DOCSLIB.ORG

Deep Learning in Radio Astronomy

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Deep Learning in Radio Astronomy Deep Learning in Radio Astronomy Dissertation zur Erlangung des Doktorgrades an der Fakultät für Mathematik, Informatik und Naturwissenschaften Fachereich Physik der Universität Hamburg vorgelegt von Vesna Lukic Hamburg, Wintersemester 2019 1 Gutachter/innen der Dissertation: Prof. Dr. Marcus Brüggen Zusammensetzung der Prüfungskommission: Prof. Dr. Marcus Brüggen Prof. Dr. Gregor Kasiescka Prof. Dr. Francesco De Gasperin Prof. Dr. Jochen Liske Prof. Dr. Robi Banerjee Vorsitzende/r der Prüfungskommission: Prof. Dr. Jochen Liske Datum der Disputtation: 26.11.2019 Vorsitzender Fach-Promotionsausschusses PHYSIK: Prof. Dr. Günter H. W. Sigl Leiter des Fachbereichs PHYSIK: Prof. Dr. Michael Pothoff Dekan der Fakultät MIN: Prof. Dr. Heinrich Graener 2 3 Zusammenfassung Aktuelle und kommende radioastronomische Himmelsvermessungen liefern weiterhin neue Erkenntnisse über die Entstehung und Entwicklung von Galaxien, unseres kosmologischen Modells und seiner Parameter. Die vorliegende Arbeit fasst unsere Arbeiten zu Tiefenlerntech- niken für die Radioastronomie zusammen. Das Datenvolumen, das bei radioastronomische Durchmusterungen anfällt, ist enorm und nimmt aufgrund von technologischen Verbesserun- gen ständig zu. Dies führt zu einer steigenden Nachfrage nach der Entwicklung komplexerer Tools zur Analyse der Daten, da manuelle Analysen nicht mehr möglich sind. Um diesen Prozess zu vereinfachen, wurden maschinelle Lerntechniken entwickelt, die sich auf die Vo- raussetzung stützen, dass sie zum Identifizieren von Mustern und Merkmalen in Daten ver- wendet werden können. Der Schwerpunkt der vorliegenden Arbeit liegt auf der Analyse von Radiodaten auf der Basis von Bildern mit Hilfe von Deep-Learning-Techniken, ein Ansatz, der sich bei hochdimensionalen Daten bewährt hat. Mit Hilfe von Radio-Galaxien-Bildern aus dem Radio Galaxy Zoo Citizen Science Project demonstrieren wir, dass es möglich ist, drei verschiedene Klassen von Quellen zu klassifizieren. Anschließend testen unser Daten- netzwerk mit Daten Release 1. Wir vergleichen die Leistung traditioneller, tief neuronaler Faltungsnetzwerke mit der Leistung von Kapseln Netzwerke. Letztere sind eine in jüngerer Zeit entwickelte Technik, bei der Gruppen von Neuronen verwendet werden, die Eigenschaften eines Bildes einschließlich der relativen räumlichen Positionen von Merkmalen beschreiben. Anhand von Bildern aus der LOFAR-Zwei-Meter-Himmelsvermessung (LoTSS) zeigen wir, dass die herkömmlichen neuronalen Faltungsnetze für die Art der vorliegenden Funkgalax- iendaten eine bessere Leistung erbringen. Schließlich entwickeln wir einen Quellensucher, der auf einem Faltungsautocodierer basiert ist, und vergleichen ihn mit einem hochmodernen Quellensucher unter Verwendung simulierter Quadratkilometer-Array-Daten. Wir stellen fest, dass die Leistung zwischen den Quellenfindern je nach Belichtungszeit, Frequenz und Signal- Rausch-Verhältnis sich variiert. 4 This thesis is based on the following publications: V. Lukic, M. Brüggen, J K Banfield, O. I. Wong, L. Rudnick, R. P. Norris, B. Simmons • ‘Radio Galaxy Zoo: Compact and Extended Source Classification with Deep Learning’, Mon. R. Astro. Soc. 466,1, pp. 246-260 (2018) V. Lukic, M. Brüggen, B. Mingo, J.H. Croston, G. Kasieczka, P. N. Best ‘Morphological • classification of radio galaxies: capsule networks versus convolutional neural networks’, Mon. R. Astro. Soc. 487, 2, pp. 1729-1744 (2019) V. Lukic, F. De Gasperin, M. Brüggen ‘AutoSource: Radio-astronomical source-finding • with convolutional autoencoders’, submitted to Galaxies, arXiv:1910.03631 (2019) 5 Abstract Current and forthcoming radio surveys continue to provide new insights in understanding the formation and