A Physically Motivated Framework to Compare Pair Fractions of Isolated Low- and High-mass Galaxies across Cosmic Time
Bibliographic record
Abstract
Abstract Low-mass galaxy pair fractions are understudied, and it is unclear whether low-mass pair fractions evolve in the same way as more massive systems over cosmic time. In the era of JWST, Roman, and Rubin, selecting galaxy pairs in a self-consistent way will be critical to connect observed pair fractions to cosmological merger rates across all mass scales and redshifts. Utilizing the Illustris TNG100 simulation, we create a sample of physically associated low-mass (10 8 < M * < 5 × 10 9 M ⊙ ) and high-mass (5 × 10 9 < M * < 10 11 M ⊙ ) pairs between z = 0 and 4.2. The low-mass pair fraction increases from z = 0 to 2.5, while the high-mass pair fraction peaks at z = 0 and is constant or slightly decreasing at z > 1. At z = 0 the low-mass major (1:4 mass ratio) pair fraction is 4× lower than high-mass pairs, consistent with findings for cosmological merger rates. We show that separation limits that vary with the mass and redshift of the system, such as scaling by the virial radius of the host halo ( r sep < 1 R vir ), are critical for recovering pair fraction differences between low-mass and high-mass systems. Alternatively, static physical separation limits applied equivalently to all galaxy pairs do not recover the differences between low- and high-mass pair fractions, even up to separations of 300 kpc. Finally, we place isolated mass analogs of Local Group galaxy pairs, i.e., Milky Way (MW)–M31, MW–LMC, LMC–SMC, in a cosmological context, showing that isolated analogs of LMC–SMC-mass pairs and low-separation (<50 kpc) MW–LMC-mass pairs are 2–3× more common at z ≳ 2–3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".