Upcoming searches for decaying dark matter with ULTRASAT ultraviolet maps
Bibliographic record
Abstract
Decaying dark matter (DDM) can be tested via different astrophysical and cosmological probes. In particular, particles in the $\ensuremath{\sim}9.5--30\text{ }\mathrm{eV}$ mass range that decay into monochromatic photons, would contribute to the extragalactic background light (EBL) in the ultraviolet (UV) bandwidth. In this work, we show that an intriguing improvement to the constraints on such DDM models can come from broadband UV surveys, such as GALEX or the upcoming ULTRASAT satellite. These provide diffuse light maps of the UV-EBL, integrated over a wide redshift range. The cross correlation between intensity fluctuations in these maps with a reference spectroscopic galaxy survey, can be used to reconstruct the redshift evolution of the EBL intensity; in this way, it is also possible to detect signatures of contributions from DDM. We forecast the constraining power of $(\mathrm{GALEX}+\mathrm{ULTRASAT})\ifmmode\times\else\texttimes\fi{}\mathrm{DESI}$, and we show they will be able to detect DDM with decay rate up to $\mathcal{O}({10}^{\ensuremath{-}26}\text{ }\text{ }\mathrm{s})$. In the context of axionlike particles (ALP), our forecasts can be converted to constraints on the ALP-photon coupling; our results show this technique will test ALP with coupling $\ensuremath{\lesssim}\mathcal{O}({10}^{\ensuremath{-}12}\text{ }\text{ }{\mathrm{GeV}}^{\ensuremath{-}1})$, more than an order of magnitude better than current bounds in this mass range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".