Missing stellar mass in SED fitting: spatially unresolved photometry can underestimate galaxy masses
Bibliographic record
Abstract
We fit model spectral energy distributions (SEDs) to each pixel in 67 nearby (〈z〉 = 0.0057) galaxies using broad-band photometry from the Sloan Digital Sky Survey and GALEX. For each galaxy, we compare the stellar mass derived by summing the mass of each pixel to that found from fitting the entire galaxy treated as an unresolved point source. We find that, while the pixel-by-pixel and unresolved masses of galaxies with low specific star formation rates (sSFRs, such as ellipticals and lenticulars) are in rough agreement, the unresolved mass estimate for star-forming galaxies is systematically lower than the measurement from spatially resolved photometry. The discrepancy is strongly correlated with sSFR, with the highest sSFRs in our sample having masses underestimated by 25 per cent (0.12 dex) when treated as point sources. We found a simple relation to statistically correct mass estimates derived from unresolved broad-band SED fitting to the resolved mass estimates: mresolved = munresolved/(−0.057log (sSFR) + 0.34), where sSFR is in units of yr−1. We study the effect of varying spatial resolution by degrading the image resolution of the largest images and find a sharp decrease in the pixel-by-pixel mass estimate at a physical scale of approximately 3 kpc, which is comparable to spiral arm widths. The effects we observe are consistent with the ‘outshining’ idea which posits that the youngest stellar populations mask more massive, older – and thus fainter – stellar populations. Although the presence of strong dust lanes can also lead to a drastic difference between resolved and unresolved mass estimates (up to 45 per cent or 0.3 dex) for any individual galaxy, we found that resolving dust does not affect mass estimates on average. The strong correlation between mass discrepancy and sSFR is thus most likely due to the outshining systematic bias.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".