evolution of galaxies, our cosmological model and its parameters. The current thesis summarises our work on deep learning techniques applied to radio astronomy. The volume of data produced from radio surveys is vast and constantly growing due to improvements to technology. This results in increasing demand to develop more sophisticated tools to analyse the data, as manual analyses will become unfeasible. Machine learning techniques have been developed to facilitate this process, and rely on the premise that they can be trained to recognise patterns and features in data. The focus of the current thesis is analysing radio data based on images using deep learning techniques, an approach which has proven successful on high-dimensional data. Using radio galaxy images from the citizen science project Radio Galaxy Zoo, we show that it is possible to classify between compact sources and three classes of extended sources, and test our trained network on Data Release 1. We compare the performance of traditional convolutional deep neural networks against Capsule networks. The latter are a more recently developed technique using groups of neurons that describe properties of an image including the relative spatial locations of features. Using images from the LOFAR Two-metre Sky Survey (LoTSS), we show that for the type of radio galaxy data at hand, the traditional convolutional neural networks perform better. Finally, we develop a source finder based on a convolutional autoencoder, and compare the performance against a state-of-the-art source-finder, using simulated Square Kilometre Array data. We find the performance varies between the source-finders based on exposure time, frequency and signal-to-noise ratio. 6 7 Eidesstattliche Versicherung Hiermit versichere ich an Eides statt, die vorliegende Dissertationsschrift selbst verfasst und keine anderen als die angegebenen Hilfsmittel und Quellen benutzt zu haben. Die eingereichte schriftliche Fassung entspricht der auf dem elektronischen Speichermedium. Die Dissertation wurde in der vorgelegten oder einer ähnlichen Form nicht schon einmal in einem früheren Promotionsverfahren angenommen oder als ungenügend beurteilt. (Vesna Lukic) Brüssel, den 10.10.2019 8 9 Contents 1 Introduction 13 1.1 SourcesintheRadiospectrum .......................... 13 1.1.1 Introduction ................................ 13 1.1.2 Spectral line emission . 13 1.1.3 Thermal emission . 15 1.1.4 Non-thermal emission . 15 1.1.5 Galaxies with significant radio emission . 19 1.1.6 Supernovaremnants ............................ 27 1.2 Radio astronomical telescopes and surveys . 27 1.2.1 Main parameters in surveys . 27 1.2.2 Science goals and purpose of surveys . 30 1.2.3 Past and present (large-scale) surveys . 37 1.3 Machine learning and applications to astronomy . 41 1.3.1 Supervised learning and common techniques . 42 1.3.2 Unsupervised learning and common techniques . 47 1.3.3 Machine learning theory . 48 1.3.4 Machine learning techniques applied to astronomy . 53 2 Compact and Extended Radio Source Classification 63 2.1 Introduction..................................... 63 2.2 Deepneuralnetworks ............................... 66 2.3 Methods....................................... 67 2.3.1 Pre-processing ............................... 67 2.3.2 PyBDSF .................................. 68 2.3.3 Image augmentation . 69 2.3.4 Deep learning algorithms . 70 2.3.5 Selection of sources for two-class classification . 72 2.3.6 Selection of sources for four-class classification . 73 2.4 Resultsfortwoclasses ............................... 77 2.5 Results for four classes . 86 2.5.1 Comparing results with Data Release 1 of the Radio Galaxy Zoo . 88 Contents 10 2.6 Conclusions..................................... 92 3 Convolutional vs Capsule networks 97 3.1 Introduction..................................... 