HIGHLIGHTS
SUMMARY
The authors therefore consider Vector AutoRegressive Moving Average models with eXogenous input variables (VARMAX) and compare them with models that relate the input variables directly to the losses. Available data: To construct and evaluate the predictive model of losses the observations of the losses along with other quantities measured during the LHC fills of the years 2017 and 2018 are available. At present, there is no accurate physics model for particle beam losses as a function of machine settings and control parameters. In what follows the authors make a step in this direction: the . . .
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