The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Broadband and Resonant Approaches to Axion Dark Matter Detection
Open accessAbstract
When ultralight axion dark matter encounters a static magnetic field, it sources an effective electric current that follows the magnetic field lines and oscillates at the axion Compton frequency. We propose a new experiment to detect this axion effective current. In the presence of axion dark matter, a large toroidal magnet will act like an oscillating current ring, whose induced magnetic flux can be measured by an external pickup loop inductively coupled to a SQUID magnetometer. We consider both resonant and broadband readout circuits and show that a broadband approach has advantages at small axion masses. We estimate the reach of this design, taking into account the irreducible sources of noise, and demonstrate potential sensitivity to axionlike dark matter with masses in the range of 10^{-14}-10^{-6} eV. In particular, both the broadband and resonant strategies can probe the QCD axion with a GUT-scale decay constant.
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
Read, J. I. (2014) · Journal of Physics G: Nuclear and Particle Physics · doi:10.1088/0954-3899/41/6/063101 · Cited by 489
Cited by
New experimental approaches in the search for axion-like particles
Irastorza, I. G., & Redondo, J. (2018) · Progress in Particle and Nuclear Physics · doi:10.1016/j.ppnp.2018.05.003 · Cited by 745
The landscape of QCD axion models
Di Luzio, L., Giannotti, M., Nardi, E., & Visinelli, L. (2020) · Physics Reports · doi:10.1016/j.physrep.2020.06.002 · Cited by 657
Hui, L. (2021) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-120920-010024 · Cited by 373
Metadata
- Authors
- Kahn, Y., Safdi, B. R., & Thaler, J.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2016-09-30
- Volume
- 117
- Issue
- 14
- Pages
- 141801-141801
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2527143382
- Cited by
- 364
- References
- 1
- FWCI
- 18.98
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Broadband and Resonant Approaches to Axion Dark Matter Detection. https://app.cochat.ai/papers/10.1103/physrevlett.117.141801
- The paper
- Kahn, Y., Safdi, B. R., & Thaler, J. (2016). Broadband and Resonant Approaches to Axion Dark Matter Detection. Physical Review Letters. https://doi.org/10.1103/physrevlett.117.141801
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.