97 3.2 LOFAR HETDEX v1.0 dataset . 99 3.2.1 Source cutouts . 99 3.2.2 Classifications . 100 3.3 Methods....................................... 104 3.3.1 Pre-processing ............................... 104 3.3.2 Image augmentation . 105 3.4 Deep Learning algorithms . 105 3.4.1 Convolutional Neural Networks . 106 3.4.2 Capsule networks . 106 3.4.3 Deep learning parameters . 109 3.5 Results........................................ 111 3.5.1 LOFAR original images . 113 3.5.2 LOFAR original and augmented images . 122 3.5.3 Sigma-clipped images . 125 3.5.4 Additional results . 128 3.6 Conclusions . 131 4 Source-finding with convolutional autoencoders 135 4.1 Introduction..................................... 135 4.1.1 Source-finding at radio frequencies . 135 4.1.2 Types of radio sources . 137 4.1.3 Deep learning . 137 4.1.4 Simulated SKA data . 138 4.2 Methods....................................... 141 4.2.1 Convolutional autoencoders . 141 4.2.2 Pre-processing ............................... 144 4.2.3 Dataset generation . 144 4.2.4 Image augmentation . 146 4.3 Results........................................ 149 4.3.1 Very low significance source metrics at SNR=1 . 151 4.3.2 Low significance source metrics at SNR=2 . 152 4.3.3 High significance source metrics at SNR=5 . 154 4.3.4 Execution times . 158 4.4 Discussion and Conclusions . 159 4.5 Appendix ...................................... 162 Contents 11 5 Conclusions and Outlook 167 6 Bibliography 1 Contents 12 13 1 Introduction The introductory section is composed of three parts; the physics of sources in the radio spectrum, radio surveys and finally machine learning and applications in astronomy, with particular emphasis on its use in radio images. 1.1 Sources in the Radio spectrum 1.1.1 Introduction Radio astronomy is the branch of astronomy concerned with the study of celestial bodies at radio frequencies, which range from 15 MHz to 300 GHz (20m to 1mm in wavelength). The low frequency limit is set by the opacity of the ionosphere and the high frequency
Load more

Recommended publications
  • Journal Issue 26 | October 2019
    journal Issue 26 | October 2019 Communicating Astronomy with the Public Spotlighting a Black Hole What did it take to create the largest outreach campaign for an astronomical result? Tactile Subaru A project to make telescope technology accessible Naming ExoWorlds Update on the IAU100 NameExoWorlds campaign www.capjournal.org As part of the 100th anniversary commemorations, the International Astronomical Union (IAU) is organising the IAU100 NameExoWorlds global competition to allow any country in the world to give a popular name to a selected exoplanet and its News News host star. The final results of the competion will be announced in Decmeber 2019. Credit: IAU/L. Calçada. Editorial Welcome to the 26th edition of the CAPjournal! To start off, the first part of 2019 brought in a radical new era in astronomy with the first ever image showing a shadow of a black hole. For CAPjournal #26, part of the team who collaborated on the promotion of this image hs written a piece to show what it took to produce one of the largest astronomy outreach campaigns to date. We also highlight two other large outreach campaigns in this edition. The first is a peer-reviewed article about the 2016 solar eclipse in Indonesia from the founder of the astronomy website lagiselatan, Avivah Yamani. Next, an update on NameExoWorlds, the largest IAU100 campaign, as we wait for the announcement of new names for the ExoWorlds in December. Additionally, this issue touches on opportunities for more inclusive astronomy. We bring you a peer-reviewed article about outreach for inclusion by Dr. Kumiko Usuda-Sato and the speech “Diversity Across Astronomy Can Further Our Research” delivered by award-winning astronomy communicator Dr.
