Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Context. 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, with 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 Å observed band, this detector will provide wide field maps tracing the UV intensity fluctuations at the largest scales. Aims. In this paper, we suggest how to exploit the ULTRASAT full-sky map as well as its low-cadence survey in order to reconstruct the redshift evolution of the UV-EBL volume emissivity. We build upon the work of Chiang et al. (2019, ApJ, 870, 120), who used the clustering-based redshift (CBR) technique to study diffuse light maps from GALEX. Their results showed the capability of the cross correlation between GALEX and SDSS spectroscopic catalogs in constraining UV emissivity, highlighting how CBR is sensitive only to extragalactic emissions, avoiding foregrounds and Galactic contributions. Methods. In our analysis, we introduce a framework to forecast the CBR constraining power when applied to ULTRASAT and GALEX in cross correlation with the five-year DESI spectroscopic survey. Results. We show that these will yield a strong improvement in the measurement of the UV-EBL volume emissivity. For λ = 1500 Å non-ionizing continuum below z ∼ 2, we forecast a 1σ uncertainty ≲26% (9%) with conservative (optimistic) bias priors using the ULTRASAT full-sky map. Similar constraints can be obtained from its low-cadence survey, which will provide a smaller but deeper map. Finally, we discuss how these results will foster our understanding of UV-EBL models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it