HSC-CLAUDS survey: The star formation rate functions since <i>z</i> ∼ 2 and comparison with hydrodynamical simulations
Bibliographic record
Abstract
Context. Star formation rate functions (SFRFs) give an instantaneous view of the distribution of star formation rates (SFRs) in galaxies at different epochs. They are a complementary and more stringent test for models than the galaxy stellar mass function, which gives an integrated view of the past star formation activity. However, the exploration of SFRFs has been limited thus far due to difficulties in assessing the SFR from observed quantities and probing the SFRF over a wide range of SFRs. Aims. We overcome these limitations thanks to an original method that predicts the infrared luminosity from the rest-frame UV/optical color of a galaxy and then its SFR over a wide range of stellar masses and redshifts. We applied this technique to the deep imaging survey HSC-CLAUDS combined with near-infrared and UV photometry. We provide the first SFR functions with reliable measurements in the high- and low-SFR regimes up to z = 2 and compare our results with previous observations and four state-of-the-art hydrodynamical simulations. Methods. The SFR estimates are based on the calibration of the infrared excess (IRX = LIR/LUV) in the NUVrK color-color diagram. We improved upon the original calibration in the COSMOS field by incorporating Herschel photometry, which allowed us to extend the analysis to higher redshifts and to galaxies with lower stellar masses using stacking techniques. Our NrK method leads to an accuracy of individual SFR estimates of σ ∼ 0.25 dex. We show that it reproduces the evolution of the main sequence up to z = 2 and the behavior of the attenuation (or ⟨IRX⟩) with stellar mass. In addition to the known lack of evolution of this relation up to z = 2 for galaxies with M⋆ ≤ 1010.3 M⊙, we observe a plateau in ⟨IRX⟩ at higher stellar masses that depends on redshift. Results. We measure the SFR functions and cosmic SFR density up to z = 2 for a mass-selected star-forming galaxy sample (with a mass limit of M⋆ ≥ 2.109 M⊙ at z = 2). The SFR functions cover a wide range of SFRs (0.01 ≤ SFR ≤ 1000 M⊙ yr−1), providing good constraints on their shapes. They are well fitted by a Schechter function after accounting for the Eddington bias. The high-SFR tails match the far-infrared observations well, and show a strong redshift evolution of the Schechter parameter, SFR⋆, as log10(SFR⋆) = 5.8z + 0.76. The slope of the SFR functions, α, shows almost no evolution up to z = 1.5 − 2 with α = −1.3 ± 0.1. We compare the SFR functions with predictions from four state-of-the-art hydrodynamical simulations. Significant differences are observed between them, and none of the simulations are able to reproduce the observed SFRFs over the whole redshift and SFR range. We find that only one simulation is able to predict the fraction of highly star-forming galaxies at high z, 1 ≤ z ≤ 2. This highlights the benefits of using SFRFs as a constraint that can be reproduced by simulations; however, despite efforts to incorporate more physically motivated prescriptions for star-formation and feedback processes, its use remains challenging.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".