PROJECT B1
The impact of computer simulations and machine learning
on the epistemic status of LHC Data
The impact of computer simulations and machine learning on the epistemic status of LHC Data
Computer simulations (CSs) and machine learning (ML) are important tools for experimental data generation and analysis in contemporary high-energy physics (HEP). In this project, we address epistemic issues related to this use of CS and ML in HEP. We focus on the use of both in the ATLAS experiment, which has been operating at CERN’s LHC since 2008. We base our research on a rich background in the philosophy of experiment (e.g. Schiemann, 2008) as well as detailed knowledge of the relevant computational methods (e.g. Zeitnitz & Gabriel, 1994)
The project’s future objectives are: To address the possibility of concealed uncertainties induced by the use of CSs and ML in HEP; to define the precise sense of robustness that CSs and ML enjoy in HEP; and to understand epistemological challenges arising from CSs’ and ML’s purported opacity, and their management in HEP.
An important concept for our research is that of an epistemic risk (e.g. Hillerbrand, 2012a; 2014), broadly construed. We will use this concept to investigate the impact of CSs and ML on the discovery potential of the experiment. Among other things, this will include an assessment of the scope and epistemological underpinnings of the management of uncertainties in HEP, when these are induced by the use of CSs and ML.
Our past research has led to a detailed picture of the intricate relations pertaining between different simulation models used by ATLAS, in turn raising challenges for traditional tenets in the epistemology of simulation (unpublished). Other results include a classification of the kinds of CSs relevant for HEP (Hillerbrand, 2012b), detailed studies on the relation between theory, experiment, and simulation (Boge, 2019, forthcoming), the (non-)necessity of CSs for HEP experiments (Krämer, Schiemann & Zeitnitz, in prep.), as well as first results on questions of opacity (Boge & Grünke, forthcoming).
Principal Investigators:
Rafaela Hillerbrand
Gregor Schiemann
Christian Zeitnitz
Previous Member
Michael Krämer (former PI)
Paul Grünke (former doctoral researcher)
Florian Boge (former post-doctoral researcher)
Marianne van Panhuys (former doctoral researcher)
Cooperation Partners:
Johannes Grebe-Ellis (BUW)
Oliver Passon (BUW)
Workshop: Workshop Machine Learning
Publications
Boge, F. J. (2018). "Quantum Mechanics Between Ontology and Epistemology". In: European Studies in Philosophy of Science. Vol. 10.
https://www.springer.com/gp/book/9783319957647
Boge, F. J. (2019). How to infer explanations from computer simulations. Studies in History and Philosophy of Science. doi.org/10.1016/j.shpsa.2019.12.003
Boge, F. J. (2019). "The Best of Many Worlds, or, is Quantum Decoherence the Manifestation of a Disposition?". In: Studies in History and Philosophy of Modern Physics. Vol. 66. https://doi.org/10.1016/j.shpsb.2019.02.001
Boge, F. J. (2019). "Quantum Information vs. Epistemic Logic: An Analysis of the Frauchiger-Renner Theorem". In: Foundations of Physics. Vol. 49. https://doi.org/10.1007/s10701-019-00298-4
Boge, F. J. (2019). Why computer simulations are not inferences, and in what sense they are experiments. European Journal for Philosophy of Science Vol. 9, no. 13 (30 pp.). doi.org/10.1007/s13194-018-0239-z
Boge, F. J. (2020). "An Argument Against Global No Miracles Arguments". In: Synthese. Vol. 197. https://doi.org/10.1007/s11229-018-01925-9
Boge, F. J. and Zeitnitz, C. (2020). Polycratic hierarchies and networks: What simulation-modeling at the LHC can teach us about the epistemology of simulation. Synthese. https://doi.org/10.1007/s11229-020-02667-3
Boge, F. J. and Glick, D. (2021). "Is the Reality Criterion Analytic?". In: Erkenntnis. Vol. 86. https://doi.org/10.1007/s10670-019-00163-w
Boge, F. J. (2021). Why trust a simulation? Models, parameters, and robustness in simulation-infected experiments. The British Journal for the Philosophy of Science. https://doi.org/10.1086/716542
F. Boge, M. Krämer, C. Zeitnitz, Deep Learning for Scientific Discovery and the Theory-Freedom-Robustness Trade-Off (submitted)
Boge, F.J. and Poznic, M. (2021) Machine Learning and the Future of Scientific Explanation. J Gen Philos Sci 52, 171–176. https://doi.org/10.1007/s10838-020-09537-z
Boge, F. J. (2022). Two Dimensions of Opacity and the Deep Learning Predicament. Minds and Machines. https://doi.org/10.1007/s11023-021-09569-4
Boge, F. J. & Grünke, P. (forthcoming). Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics. In Andreas Kaminski, Michael Resch & Petra Gehring (eds.), The Science and Art of Simulation II.
