GALSBI-SPS: A stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications
Bibliographic record
Abstract
Context. Next-generation photometric and spectroscopic galaxy surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the selection effects on galaxies and biases on measurements of their properties, which are required, above all, for accurate estimates of redshift distributions. The forward-modelling of galaxy surveys offers a powerful framework to simultaneously recover galaxy redshift distributions and characterise the observed galaxy population. Aims. We present G AL SBI-SPS, a new stellar population synthesis (SPS)-based galaxy population model developed for cosmological and galaxy evolution studies. The model generates realistic galaxy catalogues, which we use to forward-model Hyper-Suprime Cam (HSC) observations in the COSMOS field. Methods. G AL SBI-SPS samples the physical properties of galaxies from analytical parametrisations informed by GAMA, DEVILS, and literature data, it computes galaxy magnitudes with the generative SED package P RO S PECT , and it simulates HSC images in the COSMOS field with UFig. We measured photometric properties consistently in real data and simulations. We compared redshift distributions and photometric and physical properties to observations and to those from the phenomenological G AL SBI model. Results. G AL SBI-SPS reproduces the observed g , r , i , z , y magnitude, colour, and size distributions down to i ≤ 23 with good accuracy. Median differences in magnitudes and colours remain below 0.14 mag, with the model covering the full colour space spanned by HSC data. Galaxy sizes are overestimated by ∼0.2″ on average and some tension exists in the g − r colour distribution, but the latter is comparable to that seen in the phenomenological G AL SBI model. Redshift distributions show a mild positive offset (0.01 ≲ Δ¯ z ≲ 0.08) in the mean. G AL SBI-SPS qualitatively reproduces the stellar mass–star formation rate and size–stellar mass relations seen in COSMOS2020 data. Conclusions. G AL SBI-SPS provides a realistic, survey-independent description of the galaxy population at a Stage-III-like depth using only literature-based parameters. Its predictive power is expected to improve significantly when constrained against deep observed data using simulation-based inference, thereby providing accurate redshift distributions that satisfy the stringent requirements set by Stage IV surveys.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".