Big Data in Astronomy The Large Synoptic Survey Telescope

Size: px
Start display at page:

Download "Big Data in Astronomy The Large Synoptic Survey Telescope"

Transcription

1 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

2 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

3 Big Data from 3.2G pixel camera 2000 exposures per night -> 20TB per night 10 year survey

4 Big Camera

5 Big Telescope

6 Big Data from Within its first month of operation LSST will survey more of the Universe than all previous telescopes built by mankind

7

8

9

10

11

12

13 Big Data from 800 images (movie) of the southern hemisphere in 6 colours ~ alerts/ night worldwide, within 60 seconds

14 14

15 15

16

17 LSST Basics 8.4m mirror 9.6 sq deg FOV 20,000 deg of sky 1000 visits per field filters: ugrizy nm r ~24.7 in single visit, ~27.7 stacked depth 3.2 Gpix camera ~0.01 mag precision photometry

18 Big Collaboration a subset in Tucson Arizon

19 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

20 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

21 LSST Data Products

22 LSST'science'and'engineering'tools'' All%soKware%is%version%controlled%and%provenance%informa) on%is%output%with%the%data. % Systems%are%validated%through%project%ini) ated%reviews%with%external%members%.% Slide by Andy Connolly, LSST Simulation Scientist 3

23 C/C++/Python% The'simula4on'framework' CatSim% PhoSim% Slide by Andy Connolly, LSST Simulation Scientist 15

24 0.2 % Op) cal%model %+Tracking %+Diffrac) on %+Det%Perturba) ons% % % % % % % % % +Lens%Perturba) ons %+Mirror%Perturba) ons %+Detector %+Dome%Seeing% % % % % % % % % +Low%Al) tude %+Mid%Al) tude %+High%Al) tude %+Pixeliza) on% %Atmosphere %Atmosphere %Atmosphere% 12 PhoSim Peterson%et%al%2013%

25 3%Gigapixels% % 10%sq.%degrees% % 20%million%sources% % %photons% % 11%Gbytes% % 1000%CPU%hours% % 14

26 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

27 Flux Flux Flux Flux Flux Flux g r Supernova 0 r Classification r SNPhotCC (Kessler et 0 al 2011) i i i z z T obs T obs T obs SN SDSS 2007og z=0.2 SN SDSS z=0.14 SN SDSS 2006kn z= u 0 u 0 u g g g r r r i i Leads in LSST: Alex Kim, Michael Wood-Vasey Leads in LSST:UK: Mark Sullivan (Southampton), Hiranya Peiris (U i

28 Table adapted from Rau et al Slide by Lucianne Walkowicz, Co-Chair of Transients and Variable Sta Expected Rate of Transients Class Mag t (days) Universal Rate LSST Rate Luminous SNe Mpc -3 yr Orphan Afterglows SHB Orphan Afterglows LSB On- axis GRB afterglows Tidal Disruption Flares Luminous Red Novae x Mpc -3 yr -1 ~ x Mpc -3 yr Mpc -3 yr -1 ~ Mpc -3 yr yr -1 Lsun Fallback SNe <5 x 10-6 Mpc -3 yr -1 < 800 SNe Ia x 10-5 Mpc -3 yr SNe II (3..8) x 10-5 Mpc -3 yr

29 TABLE 5 List of Par t icipant s in t he SNPhot CC. Classified SN Part icipant s Abbreviat ion a +Z b /noz c z d ph CPU e Descript ion (st rat egy class f ) P. Belov and S. Glazov Belov & Glazov yes/ no no 90 light curve χ 2 test against Nugent templates (2) S. Gonzalez Gonzalez yes/ yes no 120 cuts on SiFT O fit χ 2 and fit paramet ers (1) J. Richards, Homrighausen, InCA g no/ yes no 1 Spline fit & nonlinear dimensionality C. Schafer, P. Freeman reduct ion (4) J. Newling, M. Varuguese, JEDI-K DE yes/ yes no 10 K ernel Density Evaluat ion with 21 params (4) B. Basset t, R. Hlozek, JEDI Boost yes/ yes no 10 Boost ed decision t rees (4) D. Parkinson, M. Smit h, JEDI-Hubble yes/ no no 10 Hubble diagram K DE (3) H. Campbell, M. Hilt on, JEDI Combo yes/ no no 10 Boost ed decision t rees + Hubble K DE (3+ 4) H. Lampeit l, M. Kunz, P. Pat el (JEDI group h ) S. Philip, V. Bhat nagar, M GU+ DU-1 i no/ yes no < 1 light curve slopes & Neural Network (2) A. Singhal, A. Rai, M GU+ DU-2 no/ yes no < 1 light curve slopes & Random Forest s (2) A. M ahabal, K. Indulekha H. Campbell, B. Nichol, Port smout h χ 2 yes/ no no 1 SA LT 2 χ 2 r & False Discovery Rat e St at ist ic (1) H. L ampiet l, M.Smit h Port smout h-hubble yes/ no no 1 Deviat ion from paramet rized Hubble diagram (3) D. Poznanski Poz2007 RAW yes/ no yes 2 SN A ut omat ed Bayesian Classifier (SN A BC) (2) Poz2007 OPT yes/ no yes 2 SN A BC wit h cut s t o opt imize C FoM Ia (2). S. Rodney Rodney yes/ yes yes 230 SN Ont ology wit h Fuzzy Templat es (2) M. Sako Sako yes/ yes yes 120 χ 2 test against grid of Ia/ I I/ Ibc templates (2) S. K uhlmann, R. K essler SNA NA cuts yes/ yes yes 2 Cut on ml cs fit probability, S/ N & sampling (1) a Groups are list ed alphabet ically by abbreviat ion. b Classificat ions included for SNPhot CC/ HOSTZ. c Classificat ions included for SNPhot CC/ nohostz. d phot o-z est imat es included. e Average processi ng t ime per SN (seconds) usi ng si milar 2-3 GHz cores. f From 3, st rat egy classes are 1) select ion cut s, 2) Bayesian probabilit ies, 3) Hubble-diagram paramet rizat ion and 4) st at istical inference. g Int er nat ional Comput at ional A st rophysics Group: ht t p: / / www. i ncagr oup. org h Joint Exchange and Development Init iat ive: ht t p: / / j edi. saao. ac. za i MGU= Mahat ma Gandhi University, DU= Delhi University. best method in this first SNPhot CC, here we carefully examine the C FoM Ia for the unconfirmed sample in the SNPhot CC/ HOSTZ (Fig. 4). The entry with the highest SNPhotCC (Kessler et al 2011) subset was generally treated as a random subset, which it clearly is not ( 2.5). T he magnit ude-limit ed select ion of spectroscopic targets resulted in the selection of brighter

