Astro- -I nformatics: I nformatics: Astro Computation in the study Computation in the study of the Universe of the Universe Bob Mann and Andy Lawrence Bob Mann and Andy Lawrence Institute for Astronomy, School of Physics Institute for Astronomy, School of Physics (rgm@roe.ac.uk rgm@roe.ac.uk & & al@roe.ac.uk al@roe.ac.uk) ) (
Plan Plan � Computational Astrophysics Computational Astrophysics � – N N- -body simulations of galaxy clustering body simulations of galaxy clustering – � Astro Astro- -Informatics Informatics � – Survey astronomy & the Virtual Observatory Survey astronomy & the Virtual Observatory – � Discussion Discussion � – Astronomy and informatics Astronomy and informatics –
Plan Plan � Computational Astrophysics Computational Astrophysics � – N N- -body simulations of galaxy clustering body simulations of galaxy clustering – � Astro Astro- -Informatics Informatics � – Survey astronomy & the Virtual Observatory Survey astronomy & the Virtual Observatory – � Discussion Discussion � – Astronomy and informatics Astronomy and informatics –
Observing galaxy clustering Observing galaxy clustering � 1930s: Hubble 1930s: Hubble � – Galaxies aren Galaxies aren’ ’t uniformly distributed on sky t uniformly distributed on sky – � 1950s: Shane and 1950s: Shane and Wirtanen Wirtanen � – Map of galaxy distribution on the sky from Map of galaxy distribution on the sky from – counting 100,000 galaxies by eye by eye (10 years!) (10 years!) counting 100,000 galaxies � 1980s: 1980s: CfA CfA Redshift Redshift Survey Survey � – ( (Huchra Huchra, Geller, de , Geller, de Lapparent Lapparent) ) – – First sizeable 3D map of the local Universe First sizeable 3D map of the local Universe – � Measured rough distances to ~ 11,000 galaxies Measured rough distances to ~ 11,000 galaxies �
1985: first CfA CfA survey survey 1985: first 3D map of a pyramidal slice of space, projected into 2D ~ 500 million light years � Rich structure Rich structure – – walls, filaments, voids walls, filaments, voids… … � – How to explain this richness of structure? How to explain this richness of structure? –
Modelling galaxy clustering Modelling galaxy clustering � Physics simple in Cold Dark Matter model Physics simple in Cold Dark Matter model � – Collisionless Collisionless material moving under gravity material moving under gravity – � Apply perturbation theory to density field Apply perturbation theory to density field � – Linear theory treatment simple, but Linear theory treatment simple, but… … – – Perturbations non Perturbations non- -linear on scales of interest linear on scales of interest – � Fourier modes couple, analytic methods fail Fourier modes couple, analytic methods fail � � Need numerical simulations to model Need numerical simulations to model � galaxy clustering into non- -linear regime linear regime galaxy clustering into non – Set up test masses and evolve under gravity: Set up test masses and evolve under gravity: – i.e. gravitational N N - -body simulations body simulations i.e. gravitational
Two decades of N- -body body Two decades of N simulations simulations � 1985: Davis, 1985: Davis, Efstathiou Efstathiou, , Frenk Frenk, White , White � 3 particles – (32) (32) 3 particles – – < 10 particles per galaxy < 10 particles per galaxy – – Early success for Cold Dark Matter model Early success for Cold Dark Matter model – � 2005: Virgo Consortium 2005: Virgo Consortium � – Inc. John Peacock ( Inc. John Peacock (IfA IfA), plus EPCC ), plus EPCC – 3 particles (2202) 3 – (2202) particles – – ~ 1000 particles per galaxy ~ 1000 particles per galaxy – 2 Mass resolution increased by a factor of ~ 10 2 � Mass resolution increased by a factor of ~ 10 � 3 and simulation volume by a factor of ~ 10 3 and simulation volume by a factor of ~ 10
Theory v v Observation Observation Theory � Theory: VIRGO Theory: VIRGO � Observation: 2dFGRS Observation: 2dFGRS � � (inc. John Peacock) (inc. John Peacock) ~ 250,000 galaxies ~ 250,000 galaxies � (SDSS: ~ 500,000 galaxies) (SDSS: ~ 500,000 galaxies) � Quantitative clustering analysis reveals theory and observation in excellent agreement
