Walmsley, Mike Wu, John F. Avestruz, C. Buck, Tobias Brescia, Massimo Finkbeiner, Douglas P. Goulding, Andy D. Kacprzak, T. Melchior, Peter Pasquato, Mario Ramachandra, Nesar Ting, Yuan-Sen van de Ven, Glenn Villar, Soledad Villar, V.A. Zinger, Elad SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France Königstuhl 17 HeidelbergD-69117 Germany Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy University of Manchester ManchesterM13 9PL United Kingdom Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Leinweber Center for Theoretical Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 Heidelberg69120 Germany Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Straße 2 Heidelberg69120 Germany Department of Physics "E. Pancini " University Federico II of Napoli Via Cinthia 21 NapoliI-80126 Italy INAF Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Department of Physics Harvard University 17 Oxford St. CambridgeMA02138 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Swiss Data Science Center Paul Scherrer Institute Villigen5303 Switzerland Center for Statistics & Machine Learn

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Astrometry and Precise Radial Velocities Yield a Complete Orbital Solution for the Nearby Eccentric Brown Dwarf LHS 1610 b

arXiv

引用

arXiv 2023年

作者： Fitzmaurice, Evan Stefánsson, Guðmundur Kavanagh, Robert D. Mahadevan, Suvrath Cañas, Caleb I. Winn, Joshua N. Robertson, Paul Ninan, Joe P. Albrecht, Simon Callingham, J.R. Cochran, William D. Delamer, Megan Kanodia, Shubham Lin, Andrea S.J. Marcussen, Marcus L. Pope, Benjamin J.S. Ramsey, Lawrence W. Roy, Arpita Vedantham, Harish Wright, Jason T. Department of Astronomy & Astrophysics Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences Scholar NASA Sagan Fellow United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands NASA Postdoctoral Fellow United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C8000 Denmark Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands McDonald Observatory Department of Astronomy The University of Texas at Austin 2515 Speedway AustinTX78712 United States Center for Planetary Systems Habitability The University of Texas at Austin 2515 Speedway AustinTX78712 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States School of Mathematics & Physics University of Queensland St LuciaQLD4072 Australia Centre for Astrophysics University of Southern Queensland West Street ToowoombaQLD4350 Australia Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 N Charles St BaltimoreMD21218 United States Kapteyn Astronomical Institute University of Groningen

We characterize the LHS 1610 system, a nearby (d = 9.7 pc) M5 dwarf hosting a brown dwarf in a 10.6 day, eccentric (e ∼ 0.37) orbit. A joint fit of the available Gaia two-body solution, discovery radial velocities (RVs) from TRES, and new RVs obtained with the Habitable-zone Planet Finder, yields an orbital inclination of 117.2 ±0.9◦ and a mass constraint of 50.9 ±0.9 MJ. This gives LHS 1610 b the second most precise mass of brown dwarfs orbiting M stars within 25pc. We highlight a discrepancy between the Gaia two-body solution eccentricity (e = 0.52±0.03) and that from the RVs (e = 0.3702± 0.0003), which requires the astrometric time-series release (Gaia DR4) for further diagnostics. With a flare rate of 0.28 ±0.07 flares/day from TESS photometry, and a rotation period of 84 ±8 days, LHS 1610 joins other mid M stars—including Proxima Centauri and YZ Ceti—as nearby mid M dwarfs with flare rates on the higher end for their long rotation periods. These stars are promising candidates for searching for sub-Alfvénic star-companion interactions, raising the question whether LHS 1610 b could be driving the flares on its host star. However, the available TESS photometry is insufficient to confirm or rule out any orbital phase-dependence of the flares. We show that the LHS 1610 system, as a nearby mid M star with a large, short-period companion, is a promising target to look for evidence of star-companion interactions or aural emission from the brown dwarf at radio wavelengths. Copyright © 2023, The Authors. All rights reserved.

