Speaker
Description
Excursion is a tool to efficiently estimate level sets of
computationally expensive black box functions using Active Learning.
Excursion uses a Gaussian Process Regression as a surrogate model for
the black box function. It queries the target function (black box) iteratively in order to increase the available information regarding the desired level sets. We implement Excursion using GPyTorch which provides
state-of-the-art fast posterior fitting techniques and takes advantage
of GPUs to scale computations to higher dimensions.
In this talk, we demonstrate that Excursion significantly outperforms
traditional grid search approaches and we will detail the current work
in progress on improving Exotics searches as an intermediate step towards the ATLAS Run 2 pMSSM scan on $pp$ collisions at $\sqrt{s}=$ 13 TeV with the ATLAS detector.
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