    [Show full text]
  • Patrick Thaddeus
    PUBLISHED: 19 JUNE 2017 | VOLUME: 1 | ARTICLE NUMBER: 0170 obituary Patrick Thaddeus A pioneer in the field of astrochemistry, Patrick Thaddeus discovered dozens of exotic molecules in space and helped revolutionize our view of the interstellar medium and star formation. atrick Thaddeus did more than anyone telescope operating from a rooftop just a else to demonstrate, as he was fond few hundred yards from Broadway. After Pof saying, that chemistry was not a over two decades of steady mapping with provincial subject that stopped five miles this instrument and a near-duplicate one above our heads. As a pioneer in the field that they installed in Chile in 1982, Pat and of astrochemistry, his elegant laboratory his students obtained what is still today work provided ironclad identifications the most extensive and widely used survey of hundreds of new molecules of of the molecular Milky Way. More than astronomical interest, and his observational 40 years later, both telescopes continue to programme discovered about one-sixth yield important scientific results, including of the ~200 molecules known to exist in the discovery over the past decade of two space. His early recognition that carbon THOMAS DAME new spiral arm features of the Galaxy. monoxide would be an excellent tracer of A total of 24 PhD dissertations have the cold dense regions of space led directly been written based on observations or to the discovery of giant molecular clouds instrumental work with the two telescopes. and a revolution in our understanding of In 1986, Pat, along with several the interstellar medium and star formation.
    [Show full text]
  • List Stranica 1 Od
    list product_i ISSN Primary Scheduled Vol Single Issues Title Format ISSN print Imprint Vols Qty Open Access Option Comment d electronic Language Nos per volume Available in electronic format 3 Biotech E OA C 13205 2190-5738 Springer English 1 7 3 Fully Open Access only. Open Access. Available in electronic format 3D Printing in Medicine E OA C 41205 2365-6271 Springer English 1 3 1 Fully Open Access only. Open Access. 3D Display Research Center, Available in electronic format 3D Research E C 13319 2092-6731 English 1 8 4 Hybrid (Open Choice) co-published only. with Springer New Start, content expected in 3D-Printed Materials and Systems E OA C 40861 2363-8389 Springer English 1 2 1 Fully Open Access 2016. Available in electronic format only. Open Access. 4OR PE OF 10288 1619-4500 1614-2411 Springer English 1 15 4 Hybrid (Open Choice) Available in electronic format The AAPS Journal E OF S 12248 1550-7416 Springer English 1 19 6 Hybrid (Open Choice) only. Available in electronic format AAPS Open E OA S C 41120 2364-9534 Springer English 1 3 1 Fully Open Access only. Open Access. Available in electronic format AAPS PharmSciTech E OF S 12249 1530-9932 Springer English 1 18 8 Hybrid (Open Choice) only. Abdominal Radiology PE OF S 261 2366-004X 2366-0058 Springer English 1 42 12 Hybrid (Open Choice) Abhandlungen aus dem Mathematischen Seminar der PE OF S 12188 0025-5858 1865-8784 Springer English 1 87 2 Universität Hamburg Academic Psychiatry PE OF S 40596 1042-9670 1545-7230 Springer English 1 41 6 Hybrid (Open Choice) Academic Questions PE OF 12129 0895-4852 1936-4709 Springer English 1 30 4 Hybrid (Open Choice) Accreditation and Quality PE OF S 769 0949-1775 1432-0517 Springer English 1 22 6 Hybrid (Open Choice) Assurance MAIK Acoustical Physics PE 11441 1063-7710 1562-6865 English 1 63 6 Russian Library of Science.