关键词： Stars

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Widespread Rapid Quenching at Cosmic Noon Revealed by JWST Deep Spectroscopy

arXiv

引用

arXiv 2024年

作者： Park, Minjung Belli, Sirio Conroy, Charlie Johnson, Benjamin D. Davies, Rebecca L. Leja, Joel Tacchella, Sandro Mendel, J. Trevor Benton, Chloë Bugiani, Letizia Emami, Razieh Khoram, Amir H. Li, Yijia Maheson, Gabriel Mathews, Elijah P. Naidu, Rohan P. Nelson, Erica J. Terrazas, Bryan A. Weinberger, Rainer Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Dipartimento di Fisica e Astronomia Università di Bologna Bologna40129 Italy Centre for Astrophysics and Supercomputing Swinburne University of Technology HawthornVIC3122 Australia Australia Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Kavli Institute for Cosmology University of Cambridge CambridgeCB3 0EZ United Kingdom Cavendish Laboratory University of Cambridge CambridgeCB3 0EZ United Kingdom Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia De artment for Astro h sical and Planetar Science Universit of Colorado BoulderCO80305 United States MIT Kavli Institute for Astro h sics and S ace Research CambridgeMA02139 United States Department of Physics & Astronomy Oberlin College OberlinOH44074 United States Leibniz Institute for Astrophysics An der Starnwarte 16 Potsdam14482 Germany

Massive quiescent galaxies in the young Universe are expected to be quenched rapidly, but it is unclear whether they all experience starbursts before quenching and what physical mechanism drives rapid quenching. We study 14 massive quiescent galaxies (log(M M) > 10) at z ∼ 2 selected from a representative sample of the Blue Jay survey. We reconstruct their star formation histories (SFHs) by fitting spectral energy distribution models to the JWST/NIRSpec R ∼ 1000 spectra. We find that massive quiescent galaxies can be split into three categories with roughly equal numbers of galaxies according to their SFHs: (1) relatively old galaxies quenched at early epochs;(2) galaxies that are rapidly and recently quenched after a flat or bursty formation history (depending on the assumed prior);and (3) galaxies that are rapidly and recently quenched after a major starburst. Most recently quenched galaxies show neutral gas outflows, probed by blueshifted Na I D absorption, and ionized gas emission, with line ratios consistent with active galactic nucleus (AGN) diagnostics. This suggests that AGN activity drives multiphase gas outflows, leading to rapid quenching. By tracing back the SFHs of the entire sample, we predict the number density of massive quiescent galaxies at z = 4–6: n = (1.5–6.0) × 10−5 Mpc−3. The two old massive quiescent galaxies in our sample appear to have extremely early formation and quenching (z 6) and are possibly descendants of early post-starbursts at z > 3. These galaxies still show neutral gas reservoirs and weak Hα emission, perhaps because the ejective AGN feedback that caused rapid quenching has weakened over time. Copyright © 2024, The Authors. All rights reserved.

关键词： Rapid quenching

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Discovery of Ancient Globular Cluster Candidates in The Relic, a Quiescent Galaxy at z=2.5

arXiv

引用

arXiv 2025年

作者： Whitaker, Katherine E. Cutler, Sam E. Chandar, Rupali Pan, Richard Setton, David J. Furtak, Lukas J. Bezanson, Rachel Labbe, Ivo Leja, Joel Suess, Katherine A. Wang, Bingjie Weaver, John R. Atek, Hakim Brammer, Gabriel B. Feldmann, Robert Förster Schreiber, Natascha M. Glazebrook, Karl de Graaff, Anna Greene, Jenny E. Khullar, Gourav Marchesini, Danilo Maseda, Michael V. Miller, Tim B. Mo, Houjun Mowla, Lamiya A. Nanayakkara, Themiya Nelson, Erica J. Price, Sedona H. Rizzo, Francesca van Dokkum, Pieter Williams, Christina C. Zhang, Yanzhe Zhang, Yunchong Zitrin, Adi Department of Astronomy University of Massachusetts AmherstMA01003 United States Denmark Ritter Astrophysical Research Center University of Toledo ToledoOH43606 United States Department of Physics and Astronomy Tufts University 574 Boston Ave. MedfordMA02155 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Department of Physics Ben-Gurion University of the Negev P.O. Box 653 Be’er-Sheva84105 Israel Department of Physics & Astronomy and PITT PACC University of Pittsburgh PittsburghPA15260 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department for Astrophysical & Planetary Science University of Colorado BoulderCO80309 United States Institut d’Astrophysique de Paris CNRS Sorbonne Université 98bis Boulevard Arago Paris75014 France Copenhagen Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen Denmark Department of Astrophysics Universität Zürich Winterthurerstrasse 190 ZurichCH-8044 Switzerland Max-Planck-Institut für extraterrestrische Physik Giessenbachstrasse 1 Garching85748 Germany Centre for Astrophysics and Supercomputing Swinburne University of Technology PO Box 218 HawthornVIC3122 Australia Max-Planck-Institut für Astronomie Königstuhl 17 HeidelbergD-69117 Germany Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Department of Astronomy University of Washington Physics-Astronomy Building Box 351580 SeattleWA98195-1700 United States Department of Physics & Astronomy Tufts Unive