30 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

31 It s a Big Deal Discovery of Accelerating Universe Wins 2011 Nobel Prize

32 Why is the Universe Accelerating? Einstein s cosmological constant A new fluid called Dark Energy Equation of state w = p/ General Relativity is wrong

33 Using the bending of light to see the invisible

34

35

36 Cosmic Shear Galaxies seen through dark matter distribution analogous to Streetlamps seen through your bathroom window

37 Cosmic Shear g i ~0.2 Real data: g i ~0.03

38 Atmosphere and Telescope Convolution with kernel Real data: Kernel size ~ Galaxy size

39 Pixelisation Sum light in each square Real data: Pixel size ~ Kernel size /2

40 Noise Mostly Poisson. Some Gaussian and bad pixels. Uncertainty on total light ~ 5 per cent

41 Bridle et al 2010

42 A typical galaxy image for cosmic shear Intrinsic galaxy shape b/a ~ 0.5 Uncertainty due to no σb/a ~ 0.5 Modification due to le Δb/a ~ 0.05 Effect of changing w b δb/a ~

43 Annals of Applied Statistics March 2009

44 Slide by David Hogg Following NIPS Cosmology Workshop discussion with Iain Mu

45 Typical Running+Joseph s+code+ DES data multiple exposures Image+ Model+ Weight+ Residuals DESDM data, PSFs; im3shape fit (Zuntz, Hirsch, Kacprzak, Rowe, Ma

46 Successful fits How to deal with overlaps? interloper target model mask removes interloper lovely residuals DESDM data, PSFs; im3shape fit (Zuntz, Hirsch, Kacprzak, Rowe, Ma

47 Slide by David Kirkby Today DES-r 800s 13.7 electrons

48 Slide by David Kirkby In 10 years LSST-r 6900s 13.7 electrons

49

50 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

51 Catalogue search in phase space Find remnants of galaxies colliding with the Milky Way From positions and velocities of 10 billion stars The Sagittarius dwarf galaxy in o Leads in LSST: John Bochanski, Nitya Jacob Kallivayalil, Beth W Leads in LSST:UK: Vasily Belokurov (Cambridge), Nic Walton (Cam

52 Big Data in Astronomy The Large Synoptic Survey Telescope Prof. Sarah Bridle, University of Manchester 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST Image simulations Rapid image processing High precision image processing Catalogue search 3. Ways to get involved

53 LSST Scientific Possibilities LSST Science Book: science/scibook 598 pages 245 authors Preface 8. The Transient and Variable Universe 1. Introduction 9. Galaxies 2. LSST System Design 10. Active Galactic Nuclei 3. System Pergormance 11. Supernovae 4. Education and Public Outreach 12. Strong Lenses 5. The Solar System 13. Large-Scale Structure 6. Stellar Populations 14. Weak Lensing 7. Milky Way and Local Volume 15. Cosmological Physics

54 Science Collaborations Solar System Milky Way and Local Volume Structure Transients & Variable Stars Galaxies Active Galactic Nuclei Supernovae Stellar Populations Strong Lensing Weak Lensing Large Scale Structure & Baryon Oscillations Informatics & Statistics

55 Science Collaborations Solar System Milky Way and Local Volume Structure Transients & Variable Stars Galaxies Active Galactic Nuclei Supernovae Stellar Populations Strong Lensing Weak Lensing Large Scale Structure & Baryon Oscillations Informatics & Statistics LSST:UK I&S Leads: Hiranya Peiris (UCL), Jason McEwen (UCL)

56 Slide by Kirk Bourne, Dept of Computational & Data Sciences Ge University

57 Sign up to get involvedhttps://docs.google.com/spreadsheet/ccc?key=0aqx4pj9ojyrudfvjq U85SS02eEZxeEhTaUJKYmZjVmc&usp=sharing Current status: Submitted 40 page proposal to STFC. PPRP panel presentation on 27 th October 2014

58 Open Problems Related to LSST Shear measurement (GREAT08,, GREAT3) Cosmological parameter estimation (e.g. CosmoSIS) LSST simulations (CatSim, PhoSim, ImSim) Real-time transient classification Supernova Classification Challenge Catalogue search Dark Worlds Kaggle Challenge Strong Lens Time Delay Challenge Communication in large collaborations

59

60 Noise Bias Many identical images with different noise

61 Bias disappears at high S/N Above requirements at low S/N

62 What causes the bias? For model fitting methods Noise bias Refregier, SB et al; Kacprzak, SB et al 2012 Maximum likelihood methods are biased Calibration works well enough Model bias Voigt & Bridle 2009 e.g use wrong profile in fit e.g. use elliptical isophote model in fit

63 Galaxy Models But galaxies aren t simple Model galaxy Actual galaxy

64 Model Bias The effect of realistic galaxy shapes Measure with sims from HST data Bias for red and blue galaxies shown DES 5-year requires mean m < Plots from Tomasz Kacprzak

65 Impact on dark energy constraints Simulate for different redshifts Kacprzak, SB, et al 2013

66

67

68

69 69/19 Taken from Bridle et al GREAT08 Handb

70 Slide by Kirk Bourne, Dept of Computational & Data Sciences Ge University

71 71/19 Typical gala used for cos shear analy Typical star Used for finding Convolution kernel

72 Big Data in Astronomy The Large Synoptic Survey Telescope 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST 3. Weak Lensing in LSST 3.1 Big Data: Galaxy shape measurement 3.2 Big Models: Covariance matrix estimation 3.3 It s a Big Deal: Proving Einstein wrong 4. Ways to get involved

