(California Institute of Technology (US)), Zoltan Gecse
(Fermi National Accelerator Lab. (US))
Learning New Physics from a Machine1h
I will discuss how to use neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The algorithm that I will describe returns a global p-value that quantifies the tension between the data and the reference model. It also allows to compare directly what the network has learned with the data, giving a fully transparent account of the nature of possible signals. The potential applications are broad, from LHC physics searches to cosmology and beyond.
Raffaele Tito Dagnolo
(Univ. of California San Diego (US))