    [Show full text]
  • Radio Galaxy Zoo: Compact and Extended Radio Source Classification with Deep Learning
    MNRAS 476, 246–260 (2018) doi:10.1093/mnras/sty163 Advance Access publication 2018 January 26 Radio Galaxy Zoo: compact and extended radio source classification with deep learning V. Lukic,1‹ M. Bruggen,¨ 1‹ J. K. Banfield,2,3 O. I. Wong,3,4 L. Rudnick,5 R. P. Norris6,7 and B. Simmons8,9 1Hamburger Sternwarte, University of Hamburg, Gojenbergsweg 112, D-21029 Hamburg, Germany 2Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia 3ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO), Building A28, School of Physics, The University of Sydney, NSW 2006, Australia 4International Centre for Radio Astronomy Research-M468, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia Downloaded from https://academic.oup.com/mnras/article/476/1/246/4826039 by guest on 23 September 2021 5University of Minnesota, 116 Church St SE, Minneapolis, MN 55455, USA 6Western Sydney University, Locked Bag 1797, Penrith South, NSW 1797, Australia 7CSIRO Astronomy and Space Science, Australia Telescope National Facility, PO Box 76, Epping, NSW 1710, Australia 8Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK 9Center for Astrophysics and Space Sciences, Department of Physics, University of California, San Diego, CA 92093, USA Accepted 2018 January 15. Received 2018 January 9; in original form 2017 September 24 ABSTRACT Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. In the context of radio-interferometric imaging in astronomy, we looked for ways to identify multiple components of individual sources.
    [Show full text]
  • GCSE Astronomy Scheme of Work
    Scheme of Work GCSE (9-1) Astronomy Pearson Edexcel Level 1/Level 2 GCSE (9-1) in Astronomy (1AS0) GCSE Astronomy Scheme of Work Topic 1 Planet Earth Week 1 1.1 The Earth’s structure Specification Maths Related practical Exemplar activities Exemplar resources points skills activities 1.1 Starter: Teacher shows images of the Earth showing its Find useful information in chapter 1 2a 1.2 many diverse surface features and asks the class to of GCSE Astronomy – A Guide for 2b 1.3 a-d share what they know about its shape, size and internal Pupils and Teachers (5th ed.) by structure. Marshall, N. (Mickledore). Pupils study the shape and mean diameter of the Earth (13 000 km). Further useful information in chapter Pupils study the Earth’s interior, its main divisions and 3 of The Planets by Aderin-Pocock, their properties (approximate size, state of matter, M. et al (DK). temperature etc.): o crust o mantle o outer core o inner core. © Pearson Education Ltd 2016 1 GCSE Astronomy Scheme of Work Topic 1 Planet Earth Week 2 1.2 Latitude and longitude Specification Maths Related practical Exemplar activities Exemplar resources points skills activities 1.4 Teacher demonstrates latitude and longitude on a globe Find useful information in chapter 1 5a 1.5 a-h of the Earth. of GCSE Astronomy – A Guide for 5d Pupils and Teachers (5th ed.) by Pupils use globes, maps and/or an atlas to study Marshall, N. (Mickledore). latitude and longitude. Model globes and atlases are Pupils learn that in addition to being simply lines on a available from many retail outlets or map, latitude and longitude are actually angles.
    [Show full text]
  • Inventory of CO2 Available for Terraforming Mars
    PERSPECTIVE https://doi.org/10.1038/s41550-018-0529-6 Inventory of CO2 available for terraforming Mars Bruce M. Jakosky 1,2* and Christopher S. Edwards3 We revisit the idea of ‘terraforming’ Mars — changing its environment to be more Earth-like in a way that would allow terres- trial life (possibly including humans) to survive without the need for life-support systems — in the context of what we know about Mars today. We want to answer the question of whether it is possible to mobilize gases present on Mars today in non- atmospheric reservoirs by emplacing them into the atmosphere, and increase the pressure and temperature so that plants or humans could survive at the surface. We ask whether this can be achieved considering realistic estimates of available volatiles, without the use of new technology that is well beyond today’s capability. Recent observations have been made of the loss of Mars’s atmosphere to space by the Mars Atmosphere and Volatile Evolution Mission probe and the Mars Express space- craft, along with analyses of the abundance of carbon-bearing minerals and the occurrence of CO2 in polar ice from the Mars Reconnaissance Orbiter and the Mars Odyssey spacecraft. These results suggest that there is not enough CO2 remaining on Mars to provide significant greenhouse warming were the gas to be emplaced into the atmosphere; in addition, most of the CO2 gas in these reservoirs is not accessible and thus cannot be readily mobilized. As a result, we conclude that terraforming Mars is not possible using present-day technology. he concept of terraforming Mars has been a mainstay of sci- Could the remaining planetary inventories of CO2 be mobi- ence fiction for a long time, but it also has been discussed from lized and emplaced into the atmosphere via current or plausible 1 a scientific perspective, initially by Sagan and more recently near-future technologies? Would the amount of CO2 that could T 2 by, for example, McKay et al.