73

74

75 Big Data in Astronomy The Large Synoptic Survey Telescope 1. The Large Synoptic Survey Telescope (LSST) 2. Big Data challenges in LSST 3. Weak Lensing in LSST 3.1 Big Data: Galaxy shape measurement 3.2 Big Models: Covariance matrix estimation 3.3 It s a Big Deal: Proving Einstein wrong 4. Ways to get involved

Learning from Big Data in

Learning from Big Data in Learning from Big Data in Astronomy an overview Kirk Borne George Mason University School of Physics, Astronomy, & Computational Sciences http://spacs.gmu.edu/ From traditional astronomy 2 to Big Data

More information

The Challenge of Data in an Era of Petabyte Surveys Andrew Connolly University of Washington

The Challenge of Data in an Era of Petabyte Surveys Andrew Connolly University of Washington The Challenge of Data in an Era of Petabyte Surveys Andrew Connolly University of Washington We acknowledge support from NSF IIS-0844580 and NASA 08-AISR08-0081 The science of big data sets Big Questions

More information

LSST and the Cloud: Astro Collaboration in 2016 Tim Axelrod LSST Data Management Scientist

LSST and the Cloud: Astro Collaboration in 2016 Tim Axelrod LSST Data Management Scientist LSST and the Cloud: Astro Collaboration in 2016 Tim Axelrod LSST Data Management Scientist DERCAP Sydney, Australia, 2009 Overview of Presentation LSST - a large-scale Southern hemisphere optical survey

More information

Description of the Dark Energy Survey for Astronomers

Description of the Dark Energy Survey for Astronomers Description of the Dark Energy Survey for Astronomers May 1, 2012 Abstract The Dark Energy Survey (DES) will use 525 nights on the CTIO Blanco 4-meter telescope with the new Dark Energy Camera built by

More information

Summary of Data Management Principles Dark Energy Survey V2.1, 7/16/15

Summary of Data Management Principles Dark Energy Survey V2.1, 7/16/15 Summary of Data Management Principles Dark Energy Survey V2.1, 7/16/15 This Summary of Data Management Principles (DMP) has been prepared at the request of the DOE Office of High Energy Physics, in support

More information

Galaxy Survey data analysis using SDSS-III as an example

Galaxy Survey data analysis using SDSS-III as an example Galaxy Survey data analysis using SDSS-III as an example Will Percival (University of Portsmouth) showing work by the BOSS galaxy clustering working group" Cosmology from Spectroscopic Galaxy Surveys"

More information

Lecture 6: distribution of stars in. elliptical galaxies

Lecture 6: distribution of stars in. elliptical galaxies Lecture 6: distribution of stars in topics: elliptical galaxies examples of elliptical galaxies different classes of ellipticals equation for distribution of light actual distributions and more complex

More information

Software challenges in the implementation of large surveys: the case of J-PAS

Software challenges in the implementation of large surveys: the case of J-PAS Software challenges in the implementation of large surveys: the case of J-PAS 1/21 Paulo Penteado - IAG/USP pp.penteado@gmail.com http://www.ppenteado.net/ast/pp_lsst_201204.pdf (K. Taylor) (A. Fernández-Soto)

More information

LSST Data Management. Tim Axelrod Project Scientist - LSST Data Management. Thursday, 28 Oct 2010

LSST Data Management. Tim Axelrod Project Scientist - LSST Data Management. Thursday, 28 Oct 2010 LSST Data Management Tim Axelrod Project Scientist - LSST Data Management Thursday, 28 Oct 2010 Outline of the Presentation LSST telescope and survey Functions and architecture of the LSST data management

More information

A Preliminary Summary of The VLA Sky Survey

A Preliminary Summary of The VLA Sky Survey A Preliminary Summary of The VLA Sky Survey Eric J. Murphy and Stefi Baum (On behalf of the entire Science Survey Group) 1 Executive Summary After months of critical deliberation, the Survey Science Group

More information

LSST Data Management System Applications Layer Simulated Data Needs Description: Simulation Needs for DC3

LSST Data Management System Applications Layer Simulated Data Needs Description: Simulation Needs for DC3 LSST Data Management System Applications Layer Simulated Data Needs Description: Simulation Needs for DC3 Draft 25 September 2008 A joint document from the LSST Data Management Team and Image Simulation

More information

Cosmic Variability Study in Taiwan

Cosmic Variability Study in Taiwan Cosmic Variability Study in Taiwan Wen-Ping Chen Institute of Astronomy National Central University, Taiwan 2010 November 16@Jena/YETI Advantages in Taiwan: - Many high mountains - Western Pacific longitude

More information

Big data challenges for physics in the next decades

Big data challenges for physics in the next decades Big data challenges for physics in the next decades David W. Hogg Center for Cosmology and Particle Physics, New York University 2012 November 09 punchlines Huge data sets create new opportunities. they

More information

Data Pipelines & Archives for Large Surveys. Peter Nugent (LBNL)

Data Pipelines & Archives for Large Surveys. Peter Nugent (LBNL) Data Pipelines & Archives for Large Surveys Peter Nugent (LBNL) Overview Major Issues facing any large-area survey/search: Computational power for search - data transfer, processing, storage, databases

More information

LSST Resources for the Community Lynne Jones University of Washington/LSST

LSST Resources for the Community Lynne Jones University of Washington/LSST LSST Resources for the Community Lynne Jones University of Washington/LSST 1 Data Flow Nightly Operations : (at base facility) Each 15s exposure = 6.44 GB (raw) 2x15s = 1 visit 30 TB / night Generates

More information

ESSENCE: Determining the Nature of Dark Energy with High-z Supernovae

ESSENCE: Determining the Nature of Dark Energy with High-z Supernovae ESSENCE: Determining the Nature of Dark Energy with High-z Supernovae Ryan Foley UC Berkeley ESSENCE Team Equation of State: SupErNovae trace Cosmic Expansion C. Aguilera (NOAO/CTIO) B. Barris (IfA/UH)

More information

Astronomy & Physics Resources for Middle & High School Teachers

Astronomy & Physics Resources for Middle & High School Teachers Astronomy & Physics Resources for Middle & High School Teachers Gillian Wilson http://www.faculty.ucr.edu/~gillianw/k12 A cosmologist is.... an astronomer who studies the formation and evolution of the

More information

Detecting and measuring faint point sources with a CCD

Detecting and measuring faint point sources with a CCD Detecting and measuring faint point sources with a CCD Herbert Raab a,b a Astronomical ociety of Linz, ternwarteweg 5, A-400 Linz, Austria b Herbert Raab, chönbergstr. 3/1, A-400 Linz, Austria; herbert.raab@utanet.at

More information

The Milky Way Galaxy is Heading for a Major Cosmic Collision

The Milky Way Galaxy is Heading for a Major Cosmic Collision The Milky Way Galaxy is Heading for a Major Cosmic Collision Roeland van der Marel (STScI) [based on work with a team of collaborators reported in the Astrophysical Journal July 2012] Hubble Science Briefing

More information

CASU Processing: Overview and Updates for the VVV Survey

CASU Processing: Overview and Updates for the VVV Survey CASU Processing: Overview and Updates for the VVV Survey Nicholas Walton Eduardo Gonalez-Solares, Simon Hodgkin, Mike Irwin (Institute of Astronomy) Pipeline Processing Summary Data organization (check

More information

The Large Synoptic Survey Telescope: Status Update

The Large Synoptic Survey Telescope: Status Update The Large Synoptic Survey Telescope: Status Update Steven M. Kahn LSST Director Mid-Decadal Review Committee December 13, 2015 LSST in a Nutshell The LSST is an integrated survey system designed to conduct

More information

Data analysis of L2-L3 products

Data analysis of L2-L3 products Data analysis of L2-L3 products Emmanuel Gangler UBP Clermont-Ferrand (France) Emmanuel Gangler BIDS 14 1/13 Data management is a pillar of the project : L3 Telescope Caméra Data Management Outreach L1

More information

Einstein Rings: Nature s Gravitational Lenses

Einstein Rings: Nature s Gravitational Lenses National Aeronautics and Space Administration Einstein Rings: Nature s Gravitational Lenses Leonidas Moustakas and Adam Bolton Taken from: Hubble 2006 Science Year in Review The full contents of this book

More information

The Past, Present, and Future of Data Science Education

The Past, Present, and Future of Data Science Education The Past, Present, and Future of Data Science Education Kirk Borne @KirkDBorne http://kirkborne.net George Mason University School of Physics, Astronomy, & Computational Sciences Outline Research and Application

More information

Hubble Diagram S George Djorgovski. Encyclopedia of Astronomy & Astrophysics P. Murdin

Hubble Diagram S George Djorgovski. Encyclopedia of Astronomy & Astrophysics P. Murdin eaa.iop.org DOI: 10.1888/0333750888/2132 Hubble Diagram S George Djorgovski From Encyclopedia of Astronomy & Astrophysics P. Murdin IOP Publishing Ltd 2006 ISBN: 0333750888 Institute of Physics Publishing

More information

Elliptical Galaxies. Old view: ellipticals are boring, simple systems

Elliptical Galaxies. Old view: ellipticals are boring, simple systems Eliptical Galaxies Elliptical Galaxies Old view: ellipticals are boring, simple systems Ellipticals contain no gas & dust Ellipticals are composed of old stars Ellipticals formed in a monolithic collapse,

More information

Top 10 Discoveries by ESO Telescopes

Top 10 Discoveries by ESO Telescopes Top 10 Discoveries by ESO Telescopes European Southern Observatory reaching new heights in astronomy Exploring the Universe from the Atacama Desert, in Chile since 1964 ESO is the most productive astronomical

More information

Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. and Alex Gray

Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. and Alex Gray Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas University of Washington and Alex

More information

Using Photometric Data to Derive an HR Diagram for a Star Cluster

Using Photometric Data to Derive an HR Diagram for a Star Cluster Using Photometric Data to Derive an HR Diagram for a Star Cluster In In this Activity, we will investigate: 1. How to use photometric data for an open cluster to derive an H-R Diagram for the stars and

More information

Data Provided: A formula sheet and table of physical constants is attached to this paper. DARK MATTER AND THE UNIVERSE

Data Provided: A formula sheet and table of physical constants is attached to this paper. DARK MATTER AND THE UNIVERSE Data Provided: A formula sheet and table of physical constants is attached to this paper. DEPARTMENT OF PHYSICS AND ASTRONOMY Autumn Semester (2014-2015) DARK MATTER AND THE UNIVERSE 2 HOURS Answer question

More information

The Gaia Archive. Center Forum, Heidelberg, June 10-11, 2013. Stefan Jordan. The Gaia Archive, COSADIE Astronomical Data

The Gaia Archive. Center Forum, Heidelberg, June 10-11, 2013. Stefan Jordan. The Gaia Archive, COSADIE Astronomical Data The Gaia Archive Astronomisches Rechen-Institut am Zentrum für Astronomie der Universität Heidelberg http://www.stefan-jordan.de 1 2 Gaia 2013-2018 and beyond Progress with Gaia 3 HIPPARCOS Gaia accuracy

More information

Analytics-as-a-Service: From Science to Marketing

Analytics-as-a-Service: From Science to Marketing Analytics-as-a-Service: From Science to Marketing Data Information Knowledge Insights (Discovery & Decisions) Kirk Borne George Mason University, Fairfax, VA www.kirkborne.net @KirkDBorne Big Data: What

More information

Probability of detecting compact binary coalescence with enhanced LIGO

Probability of detecting compact binary coalescence with enhanced LIGO Probability of detecting compact binary coalescence with enhanced LIGO Richard O Shaughnessy [V. Kalogera, K. Belczynski] GWDAW-12, December 13, 2007 Will we see a merger soon? Available predictions Isolated

More information

Data Literacy For All: Astrophysics and Beyond (Astronomy is evidence-based forensic science, thus it is a data & information science)

Data Literacy For All: Astrophysics and Beyond (Astronomy is evidence-based forensic science, thus it is a data & information science) Data Literacy For All: Astrophysics and Beyond (Astronomy is evidence-based forensic science, thus it is a data & information science) Kirk Borne George Mason University, Fairfax, VA www.kirkborne.net

More information

The Tonnabytes Big Data Challenge: Transforming Science and Education. Kirk Borne George Mason University

The Tonnabytes Big Data Challenge: Transforming Science and Education. Kirk Borne George Mason University The Tonnabytes Big Data Challenge: Transforming Science and Education Kirk Borne George Mason University Ever since we first began to explore our world humans have asked questions and have collected evidence

More information

The Sloan Digital Sky Survey. From Big Data to Big Database to Big Compute. Heidi Newberg Rensselaer Polytechnic Institute

The Sloan Digital Sky Survey. From Big Data to Big Database to Big Compute. Heidi Newberg Rensselaer Polytechnic Institute The Sloan Digital Sky Survey From Big Data to Big Database to Big Compute Heidi Newberg Rensselaer Polytechnic Institute Summary History of the data deluge from a personal perspective. The transformation

More information

Chapter 15 Cosmology: Will the universe end?

Chapter 15 Cosmology: Will the universe end? Cosmology: Will the universe end? 1. Who first showed that the Milky Way is not the only galaxy in the universe? a. Kepler b. Copernicus c. Newton d. Hubble e. Galileo Ans: d 2. The big bang theory and

More information

Efficient data reduction and analysis of DECam images using multicore architecture Poor man s approach to Big data

Efficient data reduction and analysis of DECam images using multicore architecture Poor man s approach to Big data Efficient data reduction and analysis of DECam images using multicore architecture Poor man s approach to Big data Instituto de Astrofísica Pontificia Universidad Católica de Chile Thomas Puzia, Maren

More information

The LSST Data management and French computing activities. Dominique Fouchez on behalf of the IN2P3 Computing Team. LSST France April 8th,2015

The LSST Data management and French computing activities. Dominique Fouchez on behalf of the IN2P3 Computing Team. LSST France April 8th,2015 The LSST Data management and French computing activities Dominique Fouchez on behalf of the IN2P3 Computing Team LSST France April 8th,2015 OSG All Hands SLAC April 7-9, 2014 1 The LSST Data management

More information

The Search for Dark Matter, Einstein s Cosmology and MOND. David B. Cline

The Search for Dark Matter, Einstein s Cosmology and MOND. David B. Cline The Search for Dark Matter, Einstein s Cosmology and MOND David B. Cline Astrophysics Division, Department of Physics & Astronomy University of California, Los Angeles, CA 90095 USA dcline@physics.ucla.edu

More information

LSST All Hands Meeting SLAC, December 4-8 2006 (MAP)

LSST All Hands Meeting SLAC, December 4-8 2006 (MAP) LSST All Hands Meeting SLAC, December 4-8 2006 (MAP) Monday, December 4 th Plenary Session Day One, Kavli Auditorium Project Status 1:00 Welcome; Project and MREFC Status D. Sweeney 1:40 Directors Report

More information

Making astronomical discoveries on the web

Making astronomical discoveries on the web Making astronomical discoveries on the web David W. Hogg Center for Cosmology and Particle Physics, New York University Max-Planck-Institut für Astronomie, Heidelberg 2011 July 12 Conclusions It is possible

More information

Modeling Galaxy Formation

Modeling Galaxy Formation Galaxy Evolution is the study of how galaxies form and how they change over time. As was the case with we can not observe an individual galaxy evolve but we can observe different galaxies at various stages

More information

High Energy Physics (HEP) Program Status Update to the AAAC Meeting. June 1, 2015

High Energy Physics (HEP) Program Status Update to the AAAC Meeting. June 1, 2015 OFFICE OF SCIENCE High Energy Physics (HEP) Program Status Update to the AAAC Meeting June 1, 2015 Kathy Turner Program Manager, Cosmic Frontier Office of High Energy Physics Office of Science, U.S. Department

More information

First Discoveries. Asteroids

First Discoveries. Asteroids First Discoveries The Sloan Digital Sky Survey began operating on June 8, 1998. Since that time, SDSS scientists have been hard at work analyzing data and drawing conclusions. This page describes seven

More information

Reduced data products in the ESO Phase 3 archive (Status: 15 May 2015)

Reduced data products in the ESO Phase 3 archive (Status: 15 May 2015) Reduced data products in the ESO Phase 3 archive (Status: 15 May 2015) The ESO Phase 3 archive provides access to reduced and calibrated data products. All those data are stored in standard formats. The

More information

Cosmological Analysis of South Pole Telescope-detected Galaxy Clusters

Cosmological Analysis of South Pole Telescope-detected Galaxy Clusters Cosmological Analysis of South Pole Telescope-detected Galaxy Clusters March 24th Tijmen de Haan (McGill) - Moriond 2014 Photo credit: Keith Vanderlinde Outline The galaxy cluster sample Calibrating the

More information

Lecture 19 Big Bang Cosmology

Lecture 19 Big Bang Cosmology The Nature of the Physical World Lecture 19 Big Bang Cosmology Arán García-Bellido 1 News Exam 2: you can do better! Presentations April 14: Great Physicist life, Controlled fusion April 19: Nuclear power,

More information

The Messier Objects As A Tool in Teaching Astronomy

The Messier Objects As A Tool in Teaching Astronomy The Messier Objects As A Tool in Teaching Astronomy Dr. Jesus Rodrigo F. Torres President, Rizal Technological University Individual Member, International Astronomical Union Chairman, Department of Astronomy,

More information

Data Mining Challenges and Opportunities in Astronomy

Data Mining Challenges and Opportunities in Astronomy Data Mining Challenges and Opportunities in Astronomy S. G. Djorgovski (Caltech) With special thanks to R. Brunner, A. Szalay, A. Mahabal, et al. The Punchline: Astronomy has become an immensely datarich

More information

Data, Computation, and the Fate of the Universe

Data, Computation, and the Fate of the Universe Data, Computation, and the Fate of the Universe Saul Perlmutter University of California, Berkeley Lawrence Berkeley National Laboratory NERSC Lunchtime Nobel Keynote Lecture June 2014 Atomic Matter 4%

More information

Organization and Staffing

Organization and Staffing Large Synoptic Survey Telescope (LSST) Organization and Staffing Robert McKercher LPM-103 Latest Revision Date: September 3, 2013 Change Record Version Date Description Owner name 1 9/3/2013 Initial Version

