1. Simulating Everything: Galaxy Cluster Mergers in a Supercomputer
John ZuHone
Department of Astronomy and Astrophysics
University of Chicago

2. Clusters of Galaxies Why are clusters interesting? Lots of physics!
Galaxies: star formation, supernovae, active galactic nuclei
Intracluster medium: diffuse (n ~ cm-3), hot (T ~ K), magnetized plasma emitting free-free radiation in X-rays
Dark matter: collisionless particles which do not interact electromagnetically, form the bulk of the mass in clusters
Probes of cosmology
Number of clusters of a given mass as a function of redshift depends on cosmological parameters
Determine cluster masses from “scaling relations” (mass-temperature, mass-luminosity, etc.) under the assumption of hydrostatic equilibrium
Abell 1689 (Credit: NASA)

3. Cluster Mergers and Collisions
Cosmological structure formation proceeds in a “bottom-up” fashion
We see lots of mergers!
Why is it important to understand mergers?
Merging reveals the different physical properties of the different kinds of material, and may even reveal new physics (e.g., properties of the dark matter)
Understanding the merging process and its effects on global cluster properties (temperature, luminosity, etc.) helps to calibrate and constrain cluster scaling relations and therefore improves the estimates of cosmological parameters
Abell 222 and Abell 223 (Credit: ESA)

4. Cluster Mergers and Collisions
1E , “The Bullet Cluster” (Credit: Chandra X-Ray Center)

5. Cl
Galaxy Cluster Cl
Rich cluster of galaxies at a redshift of z = 0.4 (~5 billion light-years away)
One of the best examples of gravitational lensing of distant galaxies
Indications of a Collision Along the Line of Sight?
Bimodal galaxy velocity distribution
Unusual mass profile derived from gravitational lensing
X-ray surface brightness analysis indicates the existence of two components co-aligned along our line of sight
Credit: NASA

6. Testing for Cluster Components
Ota et al. 2004

7. Scientific questions concerning Cl 0024+17
Can we distinguish the existence of two cluster components aligned along our line of sight from just one?
Assuming hydrostatic equilibrium, how accurate a mass estimate can we get assuming one cluster? Two clusters?
What does this system tell us about the possibilities of discerning cluster components projected along the line of sight for other systems?

8. The FLASH Code
FLASH: A multiphysics, modular, parallel astrophysical simulation code
Essential components:
Block-Structured Adaptive Mesh Refinement (PARAMESH library)
Octree(s) of blocks make up the mesh
Refine the mesh on built-in or user-specified criteria
Can go to higher resolution using less memory and less time
Eulerian Hydrodynamics
Piecewise-Parabolic Method (PPM, Woodward & Colella 1984)
Higher-order Godunov method
Well-suited to capture sharp features like shocks and contact discontinuities
N-body
Particle-based systems, e.g. dark matter, stars, cosmic rays
Map particles to mesh (particle mass to grid density)
Map mesh to particles (grid forces to particle accelerations)

9. The FLASH Code Adaptive Mesh Refinement Top-Level Block Parent blocks
Leaf Blocks
Guard Cells

10. Setting up a Cluster Collision
Physics Modules
Massive particles representing dark matter
PPM solver for hydrodynamics
Ideal gamma-law equation of state with  = 5/3
Multigrid solver for gravitational potential (Ricker 2008)
Initial Conditions
2:1 mass ratio, M1 = 6  1014 M, M2 = 3  1014 M
Relative velocity: vrel = 3000 km/s, derived from redshift data
Ratio of gas mass to total mass: fgas = 0.12
Temperature profile derived assuming hydrostatic equilibrium
Simulation Parameters
~3 million particles
Refine mesh on sharp features in fluid and on matter density
Box size: L = 10h-1 Mpc
Finest AMR resolution: ∆x = 9.77h-1 kpc

11. Computing Resources
Simulations were run on the ASC Linux Cluster (ALC) at Lawrence Livermore National Laboratory

12. Dark Matter Density (M kpc-3)

13. Gas Density (cm-3)

14. Gas Temperature (keV)

15. Connecting Simulations to Observations
However, in real clusters we don’t get to know physical quantities directly as in simulations: we get all our information from photons
Such quantities (density, temperature, metallicity) must be derived from fitting spatial and energy distributions of photons to physically motivated models
The questions we will be able to answer from observations depend heavily on what we’re capable of doing with our instrument:
Resolution of the detector(s) (both in position and energy space)
Exposure time (how long can we observe?)
Ability to account for and model extraneous influences (point source contamination, backgrounds, foregrounds, etc.)
Creating simulated observations of our simulated systems help us to determine what scientific questions will be able to be clearly posed and answered in real systems

16. Simulating X-Ray Observations with MARX
Chandra X-Ray Telescope
Launched by NASA in 1999
Has observed planets, compact objects, supernovae, galaxies, and galaxy clusters
High resolution CCDs (0.5 seconds of arc on the sky per pixel)
MARX
Simulates on-orbit performance of Chandra
Mirror, detectors, gratings
Quantum efficiency, aspect motion
Variety of models
Point sources, disk models, cluster models
User-generated models
Credit: Chandra X-ray Center

17. From FLASH to photons Flux Map FLASH 2D Flux MARX Generator Dataset
(,T,Z)
2D Flux
Map
(,,,FX)
Images
Standard
Chandra
Tools
Photon
Events
File
Spectra
Profiles

18. Verification Study
Q: Does our procedure for generating synthetic observations work?
A: Reproduce simple test cases
Case 1: Isothermal, “-model” cluster
Case 2: Two isothermal -model clusters
Check that fitting procedure recovers temperature of gas and parameters of radial profiles

19. Simulated Image

20. Simulated Spectrum and Fitted Model
Fitted Temperature:
Tspec = 2.52 keV

21. Fitting Surface Brightness Profiles
Significant improvement in 2!

22. Mass Estimates
From the surface brightness profile and temperature information we can derive a mass estimate assuming hydrostatic equilibrium:
Estimated masses t ~ 1-2 Gyr after the collision come within 10% of the true projected mass!

23. Implications for Cosmology
X-ray surveys of portions of the sky look for clusters at a wide range of redshifts (distances) to determine the cluster mass function and help constrain cosmology
Co-aligned clusters appearing as a single cluster would give an inaccurate mass estimate; would it be possible to distinguish between the two cluster components?
However, each cluster found in such surveys has a much shorter exposure time than in single observations, thus fewer photons for each
Even so, we find that by resituating our clusters at higher redshift and resimulating shorter exposure times, even then we are able to distinguish between the two cluster components using the 2-test
Therefore, a possible systematic effect in determining the masses of clusters could be accounted for

24. Conclusions
Making an accurate connection between simulations and observations is essential to true understanding of astrophysical systems
Bridging the gap: simulated X-ray observations of a simulated galaxy cluster collision
Implications for Cl :
Statistical analysis of the surface brightness profile indicates the existence of two cluster components
Shortly after the collision, an estimate of the mass based on the assumption of hydrostatic equilibrium can be made to within ~10%, but only if the existence of both components is assumed
Even at low exposure times and high redshifts, it may be possible to determine the existence of co-aligned components, which aids the use of X-ray surveys of clusters to constrain cosmology

25. THANKS! Acknowledgements Don Lamb (advisor) ASC FLASH Center team
Collaborators at the University of Illinois at Urbana-Champaign:
Paul Ricker
Karen Yang (developed synthetic X-ray pipeline)
Practicum Advisor: Bronson Messer
Krell Institute and DOE
THANKS!