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

Agni Bethani, Brian Cox, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Alessandra Forti, David Lack, Frederick Loebinger, Jiri Masik, Stephen Menary, Francisca Munoz Sanchez, Alexander Oh, Emily Orgill, Joleen Pater, Yvonne Peters, Andrew Pilkington, Darren Price, Yang Qin, Jacob Rawling, Nicolas ScharmbergSavanna Shaw, Stephen Watts, Terence Wyatt, The ATLAS Collaboration

    Research output: Contribution to journalArticlepeer-review

    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 several 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 fb - 1 of proton–proton collision data at a centre-of-mass energy of 13 TeV collected with the ATLAS detector at the LHC in 2015, in which more than 700 event classes and more than 10 5 regions have been analysed. No significant deviations are found and consequently no data-derived signal regions for a follow-up analysis have been defined.

    Original languageEnglish
    Article number120
    JournalThe European Physical Journal C: Particles and Fields
    Volume79
    Issue number2
    Early online date6 Feb 2019
    DOIs
    Publication statusPublished - Feb 2019

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