TY  - JOUR
AU  - Begone, G.
AU  - Deisenroth, M. P.
AU  - Kim, J. S.
AU  - Liem, S.
AU  - Ruiz de Austri, R.
AU  - Welling, M.
PY  - 2019
DA  - 2019//
TI  - Accelerating the BSM interpretation of LHC data with machine learning
T2  - Phys. Dark Universe
JO  - Physics of the Dark Universe
SP  - 100293
EP  - 5pp
VL  - 24
PB  - Elsevier Science Bv
AB  - 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.
SN  - 2212-6864
UR  - https://arxiv.org/abs/1611.02704
UR  - https://doi.org/10.1016/j.dark.2019.100293
DO  - 10.1016/j.dark.2019.100293
LA  - English
N1  - WOS:000465292500018
ID  - Begone_etal2019
ER  -