Constraining $z\lesssim 2$ ultraviolet emission with the upcoming ULTRASAT satellite
Bibliographic record
Abstract
The Extragalactic Background Light (EBL) carries a huge astrophysical and cosmological content: its frequency spectrum and redshift evolution are determined by the integrated emission of unresolved sources, these being galaxies, active galactic nuclei, or more exotic components. The near-UV region of the EBL spectrum is currently not well constrained, yet a significant improvement can be expected thanks to the soon-to-be launched Ultraviolet Transient Astronomy Satellite (ULTRASAT). Intended to study transient events in the $2300-2900\,{\rm Å}$ observed band, this detector will provide wide field maps, tracing the UV intensity fluctuations on the largest scales. In this paper, we suggest how to exploit ULTRASAT to reconstruct the redshift evolution of the UV-EBL volume emissivity. We build upon the work of Chiang et al. (2019), where the Clustering-Based Redshift (CBR) technique was used to study diffuse light maps from GALEX. Their results showed the capability of the cross correlation between GALEX and SDSS spectroscopic catalogs in constraining the UV emissivity, highlighting how CBR is sensitive only to the extragalactic emissions, avoiding foregrounds and Galactic contributions. In our analysis, we introduce a framework to forecast the CBR constraining power when applied to ULTRASAT and GALEX in cross correlation with the 5-year DESI spectroscopic survey. We show that these will yield a strong improvement in the measurement of the UV-EBL volume emissivity. For $λ= 1500\,{\rm Å}$,non-ionizing continuum below $z \sim 2$, we forecast a $1σ$ uncertainty $\lesssim 26\%\,(9\%)$ with conservative (optimistic) bias priors using ULTRASAT full-sky map; similar constraints can be obtained from its low-cadence survey, which will provide a smaller but deeper map. We finally discuss how these results will foster our understanding of UV-EBL models.
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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 | 0.000 | 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) | 0.001 | 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".