Title: "Conditional Invertible Neural Networks for Neutrinos and New Physics Searches"
In this talk I will introduce two applications of conditional Invertible Neural Networks (flows) in High Energy Physics outside of the domain of generative modelling.
The first application is v-flows, a regression task trying to recover a degree of freedom which leverages the ability of cINNs to learn a prior distribution from which we can sample. v-Flows infers the momentum of neutrinos produced in collisions without making hard assumptions, instead using our knowledge of the hypothesised process. As a proof of principle it is applied to semileptonic ttbar events.
CURTAINs on the other hand uses the invertibility of cINNs to learn a transportation between datapoints sampled from two different regions of a conditional distribution. This is useful in extrapolation of background data from the sidebands of a distribution (such as the invariant mass) into a signal region. The approach preserves the relationships between the data distribution and the conditional distribution without the need to generate new datapoints by first transforming them to, or sampling from, a simpler base distribution. CURTAINs is applied in the context of a bump hunt for new resonant physics.
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