Testing the chemical tagging technique with open clusters
  Context. Stars are born together from giant molecular clouds and, if we
assume that the priors were chemically homogeneous and well-mixed, we expect
them to share the same chemical composition. Most of the stellar aggregates are
disrupted while orbiting the Galaxy and most of the dynamic information is
lost, thus the only possibility of reconstructing the stellar formation history
is to analyze the chemical abundances that we observe today.
  Aims. The chemical tagging technique aims to recover disrupted stellar
clusters based merely on their chemical composition. We evaluate the viability
of this technique to recover co-natal stars that are no longer gravitationally
bound.
  Methods. Open clusters are co-natal aggregates that have managed to survive
together. We compiled stellar spectra from 31 old and intermediate-age open
clusters, homogeneously derived atmospheric parameters, and 17 abundance
species, and applied machine learning algorithms to group the stars based on
their chemical composition. This approach allows us to evaluate the viability
and efficiency of the chemical tagging technique.
  Results. We found that stars at different evolutionary stages have distinct
chemical patterns that may be due to NLTE effects, atomic diffusion, mixing,
and biases. When separating stars into dwarfs and giants, we observed that a
few open clusters show distinct chemical signatures while the majority show a
high degree of overlap. This limits the recovery of co-natal aggregates by
applying the chemical tagging technique. Nevertheless, there is room for
improvement if more elements are included and models are improved.
