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Cited by 904

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Non-baryonic dark matter: observational evidence and detection methods

Open access
Bergström, Lars·2000·Reports on Progress in Physics
DOI 10.1088/0034-4885/63/5/2r3
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Abstract

The evidence for the existence of dark matter in the universe is reviewed. A general picture emerges, where both baryonic and non-baryonic dark matter is needed to explain current observations. In particular, a wealth of observational information points to the existence of a non-baryonic component, contributing between around 20 and 40% of the critical mass density needed to make the universe geometrically flat on large scales. In addition, an even larger contribution from vacuum energy (or cosmological constant) is indicated by recent observations. To the theoretically favoured particle candidates for non-baryonic dark matter belong axions, supersymmetric particles, and of less importance, massive neutrinos. The theoretical foundation and experimental situation for each of these is reviewed. Direct and indirect methods for detection of supersymmetric dark matter are described in some detail. Present experiments are just reaching the required sensitivity to discover or rule out some of these candidates, and major improvements are planned over the coming years.

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Metadata

Authors
Bergström, L.
Venue
Reports on Progress in Physics
Publisher
IOP Publishing
Published
2000-04-27
Volume
63
Issue
5
Pages
793-841
ISSN
  • 0034-4885 (ISSN-L)
  • 1361-6633
DOI
10.1088/0034-4885/63/5/2r3
OpenAlex
W2082637753
Cited by
904
References
10
FWCI
17.30
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Non-baryonic dark matter: observational evidence and detection methods. https://app.cochat.ai/papers/10.1088/0034-4885/63/5/2r3
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Bergström, L. (2000). Non-baryonic dark matter: observational evidence and detection methods. Reports on Progress in Physics. https://doi.org/10.1088/0034-4885/63/5/2r3

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

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