Testing the surface brightness fluctuation method on dwarf galaxies in the COSMOS field
Bibliographic record
Abstract
ABSTRACT Dwarf galaxies are important tracers of small-scale cosmological structure, yet much of our knowledge about these systems comes from the limited sample of dwarf galaxies within the Local Group. To make a comprehensive inventory of dwarf populations in the local Universe, we require effective methods for deriving distance estimates for large numbers of faint, low surface brightness objects. Here we test the surface brightness fluctuation (SBF) method, traditionally applied to brighter early-type galaxies, on a sample of 20 nearby dwarf galaxies detected in the Cosmological Evolution Survey (COSMOS) field. These objects are partially resolved in the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) images and have confirmed redshift distances in the range 17–130 Mpc. We discuss the many model choices required in applying the SBF method and explore how these affect the final distance estimates. Amongst other variations on the method, when applying the SBF method, we alter the standard equation to include a term accounting for the power spectrum of the background, greatly improving our results. For the most robust modelling choices, we find a roughly Gaussian SBF signal that correlates linearly with distance out to distances of 50–100 Mpc, but with only a fraction of the power expected. At larger distances, there is excess power relative to that predicted, probably from undetected point sources. Overall, obtaining accurate SBF distances to faint, irregular galaxies remains challenging, but may yet prove possible with the inclusion of more information about galaxy properties and point source populations, and the use of more advanced techniques.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".