More information

Class 2 Solar System Characteristics Formation Exosolar Planets

Class 2 Solar System Characteristics Formation Exosolar Planets Class 1 Introduction, Background History of Modern Astronomy The Night Sky, Eclipses and the Seasons Kepler's Laws Newtonian Gravity General Relativity Matter and Light Telescopes Class 2 Solar System

More information

Taming the Internet of Things: The Lord of the Things

Taming the Internet of Things: The Lord of the Things Taming the Internet of Things: The Lord of the Things Kirk Borne @KirkDBorne School of Physics, Astronomy, & Computational Sciences College of Science, George Mason University, Fairfax, VA Taming the Internet

More information

Visualization of Large Multi-Dimensional Datasets

Visualization of Large Multi-Dimensional Datasets ***TITLE*** ASP Conference Series, Vol. ***VOLUME***, ***PUBLICATION YEAR*** ***EDITORS*** Visualization of Large Multi-Dimensional Datasets Joel Welling Department of Statistics, Carnegie Mellon University,

More information

Big Bang Cosmology. Big Bang vs. Steady State

Big Bang Cosmology. Big Bang vs. Steady State Big Bang vs. Steady State Big Bang Cosmology Perfect cosmological principle: universe is unchanging in space and time => Steady-State universe - Bondi, Hoyle, Gold. True? No! Hubble s Law => expansion

More information

Exploring the Universe Through the Hubble Space Telescope

Exploring the Universe Through the Hubble Space Telescope Exploring the Universe Through the Hubble Space Telescope WEEK FIVE: THE HUBBLE DEEP FIELD + LIMITATIONS OF HUBBLE, COLLABORATIONS, AND THE FUTURE OF ASTRONOMY Date: October 14, 2013 Instructor: Robert

More information

Indiana University Science with the WIYN One Degree Imager

Indiana University Science with the WIYN One Degree Imager Indiana University Science with the WIYN One Degree Imager Katherine Rhode (Indiana University, WIYN SAC member) Indiana University Department of Astronomy Nine faculty members, plus active emeritus faculty

More information

Lecture 17: Dark Energy & The Big Bang

Lecture 17: Dark Energy & The Big Bang Lecture 17: Dark Energy & The Big Bang As with all course material (including homework, exams), these lecture notes are not be reproduced, redistributed, or sold in any form. Solution? ~1998 astronomers

More information

Detailed Mass Map of CL 0024+1654 from Strong Lensing

Detailed Mass Map of CL 0024+1654 from Strong Lensing Detailed Mass Map of CL 0024+1654 from Strong Lensing Tyson, Kochanski, & Dell Antonio (1998) HST WFPC2 image of CL0024+1654 slides based on presentation by Yue Zhao Rutgers Physics 690 February 21, 2008

More information

Exploring Multiwavelength AGN Variability with Swift Archival Data

Exploring Multiwavelength AGN Variability with Swift Archival Data Exploring Multiwavelength AGN Variability with Swift Archival Data, a Caryl Gronwall, b Dirk Grupe, c Dan Vanden Berk d and Jian Wu e a Spectral Sciences Inc., 4 Fourth Ave., Burlington MA 01803, USA b

More information

Chapter 15.3 Galaxy Evolution

Chapter 15.3 Galaxy Evolution Chapter 15.3 Galaxy Evolution Elliptical Galaxies Spiral Galaxies Irregular Galaxies Are there any connections between the three types of galaxies? How do galaxies form? How do galaxies evolve? P.S. You

More information

PISGAH ASTRONOMICAL RESEARCH INSTITUTE AND ASTRONOMICAL PHOTOGRAPHIC DATA ARCHIVE. www.pari.edu

PISGAH ASTRONOMICAL RESEARCH INSTITUTE AND ASTRONOMICAL PHOTOGRAPHIC DATA ARCHIVE. www.pari.edu PISGAH ASTRONOMICAL RESEARCH INSTITUTE AND ASTRONOMICAL PHOTOGRAPHIC DATA ARCHIVE www.pari.edu PARI 200ac (80ha) campus 6.2mi (10km) of roads 12 buildings 100,000 ft2 (9290 m2) Extensive Internet Infrastructure

More information

Big Data @ STScI. Enhancing STScI s Astronomical Data Science Capabilities over the Next Five Years

Big Data @ STScI. Enhancing STScI s Astronomical Data Science Capabilities over the Next Five Years Big Data @ STScI Enhancing STScI s Astronomical Data Science Capabilities over the Next Five Years Science Definition Team Report March 15, 2016 1 Table of Contents 1 Executive Summary... 3 2 Charter of

More information

165 points. Name Date Period. Column B a. Cepheid variables b. luminosity c. RR Lyrae variables d. Sagittarius e. variable stars

165 points. Name Date Period. Column B a. Cepheid variables b. luminosity c. RR Lyrae variables d. Sagittarius e. variable stars Name Date Period 30 GALAXIES AND THE UNIVERSE SECTION 30.1 The Milky Way Galaxy In your textbook, read about discovering the Milky Way. (20 points) For each item in Column A, write the letter of the matching

More information

astronomy 2008 1. A planet was viewed from Earth for several hours. The diagrams below represent the appearance of the planet at four different times.

astronomy 2008 1. A planet was viewed from Earth for several hours. The diagrams below represent the appearance of the planet at four different times. 1. A planet was viewed from Earth for several hours. The diagrams below represent the appearance of the planet at four different times. 5. If the distance between the Earth and the Sun were increased,

More information

Introduction to CCDs and CCD Data Calibration

Introduction to CCDs and CCD Data Calibration Introduction to CCDs and CCD Data Calibration Dr. William Welsh San Diego State University CCD: charge coupled devices integrated circuit silicon chips that can record optical (and X-ray) light pixel =

More information

Science Standard 4 Earth in Space Grade Level Expectations

Science Standard 4 Earth in Space Grade Level Expectations Science Standard 4 Earth in Space Grade Level Expectations Science Standard 4 Earth in Space Our Solar System is a collection of gravitationally interacting bodies that include Earth and the Moon. Universal

More information

How the properties of galaxies are affected by the environment?

