Presentation on theme: "Comparing RAVE with theoretical models of the Milky Way Sanjib Sharma Joss Bland Hawthorn University of Sydney."— Presentation transcript:
Comparing RAVE with theoretical models of the Milky Way Sanjib Sharma Joss Bland Hawthorn University of Sydney
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.
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)
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
Theoretical Model-Analytical Models Star Formation Rate SFR Initial Mass Function IMF Age Metallicity Relation AMR Phase space distribution
Sampling Analytical Model (Von Neumann rejection sampling)
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
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
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
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 )
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
This is a good match which means σ vz (τ) is approximately correct.
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.
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.