The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Scalar dark matter candidates
Open accessAbstract
Abstract We investigate the possibility that dark matter could be made of scalar candidates and focus, in particular, on the unusual mass range between a few MeVs and a few GeVs. After showing why the Lee–Weinberg limit (which usually forbids a dark matter mass below a few GeVs) does not necessarily apply in the case of scalar particles, we discuss how light candidates ( m dm O (GeV)) can satisfy both the gamma ray and relic density constraints. We find two possibilities. Either dark matter is coupled to heavy fermions (but if m dm ≲100 MeV, an asymmetry between the dark matter particle and antiparticle number densities is likely to be required), or dark matter is coupled to a new light gauge boson U . The (collisional) damping of light candidates is, in some circumstances, large enough to be mentioned, but in most cases too small to generate a non-linear matter power spectrum at the present epoch that differs significantly from the cold dark matter spectrum. On the other hand, heavier scalar dark matter particles (i.e., with m dm ≳ O (GeV)) turn out to be much less constrained. We finally discuss a theoretical framework for scalar candidates, inspired from theories with N =2 extended supersymmetry and/or extra space dimensions, in which the dark matter stability results from a new discrete (or continuous) symmetry.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Structure of Cold Dark Matter Halos
Navarro, J. F., Frenk, C. S., & White, S. D. M. (1996) · The Astrophysical Journal · doi:10.1086/177173 · Cited by 8,544
Cited by
Particle dark matter: evidence, candidates and constraints
Bertone, G., Hooper, D., & Silk, J. (2005) · Physics Reports · doi:10.1016/j.physrep.2004.08.031 · Cited by 4,979
Dark matter self-interactions and small scale structure
Tulin, S., & Yu, H.-B. (2018) · Physics Reports · doi:10.1016/j.physrep.2017.11.004 · Cited by 1,111
Bland-Hawthorn, J., Sharma, S., Tepper-Garcia, T., Binney, J., Freeman, K. C., Hayden, M. R., Kos, J., De Silva, G. M., Ellis, S., Lewis, G. F., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz217 · Cited by 224
Metadata
- Authors
- Bœhm, C., & Fayet, P.
- Venue
- Nuclear Physics B
- Publisher
- Elsevier BV
- Published
- 2004-04
- Volume
- 683
- Issue
- 1-2
- Pages
- 219-263
- ISSN
- 0550-3213 (ISSN-L)
- OpenAlex
- W1990985723
- Cited by
- 672
- References
- 1
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.