    [Show full text]
  • Machine Learning in Large Radio Astronomy Surveys (How to Do Science with Petabytes)
    Machine Learning in Large Radio Astronomy Surveys (How to do Science with Petabytes) Ray Norris, Western Sydney University & CSIRO Astronomy & Space Science, ASKAP: Australian Square Kilometre Array Pathfinder ▪ $185m telescope built by CSIRO, approaching completion ▪ Mission: to solve fundamental problems in astrophysics ▪ “EMU” = Evolutionary Map of the Universe PAFs -> Big Data Data Rate to correlator = 100 Tbit/s = 3000 Blu-ray disks/second = 62km tall stack of disks per day = world internet bandwidth in June 2012 Processed data volume = 70 PB/yr (only store 4 PB/yr) EMU: Evolutionary Map of the Universe ▪ PI Ray Norris ▪ Will survey the whole sky for radio continuum ▪ Will discover ~ 70 million galaxies, ▪ compared to 2.5 million currently known ▪ Will revolutionize our view of the Universe ▪ Will revolutionize the way we do astronomy ▪ “large-n astronomy” ASKAP Radio Continuum survey: EMU = 70 million NVSS=1.8 million current total=2.5 million From Norris, 2017, Nature Astronomy, 1,671 1940 1980 2020 EMU Team: ~300 scientists in 21 countries Key Title Project Leader project KP1. EMU Value-Added Catalogue Nick Seymour (Curtin) KP2. Characterising the Radio Sky Ian Heywood (Oxford) KP3. EMU Cosmology David Parkinson (KASA, Korea) KP4. Cosmic Web Shea Brown (Iowa) KP5. Clusters of Galaxies Melanie Johnston-Hollitt (NZ) KP6. cosmic star formation history Andrew Hopkins (AAO) KP7. Evolution of radio-loud AGN Anna Kapinska (UWA) KP8. Radio AGN in the EoR Jose Afonso (Lisbon) KP9. Radio-quiet AGN Isabella Prandoni (Bologna) KP10. Binary super-massive black holes Roger Deane (Cape Town) KP11. Local Universe Josh Marvil (NRAO) KP12. The Galactic Plane Roland Kothes (Canada) KP13.
