Data, Theories, and Scientific Explanation: The Case of Astroparticle Physics

• Applicants: Professorin Dr. Brigitte Falkenburg; Professor Dr. Wolfgang Rhode
• Subject Area: Theoretical Philosophy
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 548023664

In an interdisciplinary co-operation between philosophy and astroparticle physics (APP), we investigate the methods and explanatory power of APP as an example of current physical practice. Focusing on the methods of computer simulation and machine learning that are used for data analysis, we address the epistemological questions of the theory-ladenness of data as well as the probabilistic and causal-mechanistic explanations of APP. APP bridges the gaps between astrophysics, cosmology, and particle physics. It registers cosmic rays using systems of particle detectors function as telescopes to measure subatomic particles of extraterrestrial origin. The aim of APP is to determine the origin of this radiation and to explain its physical properties, in particular its high energy and the mechanisms of its creation and acceleration. The physical explanation is based on astrophysical models of the radiation sources (supernovae, active galactic nuclei, etc.), nuclear physics models of particle emission, and plasma physics to explain the acceleration of the emitted charged particles. The primary particles give rise to secondary particles such as neutrinos, which propagate to the earth without being scattered by interstellar gas or deflected by intergalactic magnetic fields; their production and measurement issubject to particle physics. APP thus mediates between the data and several physical theories underlying the disparate standard models of particle physics and cosmology. APP thus brings together many of the problems that characterise science in general today and are currently discussed in the philosophy of science: the influence of computer simulations and machine learning on data production; the opacity of machine learning and the epistemic significance of this opacity; the often-incoherent foundations of modelling; probabilistic and causal-mechanistic explanations; the inductive inference from phenomena to their causes. In this project, we analyse the data analysis of APP, including the role of computer simulations and machine learning, with respect to its probabilistic character and the epistemic significance of the opacity of these methods, and we compare them with the corresponding methods of particle physics and radio astronomy. We focus on the innovative probabilistic character of the machine learning methods used for data analysis. In addition, we analyse the heuristic concept of messenger particles, which is crucial for the causal-mechanistic explanations of APP, and discuss it in the context of the latest measurement results of APP. The project will also shed new light on the philosophical question of the extent to which physical theories and theory-dependent data can be interpreted in realistic terms.

• DFG Programme: Research Grants