Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and explicitly describes the corresponding sources of uncertainty. Simple illustrative examples show how these concepts can be applied in practice.
Cited by 3
Benjamin Nachman et al., Neural resampler for MonteÂ Carlo reweighting with preserved uncertainties
Phys. Rev. D 102, 076004 (2020) [Crossref]
B. M. Dillon et al., Learning the latent structure of collider events
J. High Energ. Phys. 2020, 206 (2020) [Crossref]
Gregor Kasieczka et al., Towards machine learning analytics for jet substructure
J. High Energ. Phys. 2020, 195 (2020) [Crossref]