    [Show full text]
  • SPARCS VII Final Programme
    v1.1 19/07/2017 Monday 17th July – EMU Collaboration Meeting – All welcome Session 1 — Chair: Anna Kapinska 09:00–09:15 Nick Seymour Welcome and Logistics 09:15–09:35 Ray Norris ASKAP & EMU 09:35–09:55 Josh Marvil ASKAP Commissioning and Early Science update 09:55–10:55 Discussion ASKAP Early Science process (observing, calibration, data reduction, validation, value added data) 10:55–11:25 COFFEE BREAK Session 2 — Chair: Josh Marvil 11:25–11:40 Andrew Hopkins ASKAP Pre-Early Science: GAMA23 field 11:40–11:55 Jordan Collier Radio SEDs in EMU Early Science fields 11:55–12:15 Adriano Updates from the SCORPIO project: studying resolved Ingallinera Galactic sources 12:15–12:30 Ray Norris EMU Cosmology (KSP3 update) 12:30–13:50 LUNCH BREAK Session 3 — Chair: Ray Norris 13:50–14:15 Anna Kapinska Radio-loud AGN (KSP7 update) 14:30–14:30 Luke Davies EMU cosmic Star Formation History and stellar mass growth (KSP6 update) 14:30–15:00 Discussion Engagement in EMU 15:00–15:30 COFFEE BREAK Session 4 – Chair: Josh Marvil 15:30–15:45 Minh Huynh CASDA 15:45–17:00 Minh Huynh, CASDA tutorial and discussion Josh Marvil Tuesday 18th July – Updates from Pathfinders Session 1 – Chair: Nick Seymour 09:00-09:30 Rob Beswick The e-MERLIN Legacy Programme 09:30-10:00 Bradley Frank Continuum Science with MeerKAT 10:00-10:30 Discussion What are the common issues faced by the Pathfinders? 10:30-11:00 COFFEE BREAK Session 2 – Chair: George Heald 11:00-11:30 Jess Broderick LOFAR: Recent Highlights and Future Prospects 11:30-12:00 Natasha Continuum Surveys with the MWA
    [Show full text]
  • Help Find the Location of Newly Discovered Black Holes in the LOFAR Radio Galaxy Zoo Project 26 February 2020
    Help find the location of newly discovered black holes in the LOFAR Radio Galaxy Zoo project 26 February 2020 Scientists are asking for the public's help to find the origin of hundreds of thousands of galaxies that have been discovered by the largest radio telescope ever built: LOFAR. Where do these mysterious objects that extend for thousands of light-years come from? A new citizen science project, LOFAR Radio Galaxy Zoo, gives anyone with a computer the exciting possibility to join the quest to find out where the black holes at the center of these galaxies are located. Astronomers use radio telescopes to make images of the radio sky, much like optical telescopes like the Hubble space telescope make maps of stars and galaxies. The difference is that the images made with a radio telescope show a sky that is very different from the sky that an optical telescope sees. In the radio sky, stars and galaxies are not directly seen but instead an abundance of complex structures linked to massive black holes at the centers of galaxies are detected. Most dust and gas surrounding a supermassive black hole gets consumed by the black hole, but part of the material will escape and gets ejected into deep space. This material forms large plumes of extremely hot gas, it is this gas that forms large structures that is observed by radio telescopes. The Low Frequency Array (LOFAR) telescope, As an example, take the case of the famous radio operated by the Netherlands Institute for Radio source 3C236. The upper image is the radio source, the Astronomy (ASTRON), is continuing its huge middle one an optical image showing many stars and survey of the radio sky and 4 million radio sources galaxies and the lower image an overlay of the radio and the optical image.
    [Show full text]
  • The Emergence of a Lanthanide-Rich Kilonova Following the Merger of Two Neutron Stars
    Draft version September 29, 2017 Typeset using LATEX twocolumn style in AASTeX61 THE EMERGENCE OF A LANTHANIDE-RICH KILONOVA FOLLOWING THE MERGER OF TWO NEUTRON STARS N. R. Tanvir,1 A. J. Levan,2 C. Gonzalez-Fern´ andez,´ 3 O. Korobkin,4 I. Mandel,5 S. Rosswog,6 J. Hjorth,7 P. D'Avanzo,8 A. S. Fruchter,9 C. L. Fryer,4 T. Kangas,9 B. Milvang-Jensen,10 S. Rosetti,1 D. Steeghs,2 R. T. Wollaeger,4 Z. Cano,11 C. M. Copperwheat,12 S. Covino,8 V. D'Elia,13, 14 A. de Ugarte Postigo,11, 10 P. A. Evans,1 W. P. Even,4 S. Fairhurst,15 R. Figuera Jaimes,16 C. J. Fontes,4 Y. I. Fujii,17, 18 J. P. U. Fynbo,19 B. P. Gompertz,2 J. Greiner,20 G. Hodosan,21 M. J. Irwin,3 P. Jakobsson,22 U. G. Jørgensen,23 D. A. Kann,11 J. D. Lyman,2 D. Malesani,19 R. G. McMahon,3 A. Melandri,8 P.T. O'Brien,1 J. P. Osborne,1 E. Palazzi,24 D. A. Perley,12 E. Pian,25 S. Piranomonte,14 M. Rabus,26 E. Rol,27 A. Rowlinson,28, 29 S. Schulze,30 P. Sutton,15 C.C. Thone,¨ 11 K. Ulaczyk,2 D. Watson,19 K. Wiersema,1 and R.A.M.J. Wijers28 1University of Leicester, Department of Physics & Astronomy and Leicester Institute of Space & Earth Observation, University Road, Leicester, LE1 7RH, United Kingdom 2Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom 3Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, United Kingdom 4Computational Methods Group (CCS-2), Los Alamos National Laboratory, P.O.
