Process to obtain the differential Library of semi-empirical stellar spectra with varying abundance element ratio Iveth A. Gaspar-Gorostieta1,2, Alexandre Vazdekis1,2, Carlos Allende Prieto1,2, Ariane Lançon3, Adam T. Knowles1,2 1 Instituto de Astrofísica de Canarias, 2 Universidad de la Laguna, 3 Observatoire Astronomique de Strasbourg . Contact: igaspar@iac.es SEA 2022. Tenerife, Islas Canarias, España Why do we want a semi-empirical stellar library? The data Stellar libraries we are working on: X-shooter Spectral Library (XSL): Gonneau et al. (2020), Lançon et al. (2021) MILES: Sánchez-Blázquez et al. (2006) To build up the semi-empirical library, these choices are made: Stellar mixture: Caffau (C, N and O) and Lodders (all other elements) Stellar atmosphere models: MARCS (Gustafsson 2008) Kurucz (Meszaros et al. 2012) The adopted element partition shows a significant similarity with BaSTI stellar evolution models and isochrones. Conclusions New and old molecular line lists were tested and contrasted with new and old versions of synple (spectral synthesis codes, a python wrapper to the fortran code Synspec), so the best fit of each spectrum can be made. The new line lists and the new version of the code have been used to make the spectra. The parameters to compute the synthetic spectrum are are adopted from Arentsen et al. (2019) for the XSL and Garcia-Perez et al. (2021) for MILES. A number of small but key model grids with varying stellar parameteres. Some grids with different values in the parameters (effective temperature, gravity, metallicity and the different chemical abundance ratios) were made to test the interpolated spectra made by using the code FERRE, a fitting code (Allende Prieto et al. 2006). We succesfully tested our models with 8 solar-like stellar spectra from the XSL and the grid with the parameters of this subsample is changing as follows: effective temperature (5250<Teff<6000), gravity (3.5<logg<4.5), metallicity (-2<[M/H]<2), α abundance (-0.25<[α/H]<0.25). Examples: semi-empirical spectra in the visual and NIR Differential spectral responses from varying the abundance ratios in the theoretical stars will be used to correct the empirical spectra and obtain a set of semi-empirical XSL and MILES stellar libraries with varying abundances. These new libraries will be very useful to feed stellar population synthesis models predicting galaxy spectra (Vazdekis et al. 2015, 2016, Verro et al. 2022). References Allende Prieto et al., 2018, A&A, 618, A25 Knowles et al., 2021, MNRAS, 504, 2286 Gonneau et al., 2020, A&A, 634, A133 Lançon et al., 2021, A&A, 649, A97 Sánchez-Blázquez et al., 2006, MNRAS, 371, 703 Lodders et al. 2003, AJ, 591,1220 Caffau et al. 2008, Nature, 477, 67 Gustafsson et al. 2008, A&A, 951-970 Meszaros et al. 2012, AJ, 144, 120 Vazdekis et al., 2015, MNRAS, 449, 1177 Vazdekis et al., 2016, MNRAS, 463, 3409 Verro et al. 2022, A&A, 661, A50 Arentsen et al., 2019, A&A, 627, A138 García-Pérez et al., 2021, MNRAS, 505, 4496-4514 Allende Prieto et al., 2006, ApJ, 636, 804A External galaxies show differing abundance element ratios with respect to the solar neighbourhood. Such patterns are intimately linked to the Star Formation History experienced by a galaxy, as dying stars release their freshly- forged elements in characteristic time scales that depend on their masses. These scales reflect on galaxy spectra. Such responses will be applied to the real stars to create semi- empirical spectral libraries, which will feed our population synthesis models to compute stellar population spectra with the corresponding abundance ratios. Process to obtain the differential correction We aim to build up a set of semi-empirical stellar libraries from the Near-UV to the Near-IR, with varying abundance ratios of α-elements, among others. This requires computing extensive libraries of theoretical stars that will be used to derive differential spectral responses to the varying abundances (Allende Prieto et al. 2018