Direct detection of galaxy stellar halos: NGC 3957 as a test case
Bibliographic record
Abstract
We present a direct detection of the stellar halo of the edge-on S0 galaxy NGC 3957, using ultra-deep VLT/VIMOS V and R images. This is achieved with a sky subtraction strategy based on infrared techniques. These observations allow us to reach unprecedented high signal-to-noise ratios of up to 15 kpc away from the galaxy center, rendering photon-noise negligible. The 1σ detection limits are R = 30.6 mag/arcsec2 and V = 31.4 mag/arcsec2. We conduct a thorough analysis of the possible sources of systematic errors that could affect the data: flat-fielding, differences in CCD responses, scaling of the sky background, the extended halo itself, and PSF wings. We conclude that the V–R colour of the NGC 3957 halo, calculated between 5 and 8 kpc above the disc plane where the systematic errors are modest, is consistent with an old and preferentially metal-poor normal stellar population, like that revealed in nearby galaxy halos from studies of their resolved stellar content. We do not find support for the extremely red colours found in earlier studies of diffuse halo emission, which we suggest might have been due to residual systematic errors.
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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 | 0.000 | 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) | 0.002 | 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".