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Comparing RAVE with theoretical models of the Milky Way Sanjib Sharma Joss Bland Hawthorn University of Sydney.

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Presentation on theme: "Comparing RAVE with theoretical models of the Milky Way Sanjib Sharma Joss Bland Hawthorn University of Sydney."— Presentation transcript:

1 Comparing RAVE with theoretical models of the Milky Way Sanjib Sharma Joss Bland Hawthorn University of Sydney

2 Outline Galaxia a code for generating a synthetic/mock survey according to a given galaxy evolution model A mock RAVE survey with Galaxia What matches and what does not. What can we learn with RAVE, constraining models with observations.

3 Motivation A framework to compare theoretical models of our Galaxy with observations. Theoretical model Observed Catalog Theory of Stellar Evolution (Isochrones) Galaxia Synthetic Catalog AnalyticalN-body Comparison Monte Carlo Markov Chain, Chi square, etc (Extinction, Measurement Errors) Observational Space l, b, r, μ l, μ b, v r, B, V, log(g) (Age,Pos,Vel,Metallicity,Mass)

4 Drawbacks of current schemes Besancon Model- state of the art (Robin et al 2003) Also Trilegal, (Girardi et al, Padova group) Designed for simulating a particular line of sight –at max 25 line of sights Discrete (l,b,r) step sizes to be supplied by user Not suitable for wide area surveys, or large catalog of stars – takes too much time No possibility to simulate substructures or incorporate N- body models –Sagittarius dwarf galaxy, simulation of tidally disrupted galaxies

5 Theoretical Model-Analytical Models Star Formation Rate SFR Initial Mass Function IMF Age Metallicity Relation AMR Phase space distribution

6 Sampling Analytical Model (Von Neumann rejection sampling)

7 Adaptive Mesh (Barnes Hut Tree)

8 Optimization To generate a patch do not need to generate the full galaxy –If a survey is not all sky, first check if a node intersects with survey geometry. Faint stars which dominate in number are visible only for nearby nodes. For far away nodes there is a minimum mass above which stars are visible –Sort nodes according to distance. Calculate appropriate m –Generate only those stars that are visible. r x y z m min m m max r

9 Galaxia summary Analytical model for disc, with warp –Robin et al 2003 (Besancon model) Padova Isochrones –m >0.15, Marigo et al 2008, Bertilli et al 1994 –0.07<m<0.15 Chabrier et al 2000 3d extinction model –double exponential disc with warp and flare, h R =4.4 kpc, h z =0.088 kpc – E(B-V) at infinity match Schlegel et al 1998 or –0.54 mag/kpc in solar neighborhood

10 Computational Performance Run time nearly linear with mass of the galaxy being simulated –Due to the use of adaptive mesh or node Speed- 0.16 million stars per second (2.44 GHz proc) –For shallower surveys a factor of 3 less V<20, 10,000 sq degrees towards NGP, 35 ×10 6 stars, 220 secs V<20 GAIA like survey 4 billion stars can be generated in 6 hours on a single CPU

11 A synthetic RAVE survey RAVE_internalA (S/N>20, July, VDR3) For each RAVE field match the number distribution of stars in I DENIS magnitude (9-12). –Assumption RAVE is a magnitude limited survey –Number counts were matched per 0.25 mag bin –Proper sampling required about 50 million stars in 9<I DENIS <12 Add extinction, add observational errors –Photometric errors for the time being only added for 2MASS J and K not I DENIS –Stellar parameter errors taken from Siebert et al 2011 σ= σ(log(g),T eff )

12 All stars

13 Dwarfs

14 Giants

15 Red Clumps (Siebert et al 2011 criteria)

16 (N RAVE -N model )/(N RAVE +N model )

17 Log(g) Vs T eff

18 Checking the kinematics Let us assume we have faithfully reproduced the RAVE catalog in color magnitude space. For the time being let us take radial velocities only. Convert to U V W components and check the distribution. Contains information about U Sun, V Sun, W Sun,, σ v (R,τ), τ being age

19 X y

20 Values of U Sun V Sun sensitive to weights used, range used to fit. Inner and outer regions cannot be matched simultaneously


22 This is a good match which means σ vz (τ) is approximately correct.

23 What needs to be done Need to move beyond simple one dimensional fits. Multidimensional parameter search. A model fitting machinery. Need to use distance information also. Simultaneous constraints from different surveys SDSS, 2MASS, Hipparcos etc.

24 Hunting for structures Multi-dimensional group finder EnLinK. –Density based hierarchical group finder –Uses nearest neighbor links to search for peaks and valleys in density distribution. –Gives a statistical significance of a group Develop a similar scheme for finding groups where we look for differences between a given data and an expected smooth model. Exploit all the multidimensional information that RAVE provides.

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