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Overview

Magneticum Pathfinder aims to follow the formation of cosmological structures in a hitherto unaccomplished level of detail by performing a set of large scale and high resolution simulations, taking into account many physical processes to allow detailed comparison to a variety of multi-wavelength observational data. Such simulations need to incorporate a detailed description of various complex, non-gravitational, physical processes, which determine the evolution of the cosmic baryons and have an impact on their observational properties.

Cosmology

Using WMAP7 cosmology taken from Komatsu et al. 2010:
  • Total matter density Ω0=0.272 (16.8% baryons)
  • Cosmological constant Λ0=0.728
  • Hubble constant H0=70.4
  • Index of the primordial power spectrum n=0.963 
  • Overall normalisation of the power spectrum σ8=0.809

Physics included

  • cooling, star formation, winds (Springel & Hernquist 2003)
  • Metals, stellar population and chemical enrichment
    SN-Ia, SN-II, AGB (Tornatore et al. 2003/2006)
    new cooling tables (Wiersma et al. 2009)
  • Black holes and AGN feedback (Springel & Di Matteo 2006, Fabjan et al. 2010)
    various imrovements (Hirschmann et al. 2014)
  • Thermal Conduction (1/20th Spitzer) (Dolag et al. 2004)
  • Low viscosity scheme to track turbulence (Dolag et al. 2005)
    various improvements (Beck et al. 2015)
  • Higher order SPH Kernels (Dehnen & Aly 2012)
  • Magnetic Fields (passive) (Dolag & Stasyszyn 2009)

Simulations

Magneticum Pathfinder & Magneticum

  Box0 Box1 Box2b Box2 Box3 Box4 Box5
[Mpc/h] 2688 896 640 352 128 48 18
mr 2*45363 2*15263   2*5943 2*2163 2*813  
hr     2*28803 2*15843 2*5763 2*2163 2*813
uhr         2*15363 2*5763 2*2163
xhr           2*15363 2*5763
Table 1: Number of particles used in the Magneticum Pathfinder and Magneticum simulations for the different resolution levels mr, hr, uhr and xhr. The red entries mark simulations which are currently running or not ran to z=0, the gray entries mark future, planned simulations.

 mdmmgasepsdmepsgasepsstars
mr1.3e102.6e910105
hr6.9e81.4e83.753.752
uhr3.6e77.3e61.41.40.7
xhr1.9e63.9e50.450.450.25
Table 2: Mass of dm and gas particles (in Msol/h) at the different resolution levels and the according softenings (in kpc/h) used.

Extra Runs

Dark Matter control simulations

We performed dark matter only control runs for various boxes. To avoid a different sampling of the initial density fluctuations, all these runs were done using two dark matter particle species, e.g. by treating the gas particles as dark matter only particles. Available dark matter only simulations include Box1/mr, Box3/hr, Box4/uhr and Box5/xhr. The imprint of the baryonic physics on the mass function of haloes as covered by the simulation set can be seen in the figure at the right.

ANGUS (AustraliaN GADGET-3 early Universe Simulations)

This project aims to study the role of feedback from supernovae and black holes in the evolution of the star formation rate function (SFRF) of high redshift (z ~ 4 - 7) galaxies and consists of a set of small, high resolution boxes. The picture on the right shows a explorational simulation of Box5/xhr at z=2.7.

 Box4/0.5Box5Box6
[Mpc/h]241812
uhr 2*28832*21632*1443
axhr 2*38432*2563
xhr  2*57632*3843
Table 4: Number of particles used in the ANGUS simulations.

MHD simulations

Currently there are various simulations of Box3/hr as full MHD simulations available, including simulations combining cooling and star-formation with MHD. They are probing different scenarious for the origin of the magnetic field within the LSS. The picture on the left shows a slice through the simulation at z=1, showing the gas density (left panel) and the magnetic field distribution (right panel), as obtained from the magnetig field seeding from supernova events.

Post processing

On the fly post-processing

Similar to Saro et al. (2006), we compute the luminosities of our simulated galaxies on the fly using the stellar population synthesis model of Bruzual & Charlot (2007) in different spectral bands (u,V,G,r,i,z,Y,J,H,K,L,M) as well as their star-formation rate and HI content. Following Nuzza et. al. 2010 we also take dust attenuation into account when computing the observer frame luminosities. The attenuation is estimated from the radial dust profile, computed from the metal and HI profile of the galaxies.

Data Management

All simulation outputs employ an algorithm which sorts the particles among the CPUs among a space filling curve and produces an auxiliary file which allows us to identify the sub-data volume elements of any stored property among all particle species associated to each element of the space filling curve used. Finally there is a super index build which allows us to identify the sub snapshots which belongs to the different parts of the space filing curve. This allows us to effectively collect all data associated to a given volume in space (defined by the elements of the space filling curve it occupies) with a minimum of reading overhead (see figure on the right).