Classification algorithms applied to structure formation simulations
Throughout cosmological simulations, the properties of the matter density field in the initial conditions have a decisive impact on the features of the structures formed today. In this paper we use a random-forest classification algorithm to infer whether or not dark matter particles, traced back to the initial conditions, would end up in dark matter halos whose masses are above some threshold. This problem might be posed as a binary classification task, where the initial conditions of the matter density field are mapped into classification labels provided by a halo finder program. Our results show that random forests are effective tools to predict the output of cosmological simulations without running the full process. These techniques might be used in the future to decrease the computational time and to explore more efficiently the effect of different dark matter/dark energy candidates on the formation of cosmological structures.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationClassificationSimilar Papers 제목 키워드 기반
Weakly Supervised Classification in High Energy Physics
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classi…
BIG-bench Machine LearningBinary ClassificationClassificationGeneral Classification+2Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model
In this work we propose Support Vector Machine classification algorithms to classify onedimensional crystal lattice waves from locally sampled data. Different learning datasets of particle displacements, momenta and ener…
ClassificationDimensionality ReductionSupervised Learning in Multilayer Spiking Neural Networks
The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neuro…
Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties
Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials scien…
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines
Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to elimi…
BenchmarkingClassificationClassification Of Hyperspectral Imagesfeature selection+2