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

Probabilistic cosmic web classification using fast-generated training data