Great Optically Luminous Dropout Research Using Subaru HSC (GOLDRUSH). I. UV luminosity functions at <i>z</i> ∼ 4–7 derived with the half-million dropouts on the 100 deg2 sky
Bibliographic record
Abstract
Abstract We study the UV luminosity functions (LFs) at z ∼ 4, 5, 6, and 7 based on the deep large-area optical images taken by the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP). On the 100 deg2 sky of the HSC SSP data available to date, we take enormous samples consisting of a total of 579565 dropout candidates at z ∼ 4–7 by the standard color selection technique, 358 out of which are spectroscopically confirmed by our follow-up spectroscopy and other studies. We obtain UV LFs at z ∼ 4–7 that span a very wide UV luminosity range of ∼0.002–100$\, L_{\rm UV}^\ast$ (−26 &lt; MUV &lt; −14 mag) by combining LFs from our program and the ultra-deep Hubble Space Telescope legacy surveys. We derive three parameters of the best-fit Schechter function, ϕ*, $M_{\rm UV}^{\,\ast}$, and α, of the UV LFs in the magnitude range where the active galactic nucleus (AGN) contribution is negligible, and find that α and ϕ* decrease from z ∼ 4 to 7 with no significant evolution of $M_{\rm UV}^{\,\ast}$. Because our HSC SSP data bridge the LFs of galaxies and AGNs with great statistical accuracy, we carefully investigate the bright end of the galaxy UV LFs that are estimated by the subtraction of the AGN contribution either aided by spectroscopy or the best-fit AGN UV LFs. We find that the bright end of the galaxy UV LFs cannot be explained by the Schechter function fits at &gt;2 σ significance, and require either double power-law functions or modified Schechter functions that consider a magnification bias due to gravitational lensing.
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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 | 0.001 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 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".