Abstract
Large scale experiments at CERN’s Large Hadron Collider (LHC) rely heavily on computer simulations (CSs), a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: (a) with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. (b) In order to perform the epistemic function Winsberg (in Magnani L, Nersessian N, Thagard P (eds) Model-based reasoning in scientific discovery. Kluwer Academic/Plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: (i) holistic validation (also Lenhard and Winsberg in Stud Hist Philos Sci Part B Stud Hist Philos Modern Phys 41(3):253–262, 2010; in Carrier M, Nordmann A (eds) Science in the context of application. Springer, Berlin, pp 115–130, 2011), and (ii) model coherence. As it turns out, (c) the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline.
Highlights
The implementation of any computer simulations (CSs) obviously requires appropriate modeling (e.g. Humphreys 2004; Morrison 2009; Winsberg 2010)
For instance: How do they impact experimenters’ inferential capacities, when it comes to the properties of a targeted system? How do models specific to simulation relate to theory or experimentation in detail? How do they relate to each another? Frankly, can philosophers retain certain preconceptions about the modeling steps involved in CSs in the simulation-intensive experimental environment that is the Large Hadron Collider (LHC)?
Building on LHC’s ATLAS experiment as a case study, “one of the largest collaborative efforts ever attempted in science”,6 we will approach this complex of problems from the following angle: Following Winsberg (1999) and Karaca (2018a, b), we will investigate the concept of a hierarchy of models in the context of simulation
Summary
A first reason to be skeptical comes from Karaca (2018a, b). Recall that the seminal account of hierarchies of models is Suppes (1962), adopted and developed, e.g., by Mayo (1996) and Harris (1999), and concerned with the connection of a specific model of some theory to experimental data. Karaca (2018a), claims that models of data acquisition, required to understand LHC experiments, fit nowhere into this hierarchical picture: On the one hand, data acquisition seems to be just less specific to the particular experimental context than ceteris paribus conditions and experimental design Regarding specialization, it might be placed right below data models. The target phenomena are the particles produced in LHC’s proton-proton scatterings; the instrumentation, solely concerns the detector and associated electronics He outlines how all these simulation models relate in various ways to different theories and experimental data, leading him to propose a “‘network of models account’ [...] of scientific experimentation, which applies to experiments that involve theoretical, experimental and simulation models.” He outlines how all these simulation models relate in various ways to different theories and experimental data, leading him to propose a “‘network of models account’ [...] of scientific experimentation, which applies to experiments that involve theoretical, experimental and simulation models.” (Karaca 2018b, p. 18)
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