Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters
  blindly recovered in the elemental abundance space   The chemical makeup of a star provides the fossil information of the
environment where it formed. Under this premise, it should be possible to use
chemical abundances to tag stars that formed within the same stellar
association. This idea - known as chemical tagging - has not produced the
expected results, especially within the thin disk where open stellar clusters
have chemical patterns that are difficult to disentangle. The ultimate goal of
this study is to probe the feasibility of chemical tagging within the thin disk
population using high-quality data from a controlled sample of stars. We also
aim at improving the existing techniques of chemical tagging and giving
guidance on different strategies of clustering analysis in the elemental
abundance space. Here we develop the first blind search of open clusters'
members through clustering analysis in the elemental abundance space using the
OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate
different strategies of analysis, determining which ones are more performing.
Second, we apply these methods to a data set including both field stars and
open clusters attempting a blind recover of as many open clusters as possible.
We show how specific strategies of data analysis can improve the final results.
Specifically, we demonstrate that open clusters can be more efficaciously
recovered with the Manhattan metric and on a space whose dimensions are
carefully selected. Using these (and other) prescriptions we are able to
recover open clusters hidden in our data set and find new members of these
stellar associations. Our results indicate that there are chances of recovering
open clusters' members via clustering analysis in the elemental abundance
space. Presumably, the performances of chemical tagging will further increase
with higher quality data and more sophisticated clustering algorithms.

5
The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
16
The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn