EPOCHS. III. Unbiased UV Continuum Slopes at 6.5 < <i>z</i> < 13 from Combined PEARLS GTO and Public JWST/NIRCam Imaging
Bibliographic record
Abstract
Acknowledgements: We acknowledge support from the ERC Advanced Investigator Grant EPOCHS (grant No. 788113), as well as two studentships from STFC for D.A. and T.H. L.W. acknowledges funding from the Faculty of Science & Engineering at the University of Manchester. L.F. acknowledges financial support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) in the form of a PhD studentship. R.W., S.H.C., and R.A.J. acknowledge support from NASA JWST Interdisciplinary Scientist grant Nos. NAG5 12460, NNX14AN10G, and 80NSSC18K0200 from the Goddard Space Flight Center. C.C. is supported by National Natural Science Foundation of China, grant Nos. 11803044, 11933003, and 12173045. This work is sponsored (in part) by the Chinese Academy of Sciences (CAS), through a grant to the CAS South America Center for Astronomy (CASSACA). We acknowledge the science research grants from the China Manned Space Project with grant No. CMS-CSST-2021-A05. M.A.M. acknowledges the support of a National Research Council of Canada Plaskett Fellowship, and the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE17010001. M.N. acknowledges INAF-Mainstreams 1.05.01.86.20. C.N.A.W. acknowledges support from the NIRCam Science Team contract to the University of Arizona, NAS 5-02015. E.Z. acknowledges project grant No. 2022-03804 from the Swedish Research Council (Vetenskapsrådet) and has also benefited from a sabbatical at the Swedish Collegium for Advanced Study. This work is based on observations made with the NASA/ESA Hubble Space Telescope (HST) and NASA/ESA/CSA JWST obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI), which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST, and NAS 5–26555 for HST. The PEARLS observations used in this work are associated with JWST programs 1176 and 2738. In addition, public data sets from JWST programs 1180, 1210, 1895, 1963 (JADES), 1324 (GLASS), 1345 (CEERS), and 2079 (NGDEEP) are also used within the work presented. Some of the data products presented herein were retrieved from the Dawn JWST Archive (DJA). DJA is an initiative of the Cosmic Dawn Center, which is funded by the Danish National Research Foundation under grant No. 140. The authors thank all involved in the construction and operations of the telescope as well as those who designed and executed these observations; their number are too large to list here, and without each of their continued efforts such work would not be possible. The authors also thank Adam Carnall for their prompt help with Bagpipes via email, as well as helpful discussions with Rebecca Bowler, Fergus Cullen, and Albert Zijlstra, which significantly improved the discussion of results. This work is dedicated to the memory of our dedicated colleague and coauthor, Mario Nonino, who sadly passed during the completion of this work. The authors thank Anthony Holloway and Sotirios Sanidas for providing their expertise in high-performance computing and other IT support throughout this work. This work makes use of astropy (Astropy Collaboration et al. 2013, 2018, 2022), matplotlib (J. D. Hunter 2007), reproject, DrizzlePac (S. L. Hoffmann et al. 2021), SciPy (P. Virtanen et al. 2020), photutils (L. Bradley et al. 2022), and galfind. v1 of the galfind code is expected to be released to the public within the next year.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".