Deep Extragalactic VIsible Legacy Survey (DEVILS): New robust merger rates at intermediate redshifts
Bibliographic record
Abstract
Mergers are fundamental to our understanding of the processes driving the evolution of the structure and morphology of galaxies, star formation, AGN activity, and the redistribution of stellar mass in the Universe. Determining the fraction and properties of mergers across cosmic time is critical to understanding the formation of the Universe we observe today. This fraction and its evolution also provide inputs and constraints for cosmological simulations, crucial for theoretical models of galaxy evolution. We present robust estimates of major close-pair fractions and merger rates at $0.2 < z < 0.9$ in the Deep Extragalactic VIsible Legacy Survey (DEVILS). We identify major mergers by selecting close-pairs with a projected spatial separation $r_{\mathrm{sep}} < 20$ h$^{-1}$ kpc and a radial velocity separation $v_{\mathrm{sep}} < 500$ km s$^{-1}$. For galaxies with stellar masses of log$_{10}$($M_\star$/$M_\odot$) = 10.66 $\pm$ 0.25 dex, we find a major close-pair fraction of $\approx 0.021$ at $0.2 < z < 0.34$ using a highly complete, unbiased spectroscopic sample. We extend these estimates to $0.2 < z < 0.9$ by combining the full probability distribution of redshifts for galaxies with high-quality spectroscopic, photometric, or grism measurements. Fitting a power-law $γ_{m} = A(1 + z)^m$, we find $A = 0.024 \pm 0.001$ and $m = 0.55 \pm 0.22$. Consistent with previous results, the shallow slope suggests weak redshift evolution in the merger fraction. When comparing with large hydrodynamical simulations, we also find consistent results. We convert close-pair fractions to merger rates using several literature prescriptions for merger timescales and provide all measurements for future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".