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تسجيل مستخدم جديدSimulating disk galaxies and interactions in Milgromian dynamics
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Since its publication 1983, Milgromian dynamics (aka MOND) has been very successful in modeling the gravitational potential of galaxies from baryonic matter alone. However, the dynamical modeling has long been an unsolved issue. In particular, the setup of a stable galaxy for Milgromian N-body calculations has been a major challenge. Here, we show a way to set up disc galaxies in MOND for calculations in the PHANTOM OF RAMSES (PoR) code by Lughausen (2015) and Teyssier (2002). The method is done by solving the QUMOND Poisson equations based on a baryonic and a phantom dark matter component. The resulting galaxy models are stable after a brief settling period for a large mass and size range. Simulations of single galaxies as well as colliding galaxies are shown.
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A great challenge in present-day physics is to understand whether the observed internal dynamics of galaxies is due to dark matter matter or due to a modification of the law of gravity. Recently, van Dokkum et al. reported that the ultra-diffuse dwar
f galaxy NGC1052-DF2 lacks dark matter, and they claimed that this would -- paradoxically -- be problematic for modified gravity theories like Milgromian dynamics (MOND). However, NGC1052-DF2 is not isolated, so that a valid prediction of its internal dynamics in MOND cannot be made without properly accounting for the external gravitational fields from neighbouring galaxies. Including this external field effect following Haghi et al. shows that NGC1052-DF2 is consistent with MOND.
Previous studies of globular cluster (GC) systems show that there appears to be a universal specific GC formation efficiency $eta$ which relates the total mass of GCs to the virial mass of host dark matter halos, $M_{vir}$ (Georgiev et al 2010, Spitl
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We present results of cosmological simulations of disk galaxies carried out with the GADGET-3 TreePM+SPH code, where star formation and stellar feedback are described using our MUlti Phase Particle Integrator (MUPPI) model. This description is based
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