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hundreds to thousands of stars can be formed in aggregates from one single molecular clouds ( shu et al . , meyer et al .
@xcite , lada et al . ) .
if we assume that the progenitor cloud was chemically well - mixed ( feng et al .
@xcite ) , then we expect to observe homogeneous chemical composition in the stars formed from this cloud as already confirmed by de silva et al .
( @xcite , @xcite and @xcite ) .
since each molecular cloud has its own history of pollution by ejecta from core - collapse supernovae ( i.e. type ii , ib and ic supernovae where most of the @xmath0-element are produced ) , type ia supernovae ( sne ia where most iron peak elements are created ) and asymptotic giant branch stars ( agb where a s - process takes place ) , we expect different open clusters to have different chemical patterns . with this information
, we could use the method of chemical tagging to track individual stars back to their common formation sites as proposed by freeman & bland - hawthorn ( ) .
the majority of the stars formed in clusters in our galaxy have already been dispersed into the field , but a few of them managed to stay gravitationally bound . old and intermediate - age open clusters ( age @xmath1100 myr ) represent a fantastic laboratory to investigate if we can distinguish between stars born from different molecular clouds .    in this study , we collected high - resolution spectra of open clusters stars observed by different instruments , we implemented a completely automatic process to derive atmospheric parameters and chemical abundances , and we used machine learning algorithms to try to recover the original clusters from the homogeneously derived chemical abundances .
we compiled more than 400 high - resolution spectra of which 20% come from the narval instrument ( aurire @xcite ) , 40% from harps ( mayor et al .
@xcite ) and 40% from uves
( dekker et al .
we mainly looked for clusters discussed on paunzen et al .
( ) and heiter et al . ( ) , although we did not strictly limit the selection to these . to match the setup in the gaia eso survey ( gilmore et al .
@xcite ) , we chose to limit the spectral analysis to the range between 480 and 680  nm and we considered only spectra with a resolution of at least 47,000 .
an automatic computational process was developed to derive atmospheric parameters and chemical abundances .
the process is based on the integrated spectroscopic framework named ispec ( blanco - cuaresma et al . ) and it was calibrated using the library of gaia fgk benchmark stars ( blanco - cuaresma et al . ) .
the atomic data was kindly provided by the ges line - list sub - working group prior to publication ( heiter et al .
, in prep . ) .
the line - list covers our wavelength range of interest and it also provides a selection of high quality lines ( based on the reliability of the oscillator strength and the blend level ) , which are ideal for the determination of chemical abundances . as model atmosphere we used marcs ( gustafsson et al . ) with the solar abundances from grevesse et al .
( @xcite ) .    after filtering non cluster members ( validated by radial velocity ) , chemically peculiar and non - fgk stars , we were left with a dataset of more than 200 stars covering 32 open clusters with abundances for 17 species corresponding to 14 different elements ( fig .
[ fig : abundances ] ) .
to evaluate if we can distinguish stars born from different molecular clouds , we designed a blind experiment where we try to recover the original clusters using only the stellar chemical abundances .
the stars were separated per evolutionary stage ( i.e. dwarfs and giants ) to reduce the impact of nlte effects and the physical atomic diffusion process .
we compressed the 17 abundances into two dimensions using the principal components analysis ( pca ) , and we executed a k - means clustering algorithm in order to form groups of stars chemically similar ( fig .
[ fig : pca ] ) . even if the conditions of the experiment were extremely favorable ( i.e. separation per evolutionary stage , filtered chemically peculiar stars ) , we were not able to recover the original clusters because of their high degree of overlapping in the chemical space .
given the set of elements used in this study , we found that some open clusters have very similar chemical signature ( such as the clusters shown in fig .
[ fig : abundances ] ) .
our blind experiment was not able to successful recover the original clusters using only the chemical abundances .
nevertheless , there are elements that seem to have a better discriminatory power such as the heavy n - capture element ba . for future analysis
, it would be interesting to include other elements such as la , nd and eu that are formed through similar processes that produce ba ( slow and rapid n - capture processes in low mass agb , brusso et al .
@xcite ; and core - collapse supernovae , kratz et al .
@xcite ) .
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past studies have already shown that stars in open clusters are chemically homogeneous ( e.g. de silva et al . 2006 , 2007 and 2009 ) . 
 these results support the idea that stars born from the same giant molecular cloud should have the same chemical composition . in this context 
 , the chemical tagging technique was proposed by freeman & bland - hawthorn 2002 . 
 the principle is to recover disrupted stellar clusters by looking only to the stellar chemical composition . in order to evaluate the feasibility of this approach , 
 it is necessary to test if we can distinguish between stars born from different molecular clouds . for this purpose 
 , we studied the chemical composition of stars in 32 old and intermediate - age open clusters , and we applied machine learning algorithms to recover the original cluster by only considering the chemical signatures .