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Oscar Agertz. Profile photo.

Oscar Agertz

Associate Professor / Senior university lecturer / Wallenberg Academy Fellow

Oscar Agertz. Profile photo.

From particles to orbits : precise dark matter density profiles using dynamical information

Author

  • Claudia Muni
  • Andrew Pontzen
  • Jason L. Sanders
  • Martin P. Rey
  • Justin I. Read
  • Oscar Agertz

Summary, in English

We introduce a new method to calculate dark matter halo density profiles from simulations. Each particle is ‘smeared’ over its orbit to obtain a dynamical profile that is averaged over a dynamical time, in contrast to the traditional approach of binning particles based on their instantaneous positions. The dynamical and binned profiles are in good agreement, with the dynamical approach showing a significant reduction in Poisson noise in the innermost regions. We find that the inner cusps of the new dynamical profiles continue inward all the way to the softening radius, reproducing the central density profile of higher resolution simulations within the 95 per cent confidence intervals, for haloes in virial equilibrium. Folding in dynamical information thus provides a new approach to improve the precision of dark matter density profiles at small radii, for minimal computational cost. Our technique makes two key assumptions that the halo is in equilibrium (phase mixed) and the potential is spherically symmetric. We discuss why the method is successful despite strong violations of spherical symmetry in the centres of haloes, and explore how substructures disturb equilibrium at large radii.

Department/s

  • eSSENCE: The e-Science Collaboration
  • Astrophysics

Publishing year

2024

Language

English

Pages

9250-9262

Publication/Series

Monthly Notices of the Royal Astronomical Society

Volume

527

Issue

3

Document type

Journal article

Publisher

Oxford University Press

Topic

  • Astronomy, Astrophysics and Cosmology

Keywords

  • dark matter
  • galaxies: haloes
  • galaxies: kinematics and dynamics

Status

Published

ISBN/ISSN/Other

  • ISSN: 0035-8711