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We propose a novel method for gradient-based optimisation of black-box simulators using local surrogate models (https://arxiv.org/abs/2002.04632). In domains such as HEP, many processes are modeled with non-differentiable simulators (such as GEANT4). However, often one wants to optimise some parameters of the detector or other apparatus relying on the knowledge from the simulator. To address such cases, we utilise deep generative models to approximate a simulator in the local neighbourhood and perform optimisation. In cases when the optimised parameter space is constrained to a low dimension sub-space, we observe that our method outperforms Bayesian optimisation, numerical optimisation, and REINFORCE-based approaches.
“All the Lenses”: Toward Large-Scale Hierarchical Inference of the Hubble Constant Using Bayesian Deep Learning
Date: October 29, 2021 1:00 pm Pacific
Speaker: Ji Won Park (Stanford)
Precise constraints on the Hubble constant (H0) can shed light on the nature of dark matter and dark energy, arguably the biggest mysteries of modern cosmology. An astrophysical phenomenon known as strong gravitational lensing enables direct measurements of H0. Seven strong gravitational lenses have been “hand-analyzed” over the last ten years, but next-generation telescope surveys will increase the sample size to tens of thousands of lenses, creating a demand for novel methods that can model large volumes of noisy data. I demonstrate the use of Bayesian neural networks (BNNs) in rapidly extracting cosmological information from the image, catalog, and time series data associated with these lenses. Quantifying various sources of uncertainty is key to minimizing systematic bias on H0. Being both accurate and efficient, the BNN pipeline is a promising tool that can combine information from all the lenses -- with varying types and signal-to-noise ratios -- into a large-scale hierarchical Bayesian model.
The Learnt Geometry of Collider Events
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