Galaxy clustering summary Galaxy clustering summary � Cold Dark Matter model accounts for Cold Dark Matter model accounts for � the observed clustering of galaxies the observed clustering of galaxies – Major triumph of modern astronomy Major triumph of modern astronomy – � Numerical simulations crucial, but this is Numerical simulations crucial, but this is � astronomers using computers, not astronomers using computers, not astronomers using computer science astronomers using computer science – Are there examples of real interaction Are there examples of real interaction – between astronomy & computer science? between astronomy & computer science? � More interesting than just number More interesting than just number- -crunching? crunching? �
Plan Plan � Computational Astrophysics Computational Astrophysics � – N N- -body simulations of galaxy clustering body simulations of galaxy clustering – � Astro Astro- -Informatics Informatics � – Survey astronomy & the Virtual Observatory Survey astronomy & the Virtual Observatory – � Discussion Discussion � – Astronomy and informatics Astronomy and informatics –
Observational Astronomy Observational Astronomy � Electromagnetic spectrum Electromagnetic spectrum � � Multiwavelength Multiwavelength view of a spiral galaxy view of a spiral galaxy � IRAS 25 µ NVSS 20cm IRAS 100 µ GB 6cm WENSS 92cm DSS Optical ROSAT ~keV – Different angular resolution of instruments – Different angular resolution of instruments – Different physical emission mechanisms Different physical emission mechanisms – (M51 graphics from Jim Gray & Alex (M51 graphics from Jim Gray & Alex Szalay Szalay) )
Changes in the way that Changes in the way that we make observations we make observations � Old Style: Old Style: Many small, specific programmes Many small, specific programmes � – Astronomer proposes observations, goes Astronomer proposes observations, goes – to telescope, brings data home on tape, to telescope, brings data home on tape, analyses data, publishes paper, puts tape analyses data, publishes paper, puts tape in desk drawer and forgets about it in desk drawer and forgets about it � New Style: New Style: Few large, multi Few large, multi- -use surveys use surveys � – Consortium designs survey to address Consortium designs survey to address – many science goals, undertakes survey many science goals, undertakes survey over several years, establishes database over several years, establishes database – many many people do people do different different science with science with – same data from DB data from DB same
Trends behind these changes Trends behind these changes � Instruments made easier to use & more Instruments made easier to use & more � effort put into data reduction software effort put into data reduction software – Easier to use data from new instrument Easier to use data from new instrument – – Multiwavelength Multiwavelength astronomy much easier astronomy much easier – � Instruments are more sensitive and have Instruments are more sensitive and have � more detector elements more detector elements – Can image large areas of sky quickly Can image large areas of sky quickly – – Survey mode of observation more efficient Survey mode of observation more efficient –
Very strong local interest Very strong local interest � Wide Field Astronomy Unit Wide Field Astronomy Unit � – Part of the Part of the UoE UoE Institute for Astronomy Institute for Astronomy – – Based at Royal Observatory Based at Royal Observatory – Edinburgh, on Blackford Blackford Hill Hill Edinburgh, on � Two strands to WFAU work Two strands to WFAU work � – Curation Curation of optical/near of optical/near- -infrared sky surveys infrared sky surveys – – Helping build the global Helping build the global “ “Virtual Observatory Virtual Observatory” ” –
The Virtual Observatory The Virtual Observatory � Goals Goals � – Federate all the world Federate all the world’ ’s astronomy data s astronomy data – – Provide resources for exploitation of data Provide resources for exploitation of data – � Challenges Challenges – – sociological & technical sociological & technical � – Heterogeneous, distributed datasets Heterogeneous, distributed datasets – � Lack of global schema; metadata often poor Lack of global schema; metadata often poor � – Legacy analysis codes in many languages Legacy analysis codes in many languages – � Solution Solution � – International collaboration International collaboration – – Architecture built on web services Architecture built on web services –
Schematic Virtual Observatory DB1 DB2 Registry Compute DB4 Resource DB3 User
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