SHAPE. I. A SOM-SED hybrid approach for efficient galaxy parameter estimation leveraging JWST
arXiv:2510.00187 · doi:10.1051/0004-6361/202555894
Abstract
With the launch and application of next-generation ground- and space-based telescopes, astronomy has entered the era of big data, necessitating more efficient and robust data analysis methods. Most traditional parameter estimation methods are unable to reconcile differences between photometric systems. Ideally, we would like to optimally rely on high-quality observation data provided by, e.g., JWST, for calibrating and improving upcoming wide-field surveys such as the China Space Station Telescope (CSST) and Euclid. To this end, we introduce a new approach (SHAPE, SOM-SED Hybrid Approach for efficient Parameter Estimation) that can bridge different photometric systems and efficiently estimate key galaxy parameters, such as stellar mass () and star formation rate (SFR), leveraging data from a large and deep JWST/NIRCam and MIRI survey (PRIMER). As a test of the methodology, we focus on galaxies at . To mitigate discrepancies between input colors and the training set, we replace the default SOM weights with stacked SEDs from each cell, extending the applicability of our model to other photometric catalogs (e.g., COSMOS2020). By incorporating a SED library (SED Lib), we apply this JWST-calibrated model to the COSMOS2020 catalog. Despite the limited sample size and potential template-related uncertainties, SOM-derived parameters exhibit a good agreement with results from SED-fitting using extended photometry. Under identical photometric constraints from CSST and Euclid bands, our method outperforms traditional SED-fitting techniques in SFR estimation, exhibiting both a reduced bias (-0.01 vs. 0.18) and a smaller (0.25 vs. 0.35). With its computational efficiency capable of processing sources per CPU per hour during the estimation phase, this JWST-calibrated estimator holds significant promise for next-generation wide-field surveys.
15 pages, 9 figures. Submitted to A&A. Comments are welcome!
References in corpus (35)
- Stellar population synthesis at the resolution of 2003
- Cosmic Star Formation History
- Star Formation in the Milky Way and Nearby Galaxies
- EAZY: A Fast, Public Photometric Redshift Code
- Accurate photometric redshifts for the CFHT Legacy Survey calibrated using the VIMOS VLT Deep Survey
- Physical Models of Galaxy Formation in a Cosmological Framework
- CIGALE: a python Code Investigating GALaxy Emission
- A simple model to interpret the ultraviolet, optical and infrared emission from galaxies
- The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies
- Modeling the Panchromatic Spectral Energy Distributions of Galaxies
- Inferring the star-formation histories of massive quiescent galaxies with BAGPIPES: Evidence for multiple quenching mechanisms
- Galaxy Structure and Mode of Star Formation in the SFR-Mass Plane from z ~ 2.5 to z ~ 0.1
- Stellar Population Inference with Prospector
- Dust-Corrected Star Formation Rates of Galaxies. II. Combinations of Ultraviolet and Infrared Tracers
- Euclid. I. Overview of the Euclid mission
- COSMOS2020: A panchromatic view of the Universe to from two complementary catalogs
- Nebular Continuum and Line Emission in Stellar Population Synthesis Models
- Data Mining and Machine Learning in Astronomy
- Star formation and dust obscuration at z~2: galaxies at the dawn of downsizing
- Measuring the Redshift Evolution of Clustering: the Hubble Deep Field South
- Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
- Euclid. III. The NISP Instrument
- Euclid. II. The VIS Instrument
- Determining the stellar masses of submillimetre galaxies: the critical importance of star formation histories
- A large accretion disk of extreme eccentricity in the TDE ASASSN-14li
- Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
- Photometric Redshift Calibration Requirements for WFIRST Weak Lensing Cosmology: Predictions from CANDELS
- Horizon-AGN virtual observatory -- 2: Template-free estimates of galaxy properties from colours
- Measures of star formation rates from Infrared (Herschel) and UV (GALEX) emissions of galaxies in the HerMES fields
- COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys
- Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
- Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
- COSMOS-Web: A history of galaxy migrations over the stellar mass-star formation rate plane
- Getting ready for the LSST data -- estimating the physical properties of main sequence galaxies
- Probabilistic and progressive deblended far-infrared and sub-millimetre point source catalogues I. Methodology and first application in the COSMOS field