    [Show full text]
  • Host Galaxies and Radio Morphologies Derived from Visual Inspection
    MNRAS 453, 2326–2340 (2015) doi:10.1093/mnras/stv1688 Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection J. K. Banfield,1,2,3‹ O. I. Wong,4 K. W. Willett,5 R. P. Norris,1 L. Rudnick,5 S. S. Shabala,6 B. D. Simmons,7 C. Snyder,8 A. Garon,5 N. Seymour,9 E. Middelberg,10 H. Andernach,11 C. J. Lintott,7 K. Jacob,5 A. D. Kapinska,´ 3,4 M. Y. Mao,12 K. L. Masters,13,14† M. J. Jarvis,7,15 K. Schawinski,16 E. Paget,8 R. Simpson,7 H.-R. Klockner,¨ 17 S. Bamford,18 T. Burchell,19 K. E. Chow,1 G. Cotter,7 L. Fortson,5 I. Heywood,1,20 T. W. Jones,5 S. Kaviraj,21 A.´ R. Lopez-S´ anchez,´ 22,23 W. P. Maksym,24 K. Polsterer,25 K. Borden,8 R. P. Hollow1 and L. Whyte8 Downloaded from Affiliations are listed at the end of the paper Accepted 2015 July 23. Received 2015 July 22; in original form 2015 February 27 http://mnras.oxfordjournals.org/ ABSTRACT We present results from the first 12 months of operation of Radio Galaxy Zoo, which upon completion will enable visual inspection of over 170 000 radio sources to determine the host galaxy of the radio emission and the radio morphology. Radio Galaxy Zoo uses 1.4 GHz radio images from both the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) in combination with mid-infrared .
    [Show full text]
  • Heliophysics at Total Solar Eclipses Jay M
    REVIEW ARTICLE PUBLISHED: XX AUGUST 2017 | VOLUME: 1 | ARTICLE NUMBER: 0190 Heliophysics at total solar eclipses Jay M. Pasachoff1,2 Observations during total solar eclipses have revealed many secrets about the solar corona, from its discovery in the 17th cen- tury to the measurement of its million-kelvin temperature in the 19th and 20th centuries, to details about its dynamics and its role in the solar-activity cycle in the 21st century. Today’s heliophysicists benefit from continued instrumental and theoretical advances, but a solar eclipse still provides a unique occasion to study coronal science. In fact, the region of the corona best observed from the ground at total solar eclipses is not available for view from any space coronagraphs. In addition, eclipse views boast of much higher quality than those obtained with ground-based coronagraphs. On 21 August 2017, the first total solar eclipse visible solely from what is now United States territory since long before George Washington’s presidency will occur. This event, which will cross coast-to-coast for the first time in 99 years, will provide an opportunity not only for massive expedi- tions with state-of-the-art ground-based equipment, but also for observations from aloft in aeroplanes and balloons. This set of eclipse observations will again complement space observations, this time near the minimum of the solar activity cycle. This review explores the past decade of solar eclipse studies, including advances in our understanding of the corona and its coronal mass ejections as well as terrestrial effects. We also discuss some additional bonus effects of eclipse observations, such as rec- reating the original verification of the general theory of relativity.
    [Show full text]