Joel Leja presents new machine-learning-accelerated methods for inferring the physical properties of galaxies from deep and wide surveys, including neural net emulators, GPU-accelerated sampling, and simulation-based inference, achieving speed-ups of 100x to 1,000,000x. The talk takes place online and will be recorded.
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