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Low-dimensional representations of high-dimensional data can reveal underlying structures and patterns that are not apparent in the original space. By projecting the data to a low-dimensional space, we construct a more compact and informative representation, making it easier to visualize and interpret the data. There are many ways to obtain low-dimensional representations, ranging from calculating simple summary statistics to constructing latent representations using sophisticated dimensionality reduction methods. In this talk, I will demonstrate the power of low-dimensional representations in the analysis of close binary light curves, focusing on a) the detection of the Darwin instability in the light curves of contact binary stars, and b) the separability of the light curves of contact binary stars, semidetached binary stars, and dark companion binary systems, which host electromagnetically silent black holes or neutron stars. |
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