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

ATLAS Collaboration

研究成果: Article

2 引用 (Scopus)

抄録

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.

元の言語English
記事番号120
ジャーナルEuropean Physical Journal C
79
発行部数2
DOI
出版物ステータスPublished - 2019 2 1

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Physics
deviation
physics
Experiments
Detectors
center of mass
indication
collisions
sensitivity
detectors
energy

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Physics and Astronomy (miscellaneous)

これを引用

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title = "A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment",
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.",
author = "{ATLAS Collaboration} and M. Aaboud and G. Aad and B. Abbott and O. Abdinov and B. Abeloos and Abidi, {S. H.} and AbouZeid, {O. S.} and Abraham, {N. L.} and H. Abramowicz and H. Abreu and Y. Abulaiti and Acharya, {B. S.} and S. Adachi and L. Adamczyk and J. Adelman and M. Adersberger and T. Adye and Affolder, {A. A.} and Y. Afik and C. Agheorghiesei and Aguilar-Saavedra, {J. A.} and F. Ahmadov and G. Aielli and S. Akatsuka and {\AA}kesson, {T. P.A.} and E. Akilli and Akimov, {A. V.} and Alberghi, {G. L.} and J. Albert and P. Albicocco and Alconada Verzini, {M. J.} and S. Alderweireldt and M. Aleksa and Aleksandrov, {I. N.} and C. Alexa and G. Alexander and T. Alexopoulos and M. Alhroob and B. Ali and G. Alimonti and J. Alison and Alkire, {S. P.} and C. Allaire and Allbrooke, {B. M.M.} and Allen, {B. W.} and Allport, {P. P.} and A. Aloisio and A. Alonso and Takashi Mitani and Masahiro Morinaga",
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AU - ATLAS Collaboration

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AU - Aad, G.

AU - Abbott, B.

AU - Abdinov, O.

AU - Abeloos, B.

AU - Abidi, S. H.

AU - AbouZeid, O. S.

AU - Abraham, N. L.

AU - Abramowicz, H.

AU - Abreu, H.

AU - Abulaiti, Y.

AU - Acharya, B. S.

AU - Adachi, S.

AU - Adamczyk, L.

AU - Adelman, J.

AU - Adersberger, M.

AU - Adye, T.

AU - Affolder, A. A.

AU - Afik, Y.

AU - Agheorghiesei, C.

AU - Aguilar-Saavedra, J. A.

AU - Ahmadov, F.

AU - Aielli, G.

AU - Akatsuka, S.

AU - Åkesson, T. P.A.

AU - Akilli, E.

AU - Akimov, A. V.

AU - Alberghi, G. L.

AU - Albert, J.

AU - Albicocco, P.

AU - Alconada Verzini, M. J.

AU - Alderweireldt, S.

AU - Aleksa, M.

AU - Aleksandrov, I. N.

AU - Alexa, C.

AU - Alexander, G.

AU - Alexopoulos, T.

AU - Alhroob, M.

AU - Ali, B.

AU - Alimonti, G.

AU - Alison, J.

AU - Alkire, S. P.

AU - Allaire, C.

AU - Allbrooke, B. M.M.

AU - Allen, B. W.

AU - Allport, P. P.

AU - Aloisio, A.

AU - Alonso, A.

AU - Mitani, Takashi

AU - Morinaga, Masahiro

PY - 2019/2/1

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N2 - 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.

AB - 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.

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