Globular clusters (GCs) are some of the oldest bound structures in the Universe, holding clues to the earliest epochs of star formation and galaxy assembly. However, accurate age measurements of ancient clusters are challenging due to the age-metallicity degeneracy. Here, we report the discovery of 42 compact stellar systems within the’Relic’, a massive, quiescent galaxy at z = 2.53. The Relic resides in an overdensity behind the Abell 2744 cluster, with a prominent tidal tail extending towards two low-mass companions. Using deep data from the UNCOVER/MegaScience JWST Surveys, we find that clusters formed in age intervals ranging from 8 Myr up to ∼ 2 Gyr, suggesting a rich formation history starting at z ∼ 10. While the cluster-based star formation history is broadly consistent with the high past star formation rates derived from the diffuse host galaxy light, one potential discrepancy is a tentative ∼ 2 − 3× higher rate in the cluster population for the past Gyr. Taken together with the spatial distribution and low inferred metallicities of these young-to-intermediate age clusters, we may be seeing direct evidence for the accretion of star clusters in addition to their early in-situ formation. The cluster masses are high, ∼ 106 − 107 M☉, which may explain why we are able to detect them around this likely post-merger galaxy. Overall, the Relic clusters are consistent with being precursors of the most massive present-day GCs. This unique laboratory enables the first connection between long-lived, high-redshift clusters and local stellar populations, offering insights into the early stages of GC evolution and the broader processes of galaxy assembly. © 2025, CC BY.

关键词： Galaxies

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Timescale of twin-peak quasi-periodic oscillations and mass of accreting neutron stars

arXiv

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arXiv 2019年

作者： Török, Gabriel Goluchová, Kateřina Šrámková, Eva Urbanec, Martin Straub, Odele Institute of Physics and Research Centre for Computational Physics and Data Processing Silesian University in Opava Bezručovo nám. 13 OpavaCZ-74601 Czech Republic Max Planck Institute for extraterrestrial Physics Gießenbachstraße 1 Garching85748 Germany

Einstein’s general relativity predicts that orbital motion of accreted gas approaching a neutron star (NS) in a NS low-mass X-ray binary (LMXB) system occurs on a time scale proportional to the NS mass. Radiation of the gas accounts for most of the observed LMXBs variability. In more than a dozen of sources twin-peak quasi-periodic oscillations (QPOs) have been observed. Inspired by the expected proportionality between periods of orbital motion and NS mass we present a straightforward comparison among these sources. We investigate relations between QPO periods and their ratios and identify characteristic time scales of QPOs associated to individual sources. These timescales are likely determined by the relative mass of each NS. We show that the characteristic time scale of the millisecond pulsar XTE J1807.4-294 is longer than for most other NS LMXBs. Consequently, models of QPOs that consider geodesic orbital frequencies imply that the X-ray pulsars’ mass has to be about 50% higher than the average mass of other sources. Consideration of other X-ray pulsars indicates that the exceptionality of XTE J1807.4-294 cannot be related to NS magnetic field in any simple manner. We suggest that QPOs observed in this source can help to discriminate between the proposed versions of the NS equation of state. Copyright © 2019, The Authors. All rights reserved.

关键词： Time measurement

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Physical Publicly Verifiable Randomness from Pulsars

arXiv

引用

arXiv 2022年

作者： Dawson, Joanne R. Hobbs, George Gao, Yansong Camtepe, Seyit Pieprzyk, Josef Feng, Yi Tranfa, Luke Bradbury, Sarah Zhu, Weiwei Li, Di CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia Department of Physics and Astronomy MQ Research Centre in Astronomy Astrophysics and Astrophotonics Macquarie University NSW2109 Australia School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China CSIRO Data61 PO Box 76 EppingNSW1710 Australia Institute of Computer Science Polish Academy of Sciences Poland National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China BrisbaneQLD4001 Australia NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa

We demonstrate how radio pulsars can be used as random number generators. Specifically, we focus on publicly verifiable randomness (PVR), in which the same sequence of trusted and verifiable random numbers is obtained by multiple parties. PVR is a critical building block for many processes and algorithms (including cryptography, scientific trials, electoral audits and international treaties). However, current approaches (based on number theory) may soon become vulnerable to quantum computers, motivating a growing demand for PVR based on natural physical phenomena. In this context, we explore pulsars as a potential physical PVR source. We first show that bit sequences extracted from the measured flux densities of a bright millisecond pulsar can pass standardised tests for randomness. We then quantify three illustrative methods of bit-extraction from pulsar flux density sequences, using simultaneous observations of a second pulsar carried out with the Parkes telescope in Australia and the Five-hundred-metre Aperture Spherical radio Telescope (FAST) in China, supported by numerical simulations. We demonstrate that the same bit sequence can indeed be obtained at both observatories, but the ubiquitous presence of radiometer noise needs to be accounted for when determining the expected bit error rate between two independent sequences. We discuss our results in the context of an imaginary use-case in which two mutually distrusting parties wish to obtain the same random bit sequence, exploring potential methods to mitigate against a malicious participant. © 2022, CC BY-NC-ND.

关键词： Random processes

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UNCOVER: The growth of the first massive black holes from JWST/NIRSpec – spectroscopic redshift confirmation of an X-ray luminous AGN at z=10.1

arXiv

引用

arXiv 2023年

作者： Goulding, Andy D. Greene, Jenny E. Setton, David J. Labbe, Ivo Bezanson, Rachel Miller, Tim B. Atek, Hakim Bogdán, Ákos Brammer, Gabriel Chemerynska, Iryna Cutler, Sam E. Dayal, Pratika Fudamoto, Yoshinobu Fujimoto, Seiji Furtak, Lukas J. Kokorev, Vasily Khullar, Gourav Leja, Joel Marchesini, Danilo Natarajan, Priyamvada Nelson, Erica Oesch, Pascal A. Pan, Richard Papovich, Casey Price, Sedona H. van Dokkum, Pieter Wang, Bingjie Weaver, John R. Whitaker, Katherine E. Zitrin, Adi Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy PITT PACC University of Pittsburgh PittsburghPA15260 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Department of Astronomy Yale University New HavenCT06511 United States Department of Physics and Astronomy Northwestern University 1800 Sherman Ave EvanstonIL60201 United States Institut d’Astrophysique de Paris CNRS Sorbonne Université 98bis Boulevard Arago Paris75014 France Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Niels Bohr Institute University of Copenhagen Jagtvej 128 København NDK-2200 Denmark Department of Astronomy University of Massachusetts AmherstMA01003 United States Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo Japan Department of Astronomy The University of Texas at Austin AustinTX78712 United States Physics Department Ben-Gurion University of the Negev P.O. Box 653 Be’er-Sheva84105 Israel Kapteyn Astronomical Institute University of Groningen P.O. Box 800 Groningen9700AV Netherlands Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Physics and Astronomy Department Tufts University 574 Boston Ave. MedfordMA02155 United States Department of Physics Yale University New HavenCT06520 United States Black Hole Initiative

The James Webb Space Telescope is now detecting early black holes (BHs) as they transition from "seeds" to supermassive BHs. Recently, Bogdan et al. (2023) reported the detection of an X-ray luminous supermassive BH, UHZ-1, with a photometric redshift at z > 10. Such an extreme source at this very high redshift provides new insights on seeding and growth models for BHs given the short time available for formation and growth. Harnessing the exquisite sensitivity of JWST/NIRSpec, here we report the spectroscopic confirmation of UHZ-1 at z = 10.073±0.002. We find that the NIRSpec/Prism spectrum is typical of recently discovered z ≈ 10 galaxies, characterized primarily by star-formation features. We see no clear evidence of the powerful X-ray source in the rest-frame UV/optical spectrum, which may suggest heavy obscuration of the central BH, in line with the Compton-thick column density measured in the X-rays. We perform a stellar population fit simultaneously to the new NIRSpec spectroscopy and previously available photometry. The fit yields a stellar mass estimate for the host galaxy that is significantly better constrained than prior photometric estimates (M* ∼ 1.4+0−034 × 108 M☉). Given the predicted BH mass (MBH ∼ 107 − 108 M☉), the resulting ratio of MBH/M* remains two to three orders of magnitude higher than local values, thus lending support to the heavy seeding channel for the formation of supermassive BHs within the first billion years of cosmic evolution. Copyright © 2023, The Authors. All rights reserved.

关键词： Black holes

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

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