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Learning holographic horizons
Vishnu Jejjala, Sukrut Mondkar, Ayan Mukhopadhyay, Rishi Raj
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We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas. We show that simple features of only the time series of the pressure anisotropy, namely the values and half-widths of the maxima and minima, the times these are attained, and the times of the first zeroes can predict the areas of the apparent and event horizons in the dual bulk geometry at all times with a fixed maximum length ($10$) of the input vector. We also argue that the entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations.
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