  To accurately measure a star's atmospheric parameters and chemical
abundances, it is crucial to have high-quality spectra. Analysing the detailed
chemical abundances of groups of stars can help us better understand
nucleosynthesis, galactic chemical enrichment, and stellar evolution. In this
study, we explored whether stellar spots can affect a star's inferred
metallicity and, if so, where the impact is the strongest. To investigate this,
we created synthetic infrared spectra that included stellar spots for a sample
of main-sequence stars younger than the sun. We then applied two models to the
data: one that accounted for spots and one that did not. From this, we can
determine the bias introduced when fitting spotted spectra with a non-spotted
model and how this bias varies with different parameters. Our findings revealed
that fitting spotted spectra with a non-spotted model can introduce a scatter
of up to 0.05 dex in the inferred metallicity, especially for stars with high
levels of spot coverage. This bias is similar in magnitude to other relevant
effects, such as atomic diffusion, radiative levitation, or non-local
thermodynamic equilibrium. We also found that the effect is most pronounced in
young stars and decreases with age. These results suggest that stellar spots
can introduce a systematic uncertainty in metallicity that is not currently
accounted for in spectroscopic analysis. This could potentially limit
scientific inferences for population-level studies of young stars and
differential abundance analyses.
