Search for Dark Matter Axions with Tunable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>TM</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>020</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> Mode
Bibliographic record
Abstract
Axions are hypothesized particles believed to potentially resolve two major puzzles in modern physics: the strong CP problem and the nature of dark matter. Cavity-based axion haloscopes represent the most sensitive tools for probing their theoretically favored couplings to photons in the microelectronvolt range. However, as the search mass (or frequency) increases, the detection efficiency decreases, largely due to a decrease in cavity volume. Despite the potential of higher-order resonant modes to preserve experimental volume, their practical application in searches has been limited by the challenge of maintaining a high form factor over a reasonably wide search bandwidth. We introduce an innovative tuning method that uses the unique properties of auxetic materials, designed to effectively tune higher modes. This approach was applied to the TM_{020} mode for a dark matter axion search exploring a mass range from 21.38 to 21.79 μeV, resulting in the establishment of new exclusion limits for axion-photon coupling greater than approximately 10^{-13} GeV^{-1}. These findings should allow use of higher-order modes for cavity haloscope searches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".