Reviving MeV-GeV indirect detection with inelastic dark matter
Bibliographic record
Abstract
Thermal relic dark matter below ∼ 10 GeV is excluded by cosmic microwave background data if its annihilation to visible particles is unsuppressed near the epoch of recombination. Usual model-building measures to avoid this bound involve kinematically suppressing the annihilation rate in the low-velocity limit, thereby yielding dim prospects for indirect detection signatures at late times. In this work, we investigate a class of cosmologically viable sub-GeV thermal relics with late-time annihilation rates that are detectable with existing and proposed telescopes across a wide range of parameter space. We study a representative model of inelastic dark matter featuring a stable state χ 1 and a slightly heavier excited state χ 2 whose abundance is thermally depleted before recombination. Since the kinetic energy of dark matter in the Milky Way is much larger than it is during recombination, χ 1 χ 1 → χ 2 χ 2 upscattering can efficiently regenerate a cosmologically long-lived Galactic population of χ 2 , whose subsequent coannihilations with χ 1 give rise to observable gamma-rays in the ∼ 1 MeV − 100 MeV energy range. We find that proposed MeV gamma-ray telescopes, such as e-ASTROGAM, AMEGO, and MAST, would be sensitive to much of the thermal relic parameter space in this class of models and thereby enable both discovery and model discrimination in the event of a signal at accelerator or direct detection experiments. Published by the American Physical Society 2024
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".