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arXiv 2024年

作者： Gianolli, Vittoria Elvezia Bianchi, S. Petrucci, P.-O. Brusa, M. Chartas, G. Lanzuisi, G. Matzeu, G.A. Parra Ursini, F. Behar, E. Bischetti, M. Comastri, A. Costantini, E. Cresci, G. Dadina, M. De, B. Marco, M. De Rosa, A. Fiore, F. Gaspari, M. Gilli, R. Giustini, M. Guainazzi, M. King, A.R. Kraemer, S. Kriss, G. Krongold, Y. La Franca, F. Longinotti, A.L. Luminari, A. Maiolino Marconi, R.A. Mathur, S. Matt, G. Mehdipour, M. Merloni, A. Middei, R. Miniutti, G. Nardini, E. Panessa, F. Perna, M. Piconcelli, E. Ponti, G. Ricci, F. Serafinelli, R. Tombesi, F. Vignali, C. Zappacosta, L. Université Grenoble Alpes CNRS IPAG Grenoble38000 France Dipartimento di Matematica e Fisica Università degli Studi Roma Tre Via della Vasca Navale 84 Roma00146 Italy University of Bologna Via Gobetti 93/2 BolognaI-40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Gobetti 93/3 BolognaI-40129 Italy Villanueva de la Cañada MadridE-28691 Spain Department of Physics and Astronomy College of Charleston CharlestonSC29424 United States Physics Department The Technion Haifa32000 Israel Dipartimento di Fisica Universitá di Trieste Sezione di Astronomia Via G.B. Tiepolo 11 TriesteI-34131 Italy SRON Netherlands Institute for Space Research Niels Bohrweg 4 CA Leiden2333 Netherlands INAF Osservatorio Astrofisico di Arcetri Largo Enrico Fermi 5 Firenze50125 Italy Departament de Física EEBE Universitat Politècnica de Catalunya Av. Eduard Maristany 16 Barcelona08019 Spain INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544-1001 United States CSIC-INTA Campus ESAC Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Keplerlaan 1 Noordwijk2201 AZ Netherlands Department of Physics & Astronomy University of Leicester LeicesterLE1 7RH United Kingdom Astronomical Institute Anton Pannekoek University of Amsterdam Science Park 904 NL-1098 XH Netherlands Department of Physics Institute for Astrophysics and Computational Sciences The Catholic University of America WashingtonDC20064 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Instituto de Astronomía Universidad Nacional Autónoma de México Circuito Exterior Ciudad Universitaria Ciudad de México04510 Mexico INAF Istituto di Astrofisica e Planetologia Spaziali Via Fosso del Cavaliere Roma00133 Italy INAF Osservatorio Astronomico di Roma Via Frascati 33 Monte Po

The detection of blueshifted absorption lines likely associated with ionized iron K-shell transitions in the X-ray spectra of many active galactic nuclei (AGNs) suggests the presence of a highly ionized gas outflowing with mildly relativistic velocities (0.03c - 0.6c) named ultra-fast outflow (UFO). Within the SUBWAYS project, we characterized these winds starting from a sample of 22 radio-quiet quasars at an intermediate redshift (0.1 ≤ z ≤ 0.4) and compared the results with similar studies in the literature on samples of local Seyfert galaxies (i.e., 42 radio-quiet AGNs observed with XMM-Newton at z ≤ 0.1) and high redshift radio-quiet quasars (i.e., 14 AGNs observed with XMM-Newton and Chandra at z ≥ 1.4). The scope of our work is a statistical study of UFO parameters and incidence considering the key physical properties of the sources, such as supermassive black hole (SMBH) mass, bolometric luminosity, accretion rates, and spectral energy distribution (SED) with the aim of gaining new insights into the UFO launching mechanisms. We find indications that highly luminous AGNs with a steeper X-ray/UV ratio, αox, are more likely to host UFOs. The presence of UFOs is not significantly related to any other AGN property in our sample. These findings suggest that the UFO phenomenon may be transient. Focusing on AGNs with UFOs, other important findings from this work include: (1) faster UFOs have larger ionization parameters and column densities;(2) X-ray radiation plays a more crucial role in driving highly ionized winds compared to UV;(3) the correlation between outflow velocity and luminosity is significantly flatter than what is expected for radiatively driven winds;(4) more massive black holes experience higher wind mass losses, suppressing the accretion of matter onto the black hole;(5) the UFO launching radius is positively correlated with the Eddington ratio. Furthermore, our analysis suggests the involvement of multiple launching mechanisms, including radiation p

