Saying Hallo to M94's Stellar Halo: Investigating the Accretion History of the Largest Pseudobulge Host in the Local Universe
Bibliographic record
Abstract
Abstract It is not yet settled how the combination of secular processes and merging gives rise to the bulges and pseudobulges of galaxies. The nearby (D ∼ 4.2 Mpc) disk galaxy M94 (NGC 4736) has the largest pseudobulge in the local universe, and offers a unique opportunity for investigating the role of merging in the formation of its pseudobulge. We present a first ever look at M94's stellar halo, which we expect to contain a fossil record of M94's past mergers. Using Subaru's Hyper Suprime-Cam, we resolve and identify red giant branch (RGB) stars in M94's halo, finding two distinct populations. After correcting for completeness through artificial star tests, we can measure the radial profile of each RGB population. The metal-rich RGB stars show an unbroken exponential profile to a radius of 30 kpc that is a clear continuation of M94's outer disk. M94's metal-poor stellar halo is detectable over a wider area and clearly separates from its metal-rich disk. By integrating the halo density profile, we infer a total accreted stellar mass of ∼2.8 × 108 M ⊙, with a median metallicity of [M/H] = −1.4. This indicates that M94's most-massive past merger was with a galaxy similar to, or less massive than, the Small Magellanic Cloud. Few nearby galaxies have had such a low-mass dominant merger; therefore we suggest that M94's pseudobulge was not significantly impacted by merging.
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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 | 0.000 | 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 | 0.001 | 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".