GalaxyMaker and MoMaF

Project Leader

Jeremy Blaizot, Benjamin Depardon CRAL, LIP jeremy.blaizot@obs.univ-lyon1.fr benjamin.depardon@ens-lyon.fr

Simulations are often used in astrophysics to validate models. However, such models, like the one describing galaxies formation, rely on a set of parameters one has to tune so as to fit observational data. Thus, in order to find the best set, or the best sets of parameters, we need to compare the result of the model with real observational data. Hence, we need to run simulations with as many sets of parameters as possible to build catalogues of galaxies, build mock observational data, and compare them to real data obtained using real instruments. This is the aim of the two following software: GalaxyMaker and MoMaF.

Galics is a hybrid model for hierarchical galaxy formation studies, combining the outputs of large cosmological N-body simulations with simple, semi-analytic recipes to describe the fate of the baryons within dark matter halos. The simulations produce a detailed merging tree for the dark matter halos including complete knowledge of the statistical properties arising from the gravitational forces. GalaxyMaker, is one the software composing Galics, it applies a semi-analytical model to form galaxies, and create catalogues of galaxies.

MoMaF converts theoretical outputs of hierarchical galaxy formation models into catalogues of virtual observations. The general principle is straightforward: mock observing cones can be generated using semi-analytically post-processed snapshots of cosmological N-body simulations. These cones can then be projected to synthesize mock sky images.

A typical simulation is composed of the following workflow, we need to run it for each input parameters set we wish to analyse:

  • Run GalaxyMaker on one or many input files (typically a hundred files).
  • All the produced outputs are merged, and then post-processed using MoMaF. An instance of MoMaF is necessary for each parameter set for each line of sight (typically around a hundred).

    The following figure depicts the workflow.

    workflow

    Execution of this workflow is available through a web portal. It has been developed using the DIETWebboard, a tool for online management of DIET services and requests. The website allows for parameter sweep submissions, and takes care of data management. Within DIET, the MA DAG, i.e. a special agent in charge of scheduling workflow tasks, handles tasks management. Data management is handled by DAGDA: all generated files are stored inside containers, and dynamically created cleanup services take care of platform integrity and cleanup once a workflow is finished. The following figure presents a more detailed view of a workflow execution.

    detailed workflow

Website:

License type:

Not distributed.

The needs:

  • Language(s):

    Fortran 90, C/C++ for the client and servers

  • Compiler(s):

    PGF90, iFort, gnufortran, gcc

  • Library(ies):

    none

  • System(s):

    Linux

  • Memory:

    Up to 2 GB.

  • Disk:

    From a few MB to hundreds of GB

  • Mean execution time:

    From a few seconds to several days.

  • Number of processors:
    • Minimum: 1
    • Maximum: 1

      This is a sequential application

  • Use of a Database:

    No.