How the properties of galaxies are affected by the environment? How the properties of galaxies are affected by the environment? Reinaldo R. de Carvalho - DAS/INPE Marina Trevisan Reinaldo Rosa The activities in this project follow from the Tatiana Moura general context

More information

Cosmological Scale Tests of Gravity

Cosmological Scale Tests of Gravity Cosmological Scale Tests of Gravity Edmund Bertschinger MIT Department of Physics and Kavli Institute for Astrophysics and Space Research January 2011 References Caldwell & Kamionkowski 0903.0866 Silvestri

More information

1. Introduction to image processing

1. Introduction to image processing 1 1. Introduction to image processing 1.1 What is an image? An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. Figure 1: An image an array or a matrix

More information

Interstellar Cosmic-Ray Spectrum from Gamma Rays and Synchrotron

Interstellar Cosmic-Ray Spectrum from Gamma Rays and Synchrotron Interstellar Cosmic-Ray Spectrum from Gamma Rays and Synchrotron Chuck Naval Research Laboratory, Washington, DC charles.dermer@nrl.navy.mil Andy Strong Max-Planck-Institut für extraterrestrische Physik,

More information

FIRST LIGHT IN THE UNIVERSE

FIRST LIGHT IN THE UNIVERSE FIRST LIGHT IN THE UNIVERSE Richard Ellis, Caltech 1. Role of Observations in Cosmology & Galaxy Formation 2. Galaxies & the Hubble Sequence 3. Cosmic Star Formation Histories 4. Stellar Mass Assembly

More information

Pi of the Sky off-line experiment with GLORIA. Ariel Majcher National Centre for Nuclear Research Warsaw, Poland

Pi of the Sky off-line experiment with GLORIA. Ariel Majcher National Centre for Nuclear Research Warsaw, Poland Pi of the Sky off-line experiment with GLORIA Ariel Majcher National Centre for Nuclear Research Warsaw, Poland 10th INTEGRAL/BART Workshop, 22 25 April 2013 Outline GLORIA project Demonstrator experiments

More information

Probes of Star Formation in the Early Universe

Probes of Star Formation in the Early Universe Gamma Ray Bursts Probes of Star Formation in the Early Universe Edward P.J.van den Heuvel Universiteit van Amsterdam &KITP-UCSB KITP, March 17, 2007 Age of the Universe: 13.7 billion years Age of our Milky

More information

The Solar Journey: Modeling Features of the Local Bubble and Galactic Environment of the Sun

The Solar Journey: Modeling Features of the Local Bubble and Galactic Environment of the Sun The Solar Journey: Modeling Features of the Local Bubble and Galactic Environment of the Sun P.C. Frisch and A.J. Hanson Department of Astronomy and Astrophysics University of Chicago and Computer Science

More information

Dark Energy, Modified Gravity and The Accelerating Universe

Dark Energy, Modified Gravity and The Accelerating Universe Dark Energy, Modified Gravity and The Accelerating Universe Dragan Huterer Kavli Institute for Cosmological Physics University of Chicago Makeup of universe today Dark Matter (suspected since 1930s established

More information

An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY. Harrison H. Barrett University of Arizona Tucson, AZ

An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY. Harrison H. Barrett University of Arizona Tucson, AZ An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY Harrison H. Barrett University of Arizona Tucson, AZ Outline! Approaches to image quality! Why not fidelity?! Basic premises of the task-based approach!

More information

J-PAS: low-resolution (R 50) spectroscopy over 8000 deg 2

J-PAS: low-resolution (R 50) spectroscopy over 8000 deg 2 J-PAS: low-resolution (R 50) spectroscopy over 8000 deg 2 C. López-Sanjuan J. Cenarro, L. A. Díaz-García, J. Varela, K. Viironen, & the J-PAS team Centro de Estudio de Física del Cosmos de Aragón 10th

More information

FRONT-LINE RECURRENT NOVA SCIENCE REQUIRES CENTURY OLD DATA

FRONT-LINE RECURRENT NOVA SCIENCE REQUIRES CENTURY OLD DATA FRONT-LINE RECURRENT NOVA SCIENCE REQUIRES CENTURY OLD DATA Bradley E. Schaefer (Louisiana State University) What stars create Type Ia Supernova? Now a big-money question. Recurrent novae (RN) are a likely

More information

The Universe. The Solar system, Stars and Galaxies

The Universe. The Solar system, Stars and Galaxies The Universe The Universe is everything. All us, the room, the U.S. the earth, the solar system, all the other stars in the Milky way galaxy, all the other galaxies... everything. How big and how old is

More information

SKINAKAS OBSERVATORY. Astronomy Projects for University Students PROJECT THE HERTZSPRUNG RUSSELL DIAGRAM

SKINAKAS OBSERVATORY. Astronomy Projects for University Students PROJECT THE HERTZSPRUNG RUSSELL DIAGRAM PROJECT 4 THE HERTZSPRUNG RUSSELL DIGRM Objective: The aim is to measure accurately the B and V magnitudes of several stars in the cluster, and plot them on a Colour Magnitude Diagram. The students will

More information

Science Drivers for Big Data Joseph Lazio SKA Program Development Office & Jet Propulsion Laboratory, California Institute of Technology

Science Drivers for Big Data Joseph Lazio SKA Program Development Office & Jet Propulsion Laboratory, California Institute of Technology Science Drivers for Big Data Joseph Lazio SKA Program Development Office & Jet Propulsion Laboratory, California Institute of Technology 2010 California Institute of Technology. Government sponsorship

More information

Truncation of galaxy dark matter halos in high density environments arxiv:astro-ph/0609782v2 16 Oct 2006

Truncation of galaxy dark matter halos in high density environments arxiv:astro-ph/0609782v2 16 Oct 2006 Astronomy & Astrophysics manuscript no. 5543 c ESO 2014 January 20, 2014 Truncation of galaxy dark matter halos in high density environments arxiv:astro-ph/0609782v2 16 Oct 2006 M. Limousin 1,4, J.P. Kneib

More information

FXA 2008. UNIT G485 Module 5 5.5.1 Structure of the Universe. Δλ = v λ c CONTENTS OF THE UNIVERSE. Candidates should be able to :

