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This paper describes a strategy for a general search used by the ATLASCollaboration to find potential indications of new physics. Events areclassified according to their final state into many event classes. For eachevent class an automated search algorithm tests whether the data are compatiblewith the Monte Carlo simulated expectation in various distributions sensitiveto the effects of new physics. The significance of a deviation is quantifiedusing pseudo-experiments. A data selection with a significant deviation definesa signal region for a dedicated follow-up analysis with an improved backgroundexpectation. The analysis of the data-derived signal regions on a new datasetallows a statistical interpretation without the large look-elsewhere effect.The sensitivity of the approach is discussed using Standard Model processes andbenchmark signals of new physics. As an example, results are shown for 3.2fb$^{-1}$ of proton-proton collision data at a centre-of-mass energy of 13 TeVcollected with the ATLAS detector at the LHC in 2015, in which more than 700event classes and more than 10$^5$ regions have been analysed. No significantdeviations are found and consequently no data-derived signal regions for afollow-up analysis have been defined.

A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment