A Machine Learning Approach to Galaxy-LSS Classification I: Imprints on
  Halo Merger Trees
  The cosmic web plays a major role in the formation and evolution of galaxies
and defines, to a large extent, their properties. However, the relation between
galaxies and environment is still not well understood. Here we present a
machine learning approach to study imprints of environmental effects on the
mass assembly of haloes. We present a galaxy-LSS machine learning classifier
based on galaxy properties sensitive to the environment. We then use the
classifier to assess the relevance of each property. Correlations between
galaxy properties and their cosmic environment can be used to predict galaxy
membership to void/wall or filament/cluster with an accuracy of $93\%$. Our
study unveils environmental information encoded in properties of haloes not
normally considered directly dependent on the cosmic environment such as merger
history and complexity. Understanding the physical mechanism by which the
cosmic web is imprinted in a halo can lead to significant improvements in
galaxy formation models. This is accomplished by extracting features from
galaxy properties and merger trees, computing feature scores for each feature
and then applying support vector machine to different feature sets. To this
end, we have discovered that the shape and depth of the merger tree, formation
time and density of the galaxy are strongly associated with the cosmic
environment. We describe a significant improvement in the original
classification algorithm by performing LU decomposition of the distance matrix
computed by the feature vectors and then using the output of the decomposition
as input vectors for support vector machine.