关键词： Active Galactic Nuclei

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

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arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Stellar activity manifesting at a one year alias explains Barnard b as a false positive

arXiv

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arXiv 2021年

作者： Lubin, Jack Robertson, Paul Stefansson, Gudmundur Ninan, Joe Mahadevan, Suvrath Endl, Michael Ford, Eric Wright, Jason T. Beard, Corey Bender, Chad Cochran, William D. Diddams, Scott A. Fredrick, Connor Halverson, Samuel Kanodia, Shubham Metcalf, Andrew J. Ramsey, Lawrence Roy, Arpita Schwab, Christian Terrien, Ryan Department of Physics and Astronomy University of California Irvine 4129 Frederick Reines Hall IrvineCA92697 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States Department of Astronomy and Astrophysics Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Center for Exoplanets and Habitable Worlds Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States McDonald Observatory and Center for Planetary Systems Habitability University of Texas at Austin AustinTX78730 United States Institute for Computational and Data Sciences Pennsylvania State University University ParkPA16802 United States Steward Observatory University of Arizona 933 N Cherry Ave. TucsonAZ85721 United States McDonald Observatory and Center for Planetary Systems Habitability University of Texas at Austin AustinTX78712 United States Time and Frequency Division National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Space Vehicles Air Force Research Laboratory 3550 Aberdeen Ave SE Kirtland AFBNM87117 United States 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 Department of Physics and Astronomy Macquarie University Balaclava Road North RydeNSW2109 Australia Department of Physics and Astronomy Carleton College One North College Street NorthfieldMN55057 United States

Barnard's star is among the most studied stars given its proximity to the Sun. It is often considered the Radial Velocity (RV) standard for fully convective stars due to its RV stability and equatorial declination. Recently, an Msin i = 3:3M⊕ super-Earth planet candidate with a 233 day orbital period was announced by Ribas et al. (2018). New observations from the nearinfrared Habitable-zone Planet Finder (HPF) Doppler spectrometer do not show this planetary signal. We ran a suite of experiments on both the original data and a combined original + HPF data set. These experiments include model comparisons, periodogram analyses, and sampling sensitivity, all of which show the signal at the proposed period of 233 days is transitory in nature. The power in the signal is largely contained within 211 RVs that were taken within a 1000 day span of observing. Our preferred model of the system is one which features stellar activity without a planet. We propose that the candidate planetary signal is an alias of the 145 day rotation period. This result highlights the challenge of analyzing long-term, quasi-periodic activity signals over multi-year and multi-instrument observing campaigns. Copyright © 2021, The Authors. All rights reserved.

关键词： Stars

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Deep ASKAP EMU Survey of the GAMA23 field: Properties of radio sources

arXiv

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arXiv 2022年

作者： Uygun, Gulay Gurkan Prandoni, I. O'Brien, A. Raja, W. Marchetti, L. Vaccari, M. Driver, S. Taylor, E. Franzen, T. Brown, M.J.I. Shabala, S. Andernach, H. Hopkins, A.M. Norris, R.P. Leahy, D. Bilicki, M. Farajollahi, H. Galvin, T. Heald, G. Koribalski, B.S. An, T. Warhurst, K. Thüringer Landessternwarte Sternwarte 5 TautenburgD-07778 Germany CSIRO Space and Astronomy ATNF PO Box 1130 BentleyWA6102 Australia INAF-IRA Via P. Gobetti 101 BolognaI-40129 Italy Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States CSIRO Space and Astronomy ATNF P.O. Box 76 EppingNSW1710 Australia Department of Astronomy University of Cape Town Rondebosch Cape Town7701 South Africa Inter-University Institute for Data Intensive Astronomy Department of Astronomy University of Cape Town Rondebosch Cape Town7701 South Africa International Centre for Radio Astronomy Research University of Western Australia Perth Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn3122 Australia ASTRON: the Netherlands Institute for Radio Astronomy PO Box 2 Dwingeloo7990 AA Netherlands School of Physics and Astronomy Monash University ClaytonVIC3800 Australia School of Natural Sciences University of Tasmania Private Bag 37 HobartTAS7001 Australia Depto. de Astronomía DCNE Universidad de Guanajuato Callejón. de Jalisco s/n GuanajuatoCP 36023 Mexico Australian Astronomical Optics Macquarie University 105 Delhi Rd North RydeNSW2113 Australia Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Dept. Physics and Astronomy University of Calgary CalgaryABT2N 1N4 Canada Center for Theoretical Physics Polish Academy of Sciences al. Lotników 32/46 Warsaw02-668 Poland International Centre for Radio Astronomy Research Curtin University 1 Turner Avenue BentleyWA6102 Australia Shanghai Astronomical Observatory Chinese Academy of Sciences Nandan road 80 Shanghai200030 China Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

We present the Australian Square Kilometre Array Pathfinder (ASKAP) observations of the Galaxy and Mass Assembly (GAMA)23h field. The survey was carried out at 887.5 MHz and covers a ∼83 square degree field. We imaged the calibrated visibility data, taken as part of the Evolutionary Mapping of Universe (EMU) Early Science Programme, using the latest version of the ASKAPSoft pipeline. The final mosaic has an angular resolution of 10 arcsec and a central rms noise of around 38 μJy beam−1. The derived radio source catalogue has 39812 entries above a peak flux density threshold of 5σ. We searched for the radio source host galaxy counterparts using the GAMA spectroscopic (with an i-band magnitude limit of 19.2 mag) and multi-wavelength catalogues that are available as part of the collaboration. We identified hosts with GAMA spectroscopic redshifts for 5934 radio sources. We describe the data reduction, imaging, and source identification process, and present the source counts. Thanks to the wide area covered by our survey, we obtain very robust counts down to 0.2 mJy. ASKAP's exceptional survey speed, providing efficient, sensitive and high resolution mapping of large regions of the sky in conjunction with the multi-wavelength data available for the GAMA23 field, allowed us to discover 63 giant radio galaxies. The data presented here demonstrate the excellent capabilities of ASKAP in the pre-SKA era. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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Chronicling the host galaxy properties of the remarkable repeating FRB 20201124A

arXiv

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arXiv 2021年

作者： Fong, Wen-Fai Dong, Yuxin Leja, Joel Bhandari, Shivani Day, Cherie K. Deller, Adam T. Kumar, Pravir Prochaska, J. Xavier Scott, Danica R. Bannister, Keith W. Eftekhari, Tarraneh Gordon, Alexa C. Heintz, Kasper E. James, Clancy W. Kilpatrick, Charles D. Mahony, Elizabeth K. Escorial, Alicia Rouco Ryder, Stuart D. Shannon, Ryan M. Tejos, Nicolas Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Purdue University Department of Physics and Astronomy 525 Northwestern Avenue West LafayetteIN47907 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology HawthornVIC3122 Australia Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States 5-1-5 Kashiwanoha Kashiwa277-8583 Japan International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States Centre for Astrophysics and Cosmology Science Institute University of Iceland Dunhagi 5 Reykjavík107 Iceland Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen Ø2100 Denmark Department of Physics & Astronomy Macquarie University NSW2109 Australia Macquarie University Research Centre for Astronomy Astrophysics & Astrophotonics SydneyNSW2109 Australia Instituto de Física Pontificia Universidad Católica de Valparaíso Casilla Valparaíso 4059 Chile

We present the Australian Square Kilometre Array Pathfinder (ASKAP) localization and follow-up observations of the host galaxy of the repeating fast radio burst (FRB) source, FRB 20201124A, the fifth such extragalactic repeating FRB with an identified host. From spectroscopic observations using the 6.5-m MMT Observatory, we derive a redshift of z = 0.0979 ±0.0001, a star formation rate inferred from Hα emission of SFR(Hα) ≈ 2.1 M☉ yr-1, and a gas-phase metallicity of 12+log(O/H)≈ 9.0. By jointly modeling the 12-filter optical-mid-infrared (MIR) photometry and spectroscopy of the host, we infer a median stellar mass of ≈ 2 × 1010 M☉, internal dust extinction of AV ≈ 1 - 1.5 mag, and a mass-weighted stellar population age of ≈ 5 - 6 Gyr. Connecting these data to the radio and X-ray observations, we cannot reconcile the broad-band behavior with strong AGN activity and instead attribute the dominant source of persistent radio emission to star formation, likely originating from the circumnuclear region of the host. The modeling also indicates a hot dust component contributing to the MIR luminosity at a level of ≈ 10 - 30%. We model the host galaxy's star formation and mass assembly histories, finding that the host assembled > 90% of its mass by 1 Gyr ago and exhibited a fairly constant SFR for most of its existence, with no clear evidence of past star-burst activity. Copyright © 2021, The Authors. All rights reserved.

