Representative Signatures in Data Analysis - Epistemological Reflections

Contribution List

14. A generalized concept of signatures?

Prof. Michael Stöltzner (Department of Philosophy, University of South Carolina)

In a recent paper, Peter Mättig and I developed a generalized version of the concept of signatures broadly used in elementary particle physics and discussed its relation to phenomena and data models. My paper explores, for one, how this concept can be extended to other fields and where such an extension is most likely to fail. Secondly, I discuss to what extent signatures can be a target for...

10. Does machine-learning-driven scientific discovery pose new challenges to Putnam’s No Miracles Argument?

Johannes Mierau (TU Dortmund)

6. Models, Machine Learning, and Representation in Astroparticle Physics

Prof. Brigitte Falkenburg (TU Dortmund)

Astroparticle physics measures cosmic rays using particle detector systems that function as telescopes for detecting subatomic particles of extraterrestrial origin. As part of current multi-messenger astronomy, its models and methods bridge the gap between astrophysics, cosmology, and particle physics. My talk focuses on the causal models and probabilistic AI methods used to analyse the data...

4. Scientific Models as Representations: From Astroparticle Physics to Physics Education

Dr Marlene Doert (Department of Physics, TU Dortmund)

Modern physics is deeply shaped by the development and use of models. From detector simulations and statistical inference to the interpretation of experimental data, scientific knowledge is rarely derived from observations alone but emerges through layers of modeling that represent physical processes, instruments, and uncertainties.

At the same time, the role of models as representations of...

11. Signatures of electrons at ATLAS: The entheorization of data

Prof. Kent W. Staley (Department of Philosophy, Saint Louis University)

The study of particle signatures at High Energy Colliders provides a concrete context to think about pattern identification in experimental data. An inquiry into pattern identification in relation to particle signatures stands to yield new insights on long-standing philosophical questions about patterns (highlighted in an influential paper by Dennett), such as their relationship to phenomena...

9. Starved for Evidence: Data Scarcity, Empiricism, and Realism

Dr Mahdi Khalili (Institute of Philosophy, University of Bern)

Data scarcity is a fundamental challenge in astrophysics and astroparticle physics, especially in an era increasingly shaped by data-hungry deep learning models. Many cosmic phenomena, including fast transients, high-redshift galaxies, and multi-messenger events such as binary neutron star mergers and black hole–neutron star collisions, occur so rarely that available observational datasets are...

2. tba

Mirko Bunse (Lamarr Institute, TU Dortmund University)

5. tba

Dominik Elsässer

13. tba

Prof. Wolfgang Rhode (TU Dortmund)

8. The Explanation–Justification Gap in Deep Learning Astroparticle Physics

Dr Koray Karaca (Department of Philosophy, University of Twente)

Deep learning (DL) models are increasingly used in astroparticle physics for tasks such as gamma–hadron separation, neutrino event reconstruction, and cosmic-ray classification. While these models achieve remarkable predictive accuracy, their opacity poses a challenge to the epistemic standards of discovery. Heatmap-based explainable AI (XAI) techniques —such as heatmaps — promise insight into...

7. The impact of data quality and representativity for machine learning models

Katja Ickstadt

12. Tracing Particles: “Scanning Girls” and the Automation of Image Evalu- ation at the German Electron Synchrotron (DESY)

Dr Dinah Pfau (Institute for the History of Science and Technology, Deutsches Museum Munich)

In the course of the “industrialization” (Galison 1997) of high-energy physics during the 1950s and beyond, the evaluation of vast quantities of experimentally produced photographs was initially delegated to untrained women. However, their “practices of seeing” (Schürmann 2008) were soon identified as the economic and epistemic bottleneck of the experimental process — a barrier that was to be...

1. Unfolding data with AI at the LHC: Epistemological implications

Prof. Pierre-Hugues Beauchemin (Department of Physics and Astronomy, Tufts University, Department of Philosophy)

Unfolding is an inverse data transformation process that aims to correct high-level physics observable quantities obtained from a set of data for possible distortions introduced by the instrument. Unfolding is a crucial step to enable experimental outcomes to be directly comparable to theoretical predictions. Traditional unfolding methods used in experimental High Energy Physics consist in...

3. What can we learn from hallucination? Scientific realism and models in astrophysics in the age of the AI

Dr Silvia De Bianchi (Department of Philosophy, Università degli Studi di Milano)

In recent work, Constantin et al. (2026) suggested the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. Thanks to structural insights, they believe that one can inform the training or fine-tuning of language models for symbolic reasoning, encouraging them to generate expressions whose statistical and...