A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
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Abstract
This paper describes a strategy for a general search used by the ATLAS
Collaboration to find potential indications of new physics. Events are
classified according to their final state into many event classes. For each
event class an automated search algorithm tests whether the data are compatible
with the Monte Carlo simulated expectation in various distributions sensitive
to the effects of new physics. The significance of a deviation is quantified
using pseudo-experiments. A data selection with a significant deviation defines
a signal region for a dedicated follow-up analysis with an improved background
expectation. The analysis of the data-derived signal regions on a new dataset
allows a statistical interpretation without the large look-elsewhere effect.
The sensitivity of the approach is discussed using Standard Model processes and
benchmark signals of new physics. As an example, results are shown for 3.2
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1434-6052