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arXiv · 1206.4895

Cusp-core transformations in dwarf galaxies: observational predictions

Romain Teyssier·Andrew Pontzen·Yohan Dubois·Justin Read
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Abstract

The presence of a dark matter core in the central kiloparsec of many dwarf galaxies has been a long standing problem in galaxy formation theories based on the standard cold dark matter paradigm. Recent cosmological simulations, based on Smooth Particle Hydrodynamics and rather strong feedback recipes have shown that it was indeed possible to form extended dark matter cores using baryonic processes related to a more realistic treatment of the interstellar medium. Using adaptive mesh refinement, together with a new, stronger supernovae feedback scheme that we have recently implemented in the RAMSES code, we show that it is also possible to form a prominent dark matter core within the well-controlled framework of an isolated, initially cuspy, 10 billion solar masses dark matter halo. Although our numerical experiment is idealized, it allows a clean and unambiguous identification of the dark matter core formation process. Our dark matter inner profile is well fitted by a pseudo-isothermal profile with a core radius of 800 pc. The core formation mechanism is consistent with the one proposed recently by Pontzen & Governato. We highlight two key observational predictions of all simulations that find cusp-core transformations: (i) a bursty star formation history with peak to trough ratio of 5 to 10 and a duty cycle comparable to the local dynamical time; and (ii) a stellar distribution that is hot with v/sigma=1. We compare the observational properties of our model galaxy with recent measurements of the isolated dwarf WLM. We show that the spatial and kinematical distribution of stars and HI gas are in striking agreement with observations, supporting the fundamental role played by stellar feedback in shaping both the stellar and dark matter distribution.

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Romain Teyssier, Andrew Pontzen, Yohan Dubois, Justin Read. 2012-06-21. Cusp-core transformations in dwarf galaxies: observational predictions. https://doi.org/10.1093/mnras/sts563

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