Description
Dark matter detection experiments face persistent challenges in interpretation and cross-comparison of results. This paper presents an in‐depth investigation into the multifaceted use of models within dark matter detection, proposing a comprehensive taxonomy that distinguishes among background theory, theoretical models, phenomenological models, experimental models, and data models. It will be argued that, while the background theory establishes abstract causal relations and mathematical constraints, it does not directly yield testable results. Rather, it provides a necessary backdrop against which more specific models are developed, and these subsequent models must be constructed with autonomous, independently sourced constraints in order to avoid circular reasoning in the interpretation of experimental data.
The paper categorizes the models into five distinct types. Background theory offers the general structural and relational conditions, but without an explicit object domain. The theoretical model, by contrast, incorporates a domain of objects that introduces additional constraints and specifies interaction properties. This is followed by the phenomenological model, which translates abstract relations into a causal narrative by incorporating the specific details of the particles that are under investigation. The experimental model then adapts these phenomenological constraints to the specifics of detector design and experimental setup in order to communicate between the experimental data and theoretical framework. Finally, data models are developed from the experimental outcomes with the experimental model in mind, serving to either compare observed results with the predictions, or can be used as constraints on the experimental model that will be contrasted against the phenomenological one.
A central argument of the paper is that experimental models must be methodologically independent from their theoretical and phenomenological counterparts. In dark matter detection, this independence is crucial because the detectors are tasked not only with discovering whether dark matter exists, but also with finding out the particle's mass. The dual role of the detector complicates interpretation; since similar interaction signals can be produced by particles with different masses and velocities, the experimental model is burdened with disentangling these overlapping parameter spaces. Moreover, because different experiments employ distinct target materials and methods, direct model-independent comparisons across experimental results remain elusive.
In response to these challenges, the paper also examines several alternative approaches that have been proposed in the literature. Competing models offer divergent causal explanations for the astrophysical phenomena that originally motivated the dark matter hypothesis and can take into account the inconsistent and difficult to interpret results that the detectors have captured. Additionally, model-independent methodologies have been advanced to mitigate the reliance on uncertain phenomenological inputs, though these too face limitations due to residual experimental uncertainties.
The paper will conclude that the persistent lack of conclusive dark matter detection, despite substantial indirect evidence from cosmology, underscores the necessity of critically re-examining the hierarchical and autonomous nature of model construction in experimental practice. By clearly delineating the roles and dependencies of various model types, the paper provides new insights into why inconsistencies persist in the field.
