  The interpretation of Large Hadron Collider (LHC) data in the framework of
Beyond the Standard Model (BSM) theories is hampered by the need to run
computationally expensive event generators and detector simulators. Performing
statistically convergent scans of high-dimensional BSM theories is consequently
challenging, and in practice unfeasible for very high-dimensional BSM theories.
We present here a new machine learning method that accelerates the
interpretation of LHC data, by learning the relationship between BSM theory
parameters and data. As a proof-of-concept, we demonstrate that this technique
accurately predicts natural SUSY signal events in two signal regions at the
High Luminosity LHC, up to four orders of magnitude faster than standard
techniques. The new approach makes it possible to rapidly and accurately
reconstruct the theory parameters of complex BSM theories, should an excess in
the data be discovered at the LHC.
