Probabilistic cosmic web classification using fast-generated training
  data
  We present a novel method of robust probabilistic cosmic web particle
classification in three dimensions using a supervised machine learning
algorithm. Training data was generated using a simplified $\Lambda$CDM toy
model with pre-determined algorithms for generating halos, filaments, and
voids. While this framework is not constrained by physical modeling, it can be
generated substantially more quickly than an N-body simulation without loss in
classification accuracy. For each particle in this dataset, measurements were
taken of the local density field magnitude and directionality. These
measurements were used to train a random forest algorithm, which was used to
assign class probabilities to each particle in a $\Lambda$CDM, dark matter-only
N-body simulation with $256^3$ particles, as well as on another toy model data
set. By comparing the trends in the ROC curves and other statistical metrics of
the classes assigned to particles in each dataset using different feature sets,
we demonstrate that the combination of measurements of the local density field
magnitude and directionality enables accurate and consistent classification of
halo, filament, and void particles in varied environments We also show that
this combination of training features ensures that the construction of our toy
model does not affect classification. The use of a fully supervised algorithm
allows greater control over the information deemed important for
classification, preventing issues arising from hyperparameters and mode
collapse in deep learning models. Due to the speed of training data generation,
our method is highly scalable, making it particularly suited for classifying
large datasets, including observed data.