关键词： Stars

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Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

arXiv

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

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Resolving the nature and putative nebular emission of GS9422: an obscured AGN without exotic stars

arXiv

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arXiv 2024年

作者： Tacchella, Sandro McClymont, William Scholtz, Jan Maiolino, Roberto Ji, Xihan Villanueva, Natalia C. Charlot, Stéphane D'Eugenio, Francesco Helton, Jakob M. Williams, Christina C. Witstok, Joris Bhatawdekar, Rachana Carniani, Stefano Chevallard, Jacopo Curti, Mirko Hainline, Kevin Ji, Zhiyuan Johnson, Benjamin D. Leja, Joel Li, Yijia Maseda, Michael V. Puskás, Dávid Rieke, Marcia Robertson, Brant Shivaei, Irene Silcock, Maddie S. Simmonds, Charlotte Übler, Hannah Willmer, Christopher N.A. Willott, Chris Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cavendish Laboratory University of Cambridge 19 JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Sorbonne Université CNRS UMR 7095 Institut d'Astrophysique de Paris 98 bis bd Arago Paris75014 France Steward Observatory University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States NSF's National Optical-Infrared Astronomy Research Laboratory 950 North Cherry Avenue TucsonAZ85719 United States Camino Bajo del Castillo s/n Madrid Villanueva de la Cañada28692 Spain Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy Department of Physics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching85748 Germany Center for Astrophysics | Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States 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 of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53706 United States Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA96054 United States Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States CSIC-INTA Ctra. de Ajalvir km 4 Torrejón de Ardoz MadridE-28850 Spain Centre for Astrophysics Research Department of Physics Astronomy and Mathematics University of Hertfordshire HatfieldAL10 9AB United Kingdom NRC Herzberg 5071 West Saanich Rd VictoriaBCV9E 2E7 Canada

Understanding the sources that power nebular emission in high-redshift galaxies is fundamentally important not only for shedding light onto the drivers of reionisation, but to constrain stellar populations and the growth of black holes. Here we focus on an individual object, GS9422, a galaxy at zspec = 5.943 with exquisite data from the JADES and JEMS surveys, including 14-band JWST/NIRCam photometry and deep NIRSpec prism and grating spectroscopy. We map the continuum emission and nebular emission lines across the galaxy on 0.2-kpc scales. GS9422 has been claimed to have nebular-dominated continuum and an extreme stellar population with top-heavy initial mass *** find clear evidence for different morphologies in the emission lines, the rest-UV and rest-optical continuum emission, demonstrating that the full continuum cannot be dominated by nebular emission. While multiple models reproduce the spectrum reasonably well, our preferred model with a type-2 active galactic nucleus (AGN) and local damped Ly-α (DLA) clouds can explain both the spectrum and the wavelength-dependent morphology. The AGN powers the off-planar nebular emission, giving rise to the Balmer jump and the emission lines, including Ly-α, which therefore does not suffer DLA absorption. A central, young stellar component dominates the rest-UV emission and - together with the DLA clouds - leads to a spectral turn-over. A disc-like, older stellar component explains the flattened morphology in the rest-optical continuum. We conclude that GS9422 is consistent with being a normal galaxy with an obscured, type-2 AGN - a simple scenario, without the need for exotic stellar populations. © 2024, CC BY.

关键词： Galaxies

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Metric Assisted Stochastic Sampling (MASS) search for gravitational waves from binary black hole mergers

arXiv

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arXiv 2021年

作者： Hanna, Chad Joshi, Prathamesh Huxford, Rachael Cannon, Kipp Caudill, Sarah Chan, Chiwai Cousins, Bryce Creighton, Jolien D.E. Ewing, Becca Fernandez, Miguel Fong, Heather Godwin, Patrick Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Ohta, Hiroaki Pace, Alexander Privitera, Stephen Sachdev, Surabhi Sakon, Shio Singh, Divya Tapia, Ron Tsukada, Leo Tsuna, Daichi Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Wang, Jonathan Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States RESCEU The University of Tokyo Tokyo113-0033 Japan Nikhef Science Park Amsterdam1098 XG Netherlands Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States Graduate School of Science The University of Tokyo Tokyo113-0033 Japan LIGO Laboratory California Institute of Technology MS 100-36 PasadenaCA91125 United States LIGO Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Albert-Einstein-Institut Max-Planck-Institut für Gravitationsphysik Potsdam-GolmD-14476 Germany Department of Physics Hayes Hall Kenyon College GambierOH43022 United States Department of Physics University of Michigan Ann ArborMI48109 United States

We present a novel gravitational wave detection algorithm that conducts a matched filter search stochastically across the compact binary parameter space rather than relying on a fixed bank of template waveforms. This technique is competitive with standard template-bank-driven pipelines in both computational cost and sensitivity. However, the complexity of the analysis is simpler allowing for easy configuration and horizontal scaling across heterogeneous grids of computers. To demonstrate the method we analyze approximately one month of public LIGO data from July 27 00:00 2017 UTC – Aug 25 22:00 2017 UTC and recover eight known confident gravitational wave candidates. We also inject simulated binary black hole (BBH) signals to demonstrate the sensitivity. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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Adaptive coupling of a deep neural network potential to a classical force field

arXiv

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arXiv 2018年

作者： Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science and Beijing International Center for Mathematical Research Peking University China Beijing Institute of Big Data Research Beijing100871 China

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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The optical light curve of GRB 221009A: the afterglow and the emerging supernova

arXiv

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arXiv 2023年

作者： Fulton, M.D. Smartt, S.J. Rhodes, L. Huber, M.E. Villar, V.A. Moore, T. Srivastav, S. Schultz, A.S.B. Chambers, K.C. Izzo, L. Hjorth, J. Chen, T.-W. Nicholl, M. Foley, R.J. Rest, A. Smith, K.W. Young, D.R. Sim, S.A. Bright, J. Zenati, Y. de Boer, T. Bulger, J. Fairlamb, J. Gao, H. Lin, C.-C. Lowe, T. Magnier, E.A. Smith, I.A. Wainscoat, R. Coulter, D.A. Jones, D.O. Kilpatrick, C.D. McGill, P. Ramirez-Ruiz, E. Lee, K.-S. Narayan, G. Ramakrishnan, V. Ridden-Harper, R. Singh, A. Wang, Q. Kong, A.K.H. Ngeow, C.-C. Pan, Y.-C. Yang, S. Davis, K.W. Piro, A.L. Rojas-Bravo, C. Sommer, J. Yadavalli, S.K. Astrophysics Research Centre School of Mathematics and Physics Queen’s University BelfastBT7 1NN United Kingdom Department of Physics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Astronomy and Astrophysics Pennsylvania State University 525 Davey Laboratory 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 DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen2200 Denmark Technische Universität München TUM School of Natural Sciences Physik-Department James-Franck-Straße 1 Garching85748 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild Straße 1 Garching85748 Germany Birmingham Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham BirminghamB15 2TT United Kingdom Department of Astronomy and Astrophysics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States Institute for Astronomy University of Hawai’i 34 Ohia Ku St. PukalaniHI96768-8288 United States Gemini Observatory NSF’s NOIRLab 670 N. A’ohoku Place HiloHI96720 United States Northwestern University 1800 Sherman Ave EvanstonIL60201 United States Department of Physics and Astronomy Purdue University 525 Northwestern Avenue West LafayetteIN47907 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green St. IL61801 United States Center for Astrophysical Surveys National Center for Super

We present extensive optical photometry of the afterglow of GRB 221009A. Our data cover 0.9 − 59.9 days from the time of Swift and Fermi GRB detections. Photometry in rizy-band filters was collected primarily with Pan-STARRS and supplemented by multiple 1- to 4-meter imaging facilities. We analyzed the Swift X-ray data of the afterglow and found a single decline rate power-law f(t) ∝ t−1.556±0.002 best describes the light curve. In addition to the high foreground Milky Way dust extinction along this line of sight, the data favour additional extinction to consistently model the optical to X-ray flux with optically thin synchrotron emission. We fit the X-ray-derived power-law to the optical light curve and find good agreement with the measured data up to 5 − 6 days. Thereafter we find a flux excess in the riy bands which peaks in the observer frame at ∼ 20 days. This excess shares similar light curve profiles to the type Ic broad-lined supernovae SN 2016jca and SN 2017iuk once corrected for the GRB redshift of z = 0.151 and arbitrarily scaled. This may be representative of a supernova emerging from the declining afterglow. We measure rest-frame absolute peak AB magnitudes of Mg = −19.8 ± 0.6 and Mr = −19.4 ± 0.3 and Mz = −20.1 ± 0.3. If this is an SN component, then Bayesian modelling of the excess flux would imply explosion parameters of Mej = 7.1+2.4−1.7 M, MNi = 1.0+0.6−0.4 M, and vej = 33, 900+5−5,900700 km s−1, for the ejecta mass, nickel mass and ejecta velocity respectively, inferring an explosion energy of Ekin ' 2.6 − 9.0 × 1052 ergs. © 2023, CC BY.

关键词： Photometry

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