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Modified Newtonian Dynamics as an Alternative to Dark Matter

Open access
Sanders, Robert H., McGaugh, Stacy S.·2002·Annual Review of Astronomy and Astrophysics

Abstract

▪ Abstract Modified Newtonian dynamics (MOND) is an empirically motivated modification of Newtonian gravity or inertia suggested by Milgrom as an alternative to cosmic dark matter. The basic idea is that at accelerations below a o≈ 10−8 cm/s 2≈ cH o/6 the effective gravitational attraction approaches [Formula: see text], where g n is the usual Newtonian acceleration. This simple algorithm yields flat rotation curves for spiral galaxies and a mass-rotation velocity relation of the form M ∝ V 4 that forms the basis for the observed luminosity–rotation velocity relation—the Tully-Fisher law. We review the phenomenological success of MOND on scales ranging from dwarf spheroidal galaxies to superclusters and demonstrate that the evidence for dark matter can be equally well interpreted as evidence for MOND. We discuss the possible physical basis for an acceleration-based modification of Newtonian dynamics as well as the extention of MOND to cosmology and structure formation.

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Crossref · 10.1146/annurev.astro.40.060401.093923

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Metadata

Authors
Sanders, R. H., & McGaugh, S. S.
Venue
Annual Review of Astronomy and Astrophysics
Published
2002-09
Volume
40
Issue
1
Pages
263-317
ISSN
  • 0066-4146 (ISSN-L)
  • 1545-4282
OpenAlex
W2115399036
Cited by
723
References
5
FWCI
1.90
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Modified Newtonian Dynamics as an Alternative to Dark Matter. https://app.cochat.ai/papers/10.1146/annurev.astro.40.060401.093923
The paper
Sanders, R. H., & McGaugh, S. S. (2002). Modified Newtonian Dynamics as an Alternative to Dark Matter. Annual Review of Astronomy and Astrophysics. https://doi.org/10.1146/annurev.astro.40.060401.093923

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.