Predictions for detecting a turndown in the baryonic Tully–Fisher relation
Bibliographic record
Abstract
ABSTRACT The baryonic Tully–Fisher relation (bTFR) provides an empirical connection between baryonic mass and dynamical mass (measured by the maximum rotation velocity) for galaxies. Due to the impact of baryonic feedback in the shallower potential wells of dwarf galaxies, the bTFR is predicted to turn down at low masses from the extrapolated power-law relation at high masses. The low-mass end of the bTFR is poorly constrained due to small samples and difficulty in connecting the galaxy’s gas kinematics to its dark matter halo. Simulations can help us understand this connection and interpret observations. We measure the bTFR with 75 dwarf galaxies from the Marvel-ous and Marvelous Massive Dwarfs hydrodynamic simulations. Our sample has M$_\star = 10^6-10^9$ M$_\odot$, and is mostly gas dominated. We compare five velocity methods: V$_\text{out,circ}$ (spatially resolved mass-enclosed), V$_\text{out,mid}$ (spatially resolved mid-plane gravitational potential), and unresolved H i linewidths at different percentages of the peak flux (W$_\text{10}$, W$_\text{20}$, and W$_\text{50}$). We find an intrinsic turndown in the bTFR for maximum halo speeds $\lesssim 50$ km s$^{-1}$, or total baryonic mass M$_\text{bary}\lesssim 10^{8.5}$ M$_\odot$. We find that observing H i in lower-mass galaxies to the conventional surface density limit of 1 M$_\odot$ pc$^{-2}$ is not enough to detect a turndown in the bTFR; none of the H i velocity methods, spatially resolved or unresolved, recover the turndown, and we find bTFR slopes consistent with observations of higher-mass galaxies. However, we predict that the turndown can be recovered by resolved rotation curves if the H i limit is $\lesssim 0.08$ M$_\odot$ pc$^{-2}$, which is within the sensitivity of current H i surveys like FEASTS and MHONGOOSE.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".