GALSBI-SPS: A stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications

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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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-11-01
DOI
https://doi.org/10.1051/0004-6361/202555759
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
0.00

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article

GALSBI-SPS: A stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications

D. Gruen, Alexandre Réfrégier, Silvan Fischbacher, Tomasz Kacprzak et al.
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
article

GALSBI-SPS: A stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications

D. Gruen, Alexandre Réfrégier, Silvan Fischbacher, Tomasz Kacprzak, Sabine Bellstedt, Luca Tortorelli, A. S. G. Robotham
article en

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.

Astronomy and AstrophysicsVol. 703
The University of Western Australia (AU), Paul Scherrer Institute (CH), ETH Zurich (CH), Excellence Cluster Origins (DE), Institute of Particle Physics (CA), Swiss Data Science Center (CH), International Centre for Radio Astronomy Research (AU), Ludwig-Maximilians-Universität München (DE)
National Science Foundation, Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Science and Technology Facilities Council
Openalex Percentile: Top 75%
Galaxies: Formation, Evolution, Phenomena
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