Euclid preparation: LXXXI. The impact of nonparametric star formation histories on spatially resolved galaxy property estimation using synthetic Euclid images - Nersesian, A.; Abdurro’uf; Baes, M.; Tortora, C.; Kovačić, I.; Bisigello, L.; Corcho-Caballero, P.; Durán-Camacho, E.; Hunt, L. K.; Iglesias-Navarro, P.; Ragusa, R.; Román, J.; et al. (EDP Sciences, 2026)
Aims. We analyzed the spatially resolved and global star formation histories (SFHs) for a sample of 25 TNG50-SKIRT Atlas galaxies to assess the feasibility of reconstructing accurate SFHs from Euclid-like data. This study provides a proof of concept for extracting the spatially resolved SFHs of local galaxies with Euclid, highlighting the strengths and limitations of SFH modeling in the context of next-generation galaxy surveys.
Methods. We used the spectral energy distribution (SED) fitting code Prospector to model both spatially resolved and global SFHs using parametric and nonparametric configurations. The input consisted of mock ultraviolet near-infrared photometry derived from the TNG50 cosmological simulation and processed with the radiative transfer code SKIRT.
Results. We show that nonparametric SFHs provide a more effective approach to mitigating 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 an absolute average bias of 0.03 dex in stellar mass and 0.01 dex in both specific star formation rate and mass-weighted age. In contrast, larger offsets are estimated for all stellar properties and formation times when using a simple -model SFH, at both resolved and global scales, highlighting its limitations.
Conclusions. These results emphasize the critical role of nonparametric SFHs in both global and spatially resolved analyses, as they better capture the complex evolutionary pathways of galaxies and avoid the biases inherent in simple parametric models.