Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
Leveraging continuous-time neural models to decode the stochastic behavior of distant supermassive black holes.
Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes powering them, and modeling these variations from ground-based survey data time series, known as light curves, is a statistical chall...
ArXiv CS.LG
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This research bridges the gap between deep learning and astrophysics by applying continuous-time reinforcement learning to the complex, stochastic nature of quasar light curves. By modeling long-range temporal dependencies in astronomical data, this approach enables more precise analysis of black hole dynamics across irregular observational timelines.
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