The PAU Survey: Galaxy stellar population properties estimates with narrowband data
Bibliographic record
Abstract
A newfound interest has been seen in narrowband galaxy surveys as a promising method for achieving the necessary accuracy on the photometric redshift estimate of individual galaxies for next-generation stage IV cosmological surveys. One key advantage is the ability to provide higher spectral resolution information on galaxies, which ought to allow for a more accurate and precise estimation of the stellar population properties for galaxies. However, the impact of adding narrowband photometry on the stellar population properties estimate is largely unexplored. The scope of this work is two-fold: 1) we leverage the predictive power of broadband and narrowband data to infer galaxy physical properties, such as stellar masses, ages, star formation rates, and metallicities; and 2) we evaluate the improvement of performance in estimating galaxy properties when we use narrowband instead of broadband data. In this work, we measured the stellar population properties of a sample of galaxies in the COSMOS field for which both narrowband and broadband data are available. In particular, we employed narrowband data from the Physics of the Accelerating Universe Survey (PAUS) and broadband data from the Canada France Hawaii Telescope legacy survey (CFHTLS). We used two different spectral energy distribution (SED) fitting codes to measure galaxy properties, namely, CIGALE and PROSPECTOR. We find that the increased spectral resolution of narrowband photom try does not yield a substantial improvement in terms of constraining the galaxy properties using the SED fitting. Nonetheless, we find that we are able to obtain a more diverse distribution of metallicities and dust optical depths with CIGALE when employing the narrowband data. The effect is not as prominent as expected, which we relate to the low narrowband signal-to-noise ratio (S/N) of a majority of the sampled galaxies, the respective drawbacks of both codes, and the restriction of coverage to the optical regime. The measured properties are compared to those reported in the COSMOS2020 catalogue, showing a good agreement. We have released the catalogue of measured properties in tandem with this work.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".