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Validation of Particle Physics Simulation

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Computer Simulation Validation

Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

Abstract

The procedures of validating computer simulations of particle physics events at the LHC are summarized. Because of the strongly fluctuating particle content of LHC events and detector interactions, particle-based Monte Carlo methods are an indispensable tool for data analysis. Simulation in particle physics is founded on factorization and thus its global validation can be realized by validating each individual step in the simulation. This can be accomplished by drawing on results of previous measurements, in situ studies, and models. What is particularly important in particle physics is to quantify how well a simulation is validated such that a systematic uncertainty can be assigned to a measurement. The simulation is tested for a wide range of processes and agrees with data within the assigned uncertainties.

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Notes

  1. 1.

    Using quantum mechanical relations this energy range can be interpreted as 10\(^{-18}\) m, which is about 100 million times smaller than an atom.

  2. 2.

    The number increases if the masses of the neutrinos are considered. Not all of the parameters related to neutrinos have been measured. Since they do not affect physics at the LHC, the subject of this paper, they will not be considered further.

  3. 3.

    For simplicity particles and anti-particles will just be denoted by the name of the particle.

  4. 4.

    The notation \(Z^{0*}\) means that the boson is “off shell”, i.e., its mass is different from the default 91 GeV due to quantum mechanical uncertainty.

  5. 5.

    Here the particle physicists’ notion of “model” is used, which refers to a theoretical description of a physics process that is not fully calculable from the well founded and established “theory” of the Standard Model.

  6. 6.

    For a more detailed and also historical account of detector simulation in particle physics see (Daniel Elvira 2017).

  7. 7.

    These interactions in the detector are completely distinct from those pp interactions to test the SM and find BSM signals.

  8. 8.

    This test of validation tools represents another use of simulation in particle physics, mentioned in Sect. 26.3.2.

  9. 9.

    The exact definition is

    $$\begin{aligned} \mathrm {am}_{T2} \ = \ \min _{\mathbf {q}_{Ta}+\mathbf {q}_{Tb} \ = \ E_{\mathrm {T,miss} } } \left[ max(m_{Ta},m_{Tb} )\right] \end{aligned}$$
    (26.20)

    I.e., the minimum parent mass consistent with the observed kinematic distributions assuming input masses \(m_{Ta}\) and \(,m_{Tb}\) and certain mass combinations.

  10. 10.

    Note that using this method, other backgrounds, like W+jets, show a visible discrepancy between simulation and data.

  11. 11.

    New potentials through simulations may have been opened for using machine learning techniques in data analysis.

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Acknowledgements

I am grateful to Martin King and Michael Stöltzner and anonymous referees for valuable comments. I also profited highly from discussions with colleagues from the Research Group “Epistemology of the LHC” funded by the DFG under grant FOR 2063.

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  1. Physikalisches Institut der Universität Bonn, Bonn, Germany

    Peter Mättig

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Correspondence to Peter Mättig .

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Editors and Affiliations

  1. University of Bern, Bern, Switzerland

    Claus Beisbart

  2. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Nicole J. Saam

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Mättig, P. (2019). Validation of Particle Physics Simulation. In: Beisbart, C., Saam, N. (eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-70766-2_26

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  • DOI: https://doi.org/10.1007/978-3-319-70766-2_26

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