Boge, F. J.; Grünke, P.; Hillerbrand, R. (2022) Machine Learning: Prediction Without Explanation? Minds and Machines, 32 (1), 1–9. https://doi:10.1007/s11023-022-09597-8
Boge, F.J. (2023) Functional Concept Proxies and the Actually Smart Hans Problem: What’s Special About Deep Neural Networks in Science, Synthese, https://doi:10.1007/s11229-023-04440-8
Boge, F. J..; Carretero-Sahuquillo, M.-Á.; Grünke, P.; King, M.(2023) Introduction: Simplicity out of complexity? Physics and the aims of science. Synthese, 201 (4), Art.-Nr.: 116. https://doi:10.1007/s11229-023-04126-1
Eckert, C. and Hillerbrand, R. (2022) Models in Engineering Design as Decision-Making Aids. Engineering Studies, 14 (2), 134–157. doi:10.1080/19378629.2022.2129061
Grünke, P. D. (2019) Chess, Artificial Intelligence, and Epistemic Opacity. Információs társadalom, 19 (4), 7 – 17. doi:10.22503/inftars.XIX.2019.4.1
Grünke, P. D. (2023) Computer-based methods of knowledge generation in science - What can the computer tell us about the world?. Dissertation. Karlsruher Institut für Technologie (KIT). https://publikationen.bibliothek.kit.edu/1000155687
Harlander, R., Martinez, J.-P. and Schiemann, G. (2023) The end of the particle era? In: The European Physical Journal H , 48/6.
Hillerbrand, R. (2012). The risk of climate change. In Roeser, S., Hillerbrand, R., Sandin, P., and Peterson, M. (eds.), Handbook of Risk Theory. Springer.
Hillerbrand, R. (2012) Order out of chaos? A case study in high energy physics. Studia Philosophica Estonica, 5(2):61–78. doi:10.12697/spe.2012.5.2.05
Hillerbrand, R. (2014). Climate simulations: Uncertain projections for an uncertain world. Journal for General Philosophy of Science, 54(1):17–32. doi:10.1007/s10838-014-9266-4
Krämer M.; Schiemann G.; Zeitnitz C. (2024). Experimental high-energy physics without computer simulations. Studies in History and Philosophy of Science; volume 106, 37-42, ISSN 0039-3681, https://doi.org/10.1016/j.shpsa.2024.05.001.
Van Panhuys M., Rafaela Hillerbrand. Ahead of Evidence: Computer Simulation and Epistemic Risks in Particle Physics. Perspectives on Science 1–23. doi.org/10.1162/posc_a_00634
Poznic M. and Hillerbrand R. (2021) Scenarios as Tools of the Scientific Imagination: The Case of Climate Projections. Perspectives on Science ; 29 (1): 36–61. https://doi.org/10.1162/posc_a_00360
Schiemann G. (2017) Philosophie der Teilchenphysik. BUW Output, Nr. 17 , S. 12-17. Bergische Universität Wuppertal.
Schweer, J.; Grünke, P.; Hillerbrand, R. (2021) Beyond Opacity - Epistemic Risks in Machine Learning. AISB 2021 Symposium Proceedings: Overcoming Opacity in Machine Learning. Ed.: C. Zednik, 5–6