Speaker : Andreas Tersenov (PhD Student)
Date : 21st of October
Location: Orphanoudakis meeting room
Paper Abstract:
In inference problems, we often have domain knowledge which allows us to define summary statistics that capture most of the information content in a dataset. In this paper, we present a hybrid approach, where such physics-based summaries are augmented by a set of compressed neural summary statistics that are optimized to extract the extra information that is not captured by the predefined summaries. The resulting statistics are very powerful inputs to simulation-based or implicit inference of model parameters. We apply this generalization of Information Maximizing Neural Networks (IMNNs) to parameter constraints from tomographic weak gravitational lensing convergence maps to find summary statistics that are explicitly optimized to complement angular power spectrum estimates. We study several dark matter simulation resolutions in low- and high-noise regimes. We show that (i) the information-update formalism extracts at least 3 times and up to 8 times as much information as the angular power spectrum in all noise regimes, (ii) the network summaries are highly complementary to existing 2-point summaries, and (iii) our formalism allows for networks with smaller, physically informed architectures to match much larger regression networks with far fewer simulations needed to obtain asymptotically optimal inference..
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