Closing the Gap between Observed Low-mass Galaxy H i Kinematics and Cold Dark Matter Predictions
Bibliographic record
Abstract
Abstract Testing the standard cosmological model (ΛCDM) at small scales is challenging. Galaxies that inhabit low-mass dark matter halos provide an ideal test bed for dark matter models by linking observational properties of galaxies at small scales (low mass, low velocity) to low-mass dark matter halos. However, the observed kinematics of these galaxies do not align with the kinematics of the dark matter halos predicted to host them, obscuring our understanding of the low-mass end of the galaxy–halo connection. We use deep H i observations of low-mass galaxies at high spectral resolution in combination with cosmological simulations of dwarf galaxies to better understand the connection between dwarf galaxy kinematics and low-mass halos. Specifically, we use H i line widths to directly compare to the maximum velocities in a dark matter halo and find that each deeper measurement approaches the expected one-to-one relationship between the observed kinematics and the predicted kinematics in ΛCDM. We also measure baryonic masses and place these on the baryonic Tully–Fisher relation (BTFR). Again, our deepest measurements approach the theoretical predictions for the low-mass end of this relation, a significant improvement on similar measurements based on line widths measured at 50% and 20% of the peak. Our data also hint at the rollover in the BTFR predicted by hydrodynamical simulations of ΛCDM for low-mass galaxies.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".