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Workshop: 2nd ICML Workshop on Machine Learning for Astrophysics

Field-Level Inference with Microcanonical Langevin Monte Carlo

Adrian Bayer · Uros Seljak · Chirag Modi

Abstract: Field-level inference provides a means to optimally extract information from upcoming cosmological surveys, but requires efficient sampling of a high-dimensional parameter space.This work applies Microcanonical Langevin Monte Carlo (MCLMC) to sample the initial conditions of the Universe, as well as the cosmological parameters $\sigma_8$ and $\Omega_m$, from simulations of cosmic structure.MCLMC is shown to be over an order of magnitude more efficient than traditional Hamiltonian Monte Carlo (HMC) for a $\sim 2.6 \times 10^5$ dimensional problem. Moreover, the efficiency of MCLMC compared to HMC greatly increases as the dimensionality increases, suggesting gains of many orders of magnitude for the dimensionalities required by upcoming cosmological surveys.

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