Constraints on axionlike particles from 16.5 years of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>F</mml:mi> <mml:mi>e</mml:mi> <mml:mi>r</mml:mi> <mml:mi>m</mml:mi> <mml:mi>i</mml:mi> </mml:math> -LAT data and prospects for VLAST
Bibliographic record
Abstract
Axionlike particles (ALPs), hypothetical particles beyond the Standard Model (SM), are considered as promising dark matter candidates. ALPs can convert into photons and vice versa in a magnetic field via the Primakoff effect, potentially generating detectable oscillation in γ -ray spectra. This study analyzes 16.5 years of data from the Fermi Large Area Telescope (Fermi-LAT) on NGC 1275, the brightest galaxy in the Perseus cluster, to constrain the ALP parameter space. Our results improve the previous 95% exclusion limits of the photon-ALP coupling g a γ by a factor of 2 in the ALP mass range of 4 × 10 − 10 eV ≲ m a ≲ 5 × 10 − 9 eV . Moreover, we investigate the projected sensitivity of the future Very Large Area γ -ray Space Telescope (VLAST) on searching for ALPs. We find that (i) the expected sensitivity on the photon-ALP coupling g a γ can be stronger than that from the upcoming International Axion Observatory in the ALP mass range of 2 × 10 − 11 eV ≲ m a ≲ 1 × 10 − 7 eV , with the best sensitivity of g a γ ∼ 7 × 10 − 13 GeV − 1 at m a ∼ 2 × 10 − 10 eV ; (ii) VLAST can extend the sensitivity of the ALP masses below 5 × 10 − 12 eV , where the photon-ALP coupling g a γ ≳ 1.5 × 10 − 11 GeV − 1 will be excluded; (iii) the entire parameter space of ALP accounting for TeV transparency can be fully tested. These results demonstrate that VLAST will offer an excellent opportunity for ALPs searches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".