SHAPE

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 do not have the capacity to reconcile differences between photometric systems. Ideally, we would like to optimally rely on high-quality observational data (e.g., from JWST) for calibrating and improving upcoming wide-field surveys, such as the Chinese Space Station Survey Telescope (CSST) and Euclid. To this end, we employed the self-organizing map (SOM) method and introduced a new approach that combines a SOM with a spectral energy distribution (SED). The resulting SOM-SED Hybrid Approach for efficient Parameter Estimation (SHAPE) is able to bridge different photometric systems and efficiently estimate key galaxy parameters, such as the stellar mass (M★) 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 focused on galaxies at z ∼ 1.5 − 2.5. To mitigate discrepancies between input colors and the training set, we replaced 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 an SED library (SED Lib), we applied 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 a reduced bias (–0.01 vs. 0.18) and a smaller σNMAD (0.25 vs. 0.35). With a computational efficiency capable of processing 106 sources per CPU per hour during the estimation phase, this JWST-calibrated estimator holds significant promise for next-generation wide-field surveys.