FXA 2008. UNIT G485 Module 5 5.5.1 Structure of the Universe. Δλ = v λ c CONTENTS OF THE UNIVERSE. Candidates should be able to : 1 Candidates should be able to : CONTENTS OF THE UNIVERSE Describe the principal contents of the universe, including stars, galaxies and radiation. Describe the solar system in terms of the Sun, planets,

More information

College of Science George Mason University Fairfax, VA 22030

College of Science George Mason University Fairfax, VA 22030 College of Science George Mason University Fairfax, VA 22030 Dr. Sidney Wolff and the LSST Board of Directors LSST Corporation 933 N. Cherry Avenue Tucson, AZ 85721-0009 June 14, 2010 Dear Dr. Wolff and

More information

Understanding the Accelerating Universe using the MSE. Gong-Bo Zhao NAOC

Understanding the Accelerating Universe using the MSE. Gong-Bo Zhao NAOC Understanding the Accelerating Universe using the MSE Gong-Bo Zhao NAOC Nobel Prize 2011 a > 0 The expansion of the Universe can accelerate if In GR, to add new repulsive matter, which contributes 70%

More information

outline Einstein Vs Newton Deflection of light May, 29th 1919 the challenging Universe Sir Isaac Albert

outline Einstein Vs Newton Deflection of light May, 29th 1919 the challenging Universe Sir Isaac Albert the challenging Universe outline Einstein vs Newton and the solar eclipse of 1919 The missing mass problem Seeing the invisible: gravitational lensing Dark matter hunting Dr Roberto Trotta The challenging

More information

Please note that only the German version of the Curriculum is legally binding. All other linguistic versions are provided for information only

Please note that only the German version of the Curriculum is legally binding. All other linguistic versions are provided for information only Please note that only the German version of the Curriculum is legally binding. All other linguistic versions are provided for information only Curriculum for the Erasmus Mundus Joint Master Program in

More information

The Sino-French Gamma-Ray Burst Mission SVOM (Space-based multi-band astronomical Variable Objects Monitor)

The Sino-French Gamma-Ray Burst Mission SVOM (Space-based multi-band astronomical Variable Objects Monitor) The Sino-French Gamma-Ray Burst Mission SVOM (Space-based multi-band astronomical Variable Objects Monitor) Didier BARRET on behalf of the SVOM collaboration didier.barret@cesr.fr Outline SVOM background

More information

Photo-z Requirements for Self-Calibration of Cluster Dark Energy Studies

Photo-z Requirements for Self-Calibration of Cluster Dark Energy Studies Photo-z Requirements for Self-Calibration of Cluster Dark Energy Studies Marcos Lima Department of Physics Kavli Institute for Cosmological Physics University of Chicago DES Collaboration Meeting Barcelona

More information

http://dx.doi.org/10.1117/12.906346

http://dx.doi.org/10.1117/12.906346 Stephanie Fullerton ; Keith Bennett ; Eiji Toda and Teruo Takahashi "Camera simulation engine enables efficient system optimization for super-resolution imaging", Proc. SPIE 8228, Single Molecule Spectroscopy

More information

arxiv:astro-ph/0401123v1 8 Jan 2004

arxiv:astro-ph/0401123v1 8 Jan 2004 1 The Optical Gravitational Lensing Experiment. Real Time Data Analysis Systems in the OGLE-III Survey. 1 A. U d a l s k i arxiv:astro-ph/0401123v1 8 Jan 2004 Warsaw University Observatory, Al. Ujazdowskie

More information

CCD Calibration I: System Corrections

CCD Calibration I: System Corrections CCD Calibration I: System Corrections Best Observing Season: any Level: Intermediate Learning Goals: The student will determine a CCD camera s system corrections Terminology: blooming, CCD, charge transfer

More information

Pretest Ch 20: Origins of the Universe

Pretest Ch 20: Origins of the Universe Name: _Answer key Pretest: _2_/ 58 Posttest: _58_/ 58 Pretest Ch 20: Origins of the Universe Vocab/Matching: Match the definition on the left with the term on the right by placing the letter of the term

More information

SYLLABUS FORM WESTCHESTER COMMUNITY COLLEGE Valhalla, NY l0595. l. Course #:PHYSC 151 2. NAME OF ORIGINATOR /REVISOR: PAUL ROBINSON

SYLLABUS FORM WESTCHESTER COMMUNITY COLLEGE Valhalla, NY l0595. l. Course #:PHYSC 151 2. NAME OF ORIGINATOR /REVISOR: PAUL ROBINSON SYLLABUS FORM WESTCHESTER COMMUNITY COLLEGE Valhalla, NY l0595 l. Course #:PHYSC 151 2. NAME OF ORIGINATOR /REVISOR: PAUL ROBINSON NAME OF COURSE: ASTRONOMY 3. CURRENT DATE: OCTOBER 26, 2011. Please indicate

More information

Why is the Night Sky Dark?

Why is the Night Sky Dark? Why is the Night Sky Dark? Cosmology Studies of the universe as a whole Today Brief history of ideas (Early Greeks Big Bang) The expanding universe (Hubble, Relativity, density & destiny) An alternative

More information

Undergraduate Studies Department of Astronomy

Undergraduate Studies Department of Astronomy WIYN 3.5-meter Telescope at Kitt Peak near Tucson, AZ Undergraduate Studies Department of Astronomy January 2014 Astronomy at Indiana University General Information The Astronomy Department at Indiana

More information

WIYN ODI: Observing Process, Data Analysis and Archiving

WIYN ODI: Observing Process, Data Analysis and Archiving WIYN ODI: Observing Process, Data Analysis and Archiving Pierre Martin Yale Survey Workshop, October 2009 ODI: Scientific Challenges ODI is designed to take advantage of the best seeing conditions at WIYN.

More information

ASKAP Science Data Archive: Users and Requirements CSIRO ASTRONOMY AND SPACE SCIENCE (CASS)

ASKAP Science Data Archive: Users and Requirements CSIRO ASTRONOMY AND SPACE SCIENCE (CASS) ASKAP Science Data Archive: Users and Requirements CSIRO ASTRONOMY AND SPACE SCIENCE (CASS) Jessica Chapman, Data Workshop March 2013 ASKAP Science Data Archive Talk outline Data flow in brief Some radio

More information