Cosmic topology. Part IVa. Classification of manifolds using machine learning: a case study with small toroidal universes
arxiv(2024)
摘要
Non-trivial spatial topology of the Universe may give rise to potentially
measurable signatures in the cosmic microwave background. We explore different
machine learning approaches to classify harmonic-space realizations of the
microwave background in the test case of Euclidean E_1 topology (the 3-torus)
with a cubic fundamental domain of a size scale significantly smaller than the
diameter of the last scattering surface. Different machine learning approaches
are capable of classifying the harmonic-space realizations with accuracy
greater than 99
last-scattering surface and orientation of the topology is known. For
distinguishing random rotations of these sky realizations from realizations of
the covering space, the extreme gradient boosting classifier algorithm performs
best with an accuracy of 88
obtained with the random forest classifier along with one- and two-dimensional
convolutional neural networks. The techniques presented here can also
accurately classify non-rotated cubic E_1 topology realizations with a
topology scale slightly larger than the diameter of the last-scattering
surface, if provided enough training data. This work identifies the prospects
and the main challenges for developing machine learning techniques that are
capable of accurately classifying observationally viable topologies.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要