Speaker
Description
Recently, conditional normalizing flows have shown promise to directly approximate the posterior distribution via amortized stochastic variational inference from raw simulation data without resorting to likelihood modelling.
In this contribution, I will discuss an open-source GitHub package, "jammy_flows", a pytorch-based project which comes with many state of the art normalizing flows out of the box and is taylor-made for this physics use case. It includes normalizing flows for different manifolds like Euclidean space, intervals, the probability simplex or spheres - the latter one being in particular important for directional distribution modelling. Joint probability distributions over multiple manifolds can be easily created via an auto-regressive structure that is taken care of internally without extra work by the user. The calculation of information geometric quantities like entropy, KL-divergence based asymmetry measures and convenience functions for coverage checks are also available. Finally, I will showcase an application of conditional NFs for neutrino event reconstruction in the IceCube detector.
