Euclid preparation. The impact of nonparametric star-formation histories on spatially resolved galaxy property estimation using synthetic Euclid images

Nonparametric star-formation histories (SFHs) offer a significant advantage for modeling galaxy spectral energy distributions (SEDs) by providing the flexibility to represent the rich diversity of SFH shapes observed in galaxies. Unlike parametric models, which inherently exclude many plausible SFH forms, nonparametric approaches can accommodate the full range of possible evolutionary histories. However, this flexibility can lead to increased degeneracies and model uncertainties, even when using high-quality observational data, highlighting the need for well-motivated and physically informed priors. To assess the feasibility of reconstructing accurate SFHs from Euclid-like data, we use the SED-fitting code Prospector to model both spatially resolved and global SFHs using parametric and nonparametric configurations. This work provides a proof of concept for extracting spatially resolved SFHs of local galaxies with Euclid, highlighting the strengths and limitations of nonparametric SFH modeling in the context of next-generation galaxy surveys. We apply these methods to mock ultraviolet–near-infrared photometry derived from the TNG50 cosmological simulation and processed with the radiative transfer code SKIRT. We show that nonparametric SFHs provide a more effective approach to mitigate the outshining effect by recent star formation, offering improved accuracy in the determination of galaxy stellar properties. Also, we find that the nonparametric SFH model at resolved scales closely recovers the stellar mass formation times (within 0.1 dex) and the ground truth values from TNG50, with absolute average bias of 0.03 dex in stellar mass, and 0.01 dex in both sSFR and mass-weighted age. In contrast, larger offsets are estimated for all stellar properties and formation times when using a simple τ-model SFH, both at resolved and global scales, highlighting its limitations. These results emphasize the critical role of nonparametric SFHs both at global and spatially resolved analyses to fully capture the complex evolutionary pathways of galaxies and avoid biases inherent in simple parametric models.

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