Accelerating the BSM interpretation of LHC data with machine learning
This addresses a critical problem for particle physicists by accelerating high-dimensional theory scans, though it is incremental as it builds on existing ML applications in physics.
The paper tackles the computational bottleneck in interpreting LHC data for Beyond the Standard Model theories by introducing a machine learning method that predicts signal events up to four orders of magnitude faster than standard techniques, enabling rapid parameter reconstruction.
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.