  Stellar population synthesis (SPS) models are invaluable to study star
clusters and galaxies. They provide means to extract stellar masses, stellar
ages, star formation histories, chemical enrichment and dust content of
galaxies from their integrated spectral energy distributions, colours or
spectra. As most models, they contain uncertainties which can hamper our
ability to model and interpret observed spectra. This work aims at studying a
specific source of model uncertainty: the choice of an empirical vs. a
synthetic stellar spectral library. Empirical libraries suffer from limited
coverage of parameter space, while synthetic libraries suffer from modelling
inaccuracies. Given our current inability to have both ideal stellar-parameter
coverage with ideal stellar spectra, what should one favour: better coverage of
the parameters (synthetic library) or better spectra on a star-by-star basis
(empirical library)? To study this question, we build a synthetic stellar
library mimicking the coverage of an empirical library, and SPS models with
different choices of stellar library tailored to these investigations. Through
the comparison of model predictions and the spectral fitting of a sample of
nearby galaxies, we learned that: predicted colours are more affected by the
coverage effect than the choice of a synthetic vs. empirical library; the
effects on predicted spectral indices are multiple and defy simple conclusions;
derived galaxy ages are virtually unaffected by the choice of the library, but
are underestimated when SPS models with limited parameter coverage are used;
metallicities are robust against limited HRD coverage, but are underestimated
when using synthetic libraries.
